This letter investigates the impact of spatially oversampling a uniform linear array (ULA) on its identifiability. Under perfect statistical and noise-free conditions, the rank of the array covariance matrix is identical to that of the array manifold. We analyze the Wronskian of the steering vector components in the oversampled array manifold and demonstrate that the array manifold maintains full theoretical rank, but the Wronskian tends to vanish as the interelement spacing decreases. To evaluate the practical degrees-of-freedom (DOFs) of the oversampled array, we further examine the numerical rank of the manifold matrix by analyzing an upper bound of the minimum singular value and its dependence on the number of sensors and the oversampling factor. This analysis reveals the conditions under which the minimum singular value falls below a given threshold, causing a loss of numerical rank and correspondingly its DOFs in the oversampled array. Our results indicate that, as the number of sensors in the ULA increases within a fixed aperture, the incremental gain in numerical rank from oversampling progressively diminishes.

This letter considers two-dimensional direction of arrival (DOA) estimation of coherent signals exploiting a moving uniform rectangular array. The motion of the array induces phase variations in the received signals across spatial positions, enabling the construction of decorrelated covariance matrices through forward-backward spatial smoothing. We analyze the achievable degrees of freedom (DOFs) in terms of movement steps and examine the impact of the motion support on effective decorrelation. Notably, we show that the maximum number of DOFs can be achieved if each movement step is at least half the signal wavelength and the number of movement steps is no less than half the number of array elements. Furthermore, it is demonstrated that distributing motion across both array axes yields better decorrelation and estimation performance than restricting movement to a single dimension.

Two-channel modulo sampling based on the robust Chinese remainder theorem (RCRT) enables closed-form, point wise reconstruction, yet its recovery remains fundamentally limited by the effective RCRT threshold. This letter addresses this limitation through a hybrid architecture that integrates RCRT based recovery with difference-based unwrapping. The RCRT stage is reinterpreted as a coarse modulo operator with an expanded threshold, while the difference stage resolves the residual ambiguity by exploiting temporal structure. The proposed scheme extends recovery beyond the intrinsic RCRT range and simultaneously relaxes the oversampling requirement for a fixed difference order. Theoretical analysis establishes the resulting sampling condition, and the effectiveness of the proposed method is validated through both simulations and hardware experiments.

Hybrid analog-digital beamforming is an effective approach for practical implementations of a massive multiple-input multiple-output (MIMO) system by reducing the number of radio frequency (RF) chains. Fully connected hybrid beam-forming (F-HBF), where each RF chain is connected to each antenna, can lower hardware complexity, power consumption, and cost compared to digital beamforming. Subarray-based hybrid beamforming (S-HBF), where a specific group of RF chains is allocated to a particular subarray, can further reduce hardware requirements. The antenna array is divided into subarrays using effective partitioning so that the optimization of analog beamforming can be shared across multiple subarrays, substantially reducing computational complexity.

This paper presents a comprehensive analysis of two-channel modulo analog-to-digital converters (ADCs) systems, focusing on the sensitivity of ADC thresholds. By exploiting analytic number theory, we first investigate the relationship among ADC threshold precision, maximum signal dynamic range, and error tolerance. Our analysis reveals that even slight deviations in ADC thresholds can substantially impact the maximum reconstructed signal dynamic range and error tolerance. To address these sensitivity issues, we propose a novel approach that strategically sacrifices signal dynamic range to stabilise error tolerance in the presence of slight ADC threshold variations. We also introduce a low-complexity reconstruction algorithm that exploits this trade-off, thereby enhancing system robustness. Simulation results validate the theoretical framework and confirm the efficiency of our proposed algorithm.

Single-snapshot signal processing in sparse linear arrays has become increasingly vital, particularly in dynamic environments like automotive radar systems, where only limited snapshots are available. These arrays are often utilized either to cut manufacturing costs or result from unintended antenna failures, leading to challenges such as high sidelobe levels and compromised accuracy in direction-of-arrival (DOA) estimation. Despite deep learning’s success in tasks such as DOA estimation, the need for extensive training data to increase target numbers or improve angular resolution poses significant challenges. In response, this paper presents a novel Siamese neural network (SNN) featuring a sparse augmentation layer, which enhances signal feature embedding and DOA estimation accuracy in sparse arrays. We demonstrate the enhanced DOA estimation performance of our approach through detailed feature analysis and performance evaluation. The code for this study is available at https://github.com/ruxinzh/SNNS_SLA.

Single-satellite geolocation achieves effective localization of ground electromagnetic interference (EMI) signals with a low cost compared to the multi-satellite counterparts. In such systems, the Doppler and Doppler rate are commonly exploited to extract the information of the ground EMI sources and the constrained Unscented Kalman filter (cUKF) is found effective to provide instantaneous EMI locations over time. In this letter, we address the benefit of exploiting Doppler acceleration in the underlying single-satellite geolocation problem, and point out that exploiting additional constraint on the altitude of the ground emitter provides enhanced EMI tracking performance. The importance of such constraint is more pronounced in the beginning of the tracking process, whereas removing such constraint after a short period of time does not compromise the performance. The effect of sampling rates on performance and the required time to converge are investigated.

In this paper, we analyze the effect of sensor placement to the achievable number of degrees-of-freedom (DOFs) when the sensors deviate from a half-wavelength grid. More specifically, we consider two variations of a uniform linear array (ULA), namely, when one or more sensors are shifted from half-wavelength grid positions and when the inter-element spacing of the ULA is smaller than a half-wavelength. The numerical rank and the rank-revealing QR factorization of the array data covariance matrix are examined and the number of DOFs of the array is studied in terms of the rank of the array data covariance matrix. A threshold based on the rank-revealing QR factorization is proposed to separate the eigenvalues respectively corresponding to the signal and noise subspaces, and thus the numerical rank of the array data covariance matrix is estimated. Simulation results are provided to justify the findings and provide insights on sensor placements to preserve the array DOFs.

When the line-of-sight between the base station and mobile users is unavailable, reconfigurable intelligent surfaces (RIS) can be exploited to ensure connectivity and improve data transmission performance. The objective of this paper is to estimate and predict timevarying user-RIS channels with low pilot overhead using a small number of sparsely distributed active RIS elements. Structured covariance matrix interpolation is performed to fully utilize the array aperture from the sparse semi-passive RIS. Channel variation over time due to user movement and environmental factors can make it difficult to utilize all time slots for channel estimation and data transmission. To address this challenge, we propose a model based on long short-term memory (LSTM) networks for channel estimation and prediction to reduce the required training pilot signals and increase the transmission data rate using parallel computation. Simulation results verify the capability of the proposed approach to enhance data transmission in wireless networks and demonstrate its effectiveness compared to other machine learning models.

This paper addresses the direction-of-arrival (DOA) estimation problem of mixed coherent and uncorrelated signals using a sparse rectangular array, where tensor reconstruction is employed to preserve the structure of multi-dimensional array signals. In the proposed approach, we first estimate the DOAs of uncorrelated signals using the subspace algorithm. After eliminating the contribution of uncorrelated signals from the covariance tensor, a structural tensor decorrelation process is introduced to decorrelate the resulting coherent covariance tensor. The canonical polyadic decomposition method is employed to the decorrelated covariance tensor to detect the coherent signals. The conditions of signal resolvability are analyzed.

This letter develops a new direction-of-arrival (DOA) estimation method for mixed coherent and uncorrelated signals through the reconstruction of a set of Toeplitz matrices. More specifically, Toeplitz matrices are formed by utilizing the rows and columns of the covariance matrix, and their average is used by subspace-based algorithms to effectively estimate the signal DOAs. Compared to existing methods, the proposed approach provides a high number of degrees of freedom and requires a low computation complexity.

This paper considers a wireless network assisted by an intelligent reflecting surface (IRS) to enhance data transmission between the base station and mobile users. Our objective is to estimate and predict the user-IRS channels by exploiting a small number of sparsely distributed active elements with a low pilot overhead. The Hermitian and Toeplitz properties of the data covariance matrices are used to perform covariance matrix interpolation for enhanced estimation of the time-varying user-IRS multipath channels, and a machine learning-based channel predictor is developed to predict the channels based on prior channel estimates so as to shorten the required training pilot signals and enhance the transmission data rate. Simulation results verify the effectiveness of the proposed method for accurate channel estimation and prediction.

In this paper, we develop a deep learning framework to optimize the compressive sampling matrix in a massive multiple-input multiple-output (MIMO) system. The optimized compressive sampling matrix is utilized to project high-dimensional data received at the massive MIMO system into a lower-dimensional space so that the directions of arrival and other signal parameters can be efficiently obtained with a reduced hardware complexity. The proposed deep learning approach for optimizing the compressive measurement matrix increases its robustness and generalizability.

We consider an integrated sensing-communication system operating in a dynamic environment, such as an autonomous vehicle scenario. We propose a novel, low-cost, low power consumption and low-computation approach for designing a beam that can simultaneously reach the radar target of interest and the desired communication destination. The transmitter is a uniform linear array, equipped with quantized double phase shifters, which enables a flexible beam design while using analog only processing. Only a small number of antennas are selected to transmit in each channel use, in order to save system power and reduce antenna coupling. We propose a deep reinforcement learning approach to adaptively adjust the double phase shifters and select the active antennas in order to optimize the transmit beamforming, through a transmission and feedback trail. The actor-critic network strategy together with the Wolpertinger policy is adopted to obtain the optimal solutions efficiently and effectively. Numerical results demonstrate the feasibility of the proposed method.

In this paper, we analyze the Cramer-Rao bound of the distributed direction-of-arrival (DOA) estimation problem where the covariance matrix is formulated in a mixed-precision manner. In this scheme, the self-covariance matrix of a subarray is locally computed using the full-precision data received at the subarray, whereas one-bit data are exploited at the fusion center to compute the cross-covariance matrices between different subarrays. As such, the resulting covariance matrix of the distributed array consists of full-precision subarray self-covariance matrices and low-precision cross-covariance matrices between subarrays, thus termed as a mixed-precision covariance matrix. Such distributed DOA estimation scheme offers substantial reduction of the network communication overhead while maintaining the degrees of freedom offered by the distributed array. We provide the Cramer-Rao bound analysis which enables us to understand the importance of the self- and cross-covariance matrices and optimize the array parameters. The CRB analysis results are compared with the root mean-square error performance of the estimated signal DOAs.

Distributed array consisting of multiple subarrays is attractive for high-resolution direction-of-arrival (DOA) estimation when a large-scale array is infeasible. To achieve effective distributed DOA estimation, it is required to transmit information observed at the subarrays to the fusion center, where DOA estimation is performed. For noncoherent data fusion, the covariance matrices are used for subarray fusion. To address the complexity involved with the large array size, we propose a compression framework consisting of multiple parallel encoders and a classifier. The parallel encoders at the distributed subarrays are trained to compress the respective covariance matrices. The compressed results are sent to the fusion center where the signal DOAs are estimated using a classifier based on the compressed covariance matrices.

We propose a novel automotive radar imaging technique to provide high-resolution information in four dimensions, i.e., range, Doppler, azimuth, and elevation, by exploiting a joint sparsity design in frequency spectrum and array configurations. Random sparse step-frequency waveform is proposed to synthesize a large effective bandwidth and achieve high range resolution profiles. This concept is extended to multi-input multi-output (MIMO) radar by applying phase codes along the slow time to synthesize a two-dimensional (2D) sparse array with a high number of virtual array elements which enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA), and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. The proposed imaging radar provides point clouds with a resolution comparable to light detection and ranging (LiDAR) but with a much lower cost and is insensitive to weather conditions.

Sensor array-based joint radar-communication (JRC) systems exploit adaptive beamforming to transmit radar and communication signals in their respective directions. Optimal sensor selection is anticipated as an attractive means to achieve superior performance with a low hardware cost because of the ever-decreasing cost of the sensor deployment compared to the radio frequency (RF) chains and processors. In this paper, we address optimal sensor selection for adaptive beamforming-based JRC systems by exploiting a constrained re-weighted ℓ1-norm minimization with low computational complexity. We argue that, compared to the individual approaches, the grouped counterpart eases the hardware implementation by mollifying the unnecessary sensor switching and enables effective utilization of up-conversion chains. Simulation results clearly demonstrate the superior performance of the proposed strategies.

In this paper, we propose a new sparse coprime array design that achieves a higher number of degrees-of-freedom for direction-of-arrival (DOA) estimation. The proposed array design completely avoids lag redundancies between the two constituting subarrays of the coprime array, thus achieving the maximum number of unique correlation lags under the coprime array framework. As a result, given the same number of physical sensors, the proposed design resolves more sources than other coprime array designs with enhanced DOA estimation performance. Simulation results demonstrate the superior performance of the proposed coprime array design.

We propose a distributed unmanned aerial vehicle (UAV) network performing collaborative radar sensing for multi-target localization and motion parameter estimation. Two UAV network topologies are considered for data propagation and information fusion. In the former, we form a sequential UAV node chain, whereas in the latter, the UAV nodes are grouped into clusters and the information among different clusters is propagated through the cluster master nodes. Sparse reconstruction methods are used to fuse the target state information from previous nodes or clusters with the data measured in the underlying nodes or clusters, depending on the adopted topology, to achieve improved target state estimates. In order to minimize the communication traffic in the UAV network, each node transmits the estimated Doppler signatures or sparse target state estimates to the next UAV node in the network or the cluster master node, in lieu of the large volume of raw sampled data. Simulation results verify the effectiveness of the proposed approaches and compare the performance between the two UAV network topologies.

Multi-task compressive sensing is a framework that, by leveraging the useful information contained in multiple tasks, significantly reduces the number of measurements required for sparse signal recovery and achieves improved sparse reconstruction performance of all tasks. In this paper, a novel multi-task adaptive matching pursuit (MT-AMP) algorithm based on a hierarchical Bayesian model is proposed with the exploitation of both the group structure across different tasks and the intra-group correlation, yielding an effective means to simultaneously perform sparse recovery as well as learn the statistical inter-task and intra-group relationships. Experimental results using both synthetic data and real data sets demonstrate the superiorities of the proposed method over existing state-of-the-art algorithms.

Moving platforms enable sparse arrays to assume higher degrees of freedom and lead to increased number of lags. In essence, array motion can fill the holes in the spatial auto-correlation lags associated with a fixed platform and, therefore, increase the number of sources detectable by the same physical array. In this paper, we consider coprime arrays, and assume quasi-stationarity of the environment, where the source locations and waveforms are assumed invariant over array motion of half wavelength. Expressions of the synthetic array comprising the original coprime array and its shifted version are derived. Analysis of the difference co-array corresponding to the combined array positions before and after motion is provided. It is shown that majority, if not all, of the holes in the original array position can be filled by just a small translation shift along the coprime array axis.

A novel dilated nested array is presented to obtain an enhanced fully filled difference coarray exploiting array motions. The proposed sparse array is suitable for the cases when the sensing environment can be assumed stationary over an array motion of half wavelength or shorter. Closed-form expressions of the number of degrees of freedom in the difference coarray of the combined array before and after the translation motion are presented for direction-of-arrival (DOA) estimation. It is shown that the maximum number of consecutive lags for this case is three times that of the corresponding conventional two-level nested array. Numerical results of DOA estimation using the proposed array are provided for performance comparison and to validate array analyses.

In this letter, we consider the sparsity-based time-frequency representation (TFR) of frequency-modulated (FM) signals in the presence of burst missing samples. In the proposed method, three key procedures are used to mitigate the effect of missing samples. First, each slice in the instantaneous autocorrelation function (IAF) corresponding to the time or lag domain is converted to a Hankel matrix, and whose missing entries are recovered via the atomic norm-based approach. Second, a signal-adaptive time-frequency kernel is used to mitigate the undesired cross terms and the residual artifacts due to missing samples. Third, we apply a rank deduction technique on the obtained IAF to provide reliable TFR reconstruction results.

Massive multiple-input multiple-output (MIMO) is a promising technique for 5G communications due to its superior spectrum and energy efficiencies. Despite its many advantages, the high number of antennas used in massive MIMO brings many challenges in practical implementations. Among them, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) massive MIMO systems. In this paper, we exploit the available a priori knowledge of the channel to optimize the pilot design. By utilizing the low-rank nature of the channel matrix, we first derive the minimum number of pilot symbols required for perfect channel recovery. Further, under the general Gaussian mixture model for the channel vector, the pilot symbols are optimized to maximize the mutual information between the measurements of the user and the corresponding channel vector. Simulation results demonstrate the effectiveness of the proposed optimal pilot design for the downlink channel estimation in FDD massive MIMO systems.

Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

A novel robust adaptive beamforming algorithm based on a coprime array is proposed in this study. First, the authors exploit the virtual array structure of the coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating a spatial spectrum over each angular sector of the signal. Then, by using a closed‐form expression of the projection‐based method, the steering vectors (SVs) of the signals are estimated from the intersection of the two subspaces. Moreover, according to the covariance fitting theory, the power associated with the desired signal, interference signals, and noise is obtained from the virtual sample covariance matrix. In addition, to reconstruct the interference‐plus‐noise covariance matrix (INCM), the maximum correlation principle is used to transfer the virtual SVs of the signals to real ones corresponding to a physical array. Finally, with the estimated SV of the desired signal and INCM, the proposed robust algorithm is devised. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm.

Sparse arrays can achieve a higher number of degrees of freedom (DOFs) compared with uniform linear array (ULA) counterparts. To further reduce the number of physical sensors while keeping a high number of DOFs, a direction of arrival (DOA) estimation algorithm by exploiting coprime frequencies base on a sparse ULA is recently proposed. However, the performance of such approach is not properly analyzed. In this letter, we analyze the Cramér-Rao bound (CRB) as the lower bound of the DOA estimation performance. The difference between the results presented in this letter and the recent CRB results on sparse arrays lies primarily in the additional phases occurred when utilizing different frequencies. It is shown in this letter that the phases affect the covariance matrix of the received data vector and, as a result, change the number of resolvable sources and alter the achieved CRB. We first demonstrate the effect of the additional phases with an example of two closely spaced sources, and the CRB for a sparse ULA exploiting two coprime frequencies is then derived. Numerical simulations are provided to validate the analyses.

In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization problem is formulated to recover all the rows and columns of the unknown correlation matrix entries corresponding to the interpolated sensors. The optimized Hermitian positive semidefinite Toeplitz matrix functions as the covariance matrix of the interpolated ULA, which enables to resolve off-grid sources. Simulation results demonstrate that the proposed array interpolation-based DOA estimation algorithm achieves improved performance as compared to existing coarray-based DOA estimation algorithms in terms of the number of achievable degrees-of-freedom and estimation accuracy.

Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an efficient and high-resolution EIT image reconstruction method in the framework of sparse Bayesian learning. Significant performance improvement is achieved by imposing structure-aware priors on the learning process to incorporate the prior knowledge that practical conductivity distribution maps exhibit clustered sparsity and intra-cluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results.

As a new emerging technology for wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end circuits in a dense circuit board. In this paper, we apply the compressive sensing technique to reduce the required number of front-end circuits and the overall computational complexity. Unlike the commonly adopted random projections, we utilize the a priori probability distribution of the directions-of-arrival (DOAs) of the signals to optimize compressive sensing kernels for massive MIMO systems, such that the mutual information between the compressed measurement and the DOA is maximized. With the optimized sensing matrix, we present a compressive sensing spatial spectrum estimator under the minimum variance distortionless response criterion. Simulation results demonstrate performance advantages of the proposed optimal sensing kernel over random sensing kernels.

In this paper, we consider robust direction-of-arrival (DOA) estimation for an array that contains mis-calibrated sensors with unknown gain and phase uncertainties. We develop two robust DOA estimation algorithms based on the maximum correntropy criterion (MCC). In the first algorithm, adaptively optimized weighting factors are obtained and applied to each sensor to effectively mitigate the effect of calibration error and array manifold distortions, and the results are fed into sparse reconstruction methods for DOA estimation. In the second algorithm, we further estimate the gain and phase errors of the mis-calibrated sensors so that the entire array is fully calibrated for improved DOA estimation. The effectiveness of the proposed techniques is verified using simulation results.

A coprime array has a larger array aperture as well as increased degrees‐of‐freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low‐computational complexity. In this study, the authors focus on the problem of efficient direction‐of‐arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower‐dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high‐resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

Group-sparsity-based method is applied to the 2qth-order difference coarray for underdetermined wideband direction of arrival (DOA) estimation. For complexity reduction, a focused compressive-sensing-based approach is proposed, without sacrificing its performance. Different from the conventional focusing approach, in the proposed one, focusing is applied to the virtual arrays and no preliminary DOA estimation is required. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms.

Fast and accurate detection of elderly falls can significantly reduce the rate of morbidity and mortality. In the past decade, extensive research has been performed to achieve real-time fall monitoring solutions. In this paper, we consider the radar-based modality and utilize the family of fractional Fourier transform to enhance the motion Doppler signature of falls. Compare with the conventional time-frequency analysis approaches, the proposed method achieves higher signal energy concentration and thus yields improved fall detection in low signal-to-noise ratio scenarios. Experimental results are used to validate the theoretical analysis and to demonstrate the feasibility of the proposed approach.

Increased demand on spectrum sensing over a broad frequency band requires a high sampling rate and thus leads to a prohibitive volume of data samples. In some applications, e.g., spectrum estimation, only the second-order statistics are required. In this case, we may use a reduced data sampling rate by exploiting a low-dimensional representation of the original high-dimensional signals. In particular, the covariance matrix can be reconstructed from compressed data by utilizing its specific structure, e.g., the Toeplitz property. In this paper, we propose a general coprime sampling concept that implements effective compression of Toeplitz covariance matrices. Given a fixed number of data samples, we examine different schemes on covariance matrix acquisition, based on segmented data sequences. The effectiveness of the proposed technique is verified using simulation results.

In this paper, we address the suppression of frequency modulated jammers in a multi-sensor Global Navigation Satellite System (GNSS) receiver. In particular, we consider the case of sparsely sampled signals and compressed observations. In this case, applying conventional time-frequency (TF) analysis for jammer characterization produces noise-like artifacts which, if not properly considered, would obscure the jammer TF representation and lead to considerable errors in jammer signal estimation and excision. In the proposed approach, a multi-sensor data-dependent TF kernel is applied for effective mitigation of artifacts due to missing samples. Sparse reconstruction methods are then applied to obtain nonparametric instantaneous frequency estimation. We apply the continuous-structure aware Bayesian compressive sensing method to exploit the contiguous nature of the jammer TF signature, leading to enhanced localization and suppression.

For radar echoes sampled at sub-Nyquist rates, it is impractical, if not impossible, to recover full-range Nyquist samples because of huge storage and computational loads. By exploiting the banded structure of the measurement matrix, we develop a novel segment-sliding reconstruction (SegSR) scheme to recover the Nyquist samples through low-cost segment-based computations. An important feature of the proposed SegSR scheme is that the measurement sub-matrix in each segment satisfies the restricted isometry property and thus the recovery performance is guaranteed. Because of the segmenting reconstruction, the adjacent segments will introduce interferences for current segment reconstruction. To reduce the effect of such interference, a two-step orthogonal matching pursuit process (TOMPP) algorithm is proposed for improved segment-based reconstructions. The effectiveness of the proposed SegSR with TOMPP is validated by simulations.

Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.

In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, the continuous distribution of the NLMS weight vector does not match the sparse characteristics of strong multipath components and target echoes, thus resulting in degraded cancelation performance. Motivated by this fact, a novel 2-D sparse-weight NLMS filtering scheme is proposed by extending the NLMS to a 2-D structure, in which the weight vector is sparsely distributed and adaptively adjusted based on the sparse strong multipath components and target echoes.

This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio-temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.

We use the phase difference among sensors, by solving the phase wrapping problem based on the cross-spectrum between sensors, to obtain the direction of arrival without exhaustive search in the two-dimensional angle space. The proposed approach does not need the typical sensor separation or interfrequency separation requirements. Simulation results with narrowband, wideband signals (bird chirps), and field experiments using a Voxnet system validate the proposed method and demonstrate a significant performance enhancement at a low computation burden.

A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization allow significant reduction of data to be transmitted to the fusion center without additional energy consumption requirement in the source coding/compression operation. Compared with existing compressive sensing-based DOA estimation methods, the superiority of the proposed approach in providing data volume reduction and performance improvement is verified by both simulations and field experiments using a prototype wireless sensor array network platform.

Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm.

For spatially distributed targets encountered in radar and sonar applications, direct application of subspace-based methods usually do not lead to an accurate estimation of the direction and angular extent of the signal arrivals. If the spatial distribution of the targets can be parameterized with a known model a priori, the direction-of-arrival (DOA) estimation problems can be simplified as parameter estimation problems. However, these methods do not apply when the targets are not parameterizable. Motivated by this fact, we propose an effective approach for the DOA estimation of nonparametric spatially extended targets. In the proposed approach, the spatially extended targets are modeled as a continuous sparse structure, which are effectively estimated using the Bayesian compressive sensing techniques based on a paired spike-and-slab prior accounting for the angular target spread. In particular, the problem is examined under a collocated multiple-input multiple-output (MIMO) radar platform. Signal transmission at multiple coprime transmit frequencies are also considered to achieve increased degrees-of-freedom. The group sparsity of the targets across different frequencies is exploited to achieve improved DOA estimation performance.

In this paper, we propose a novel structured compressive sensing algorithm based on non-parametric Bayesian framework for the reconstruction of sparse entries with a continuous structure. A paired spike-and-slab prior is first employed to impose signal sparsity. A logistic Gaussian kernel model, which involves the logistic model and location-dependent Gaussian kernel, is then proposed to encourage the underlying structure of a sparse signal. A closed-form and analytical posterior inference is carried out in a Gibbs sampling scheme. Simulation results demonstrate that the proposed algorithm outperforms existing state-of-the-art sparse Bayesian learning algorithms.

A multichannel terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR) system is capable of imaging a wider swath with a higher azimuth resolution for improved moving target detection. For TOPS SAR, due to antenna beam steering, the azimuth bandwidth of background clutter is much larger than the instantaneous signal bandwidth. To overcome this problem, a method referred to as spectrum compression space–time adaptive processing (SC-STAP) is proposed in this letter. Through the SC process, both the Doppler spectrum and the spatial spectrum of the background clutter are simultaneously compressed. This key step achieves fully overlapped clutter space–time spectrum lines and, as such, enables effective clutter suppression and target signal alias compensation by applying linearly constrained STAP. Furthermore, in order to avoid the target ambiguities arising from the spectral wrapping, an approach based on deramp processing is proposed to focus the moving targets for TOPS SAR mode. Simulation results validate the effectiveness of the proposed algorithm.

In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of clustered sparse entries based on hierarchical Bayesian framework. By extending a paired spike-and-slab prior to a general multi-task model, the proposed algorithm has the capability of modeling both inter-task and intra-task dependencies of the observation data. The latter is achieved by imposing a clustered prior on non-zero entries and finds applications in radar where targets exhibit spatial extent. Simulation results verify that the proposed algorithm outperforms state-of-the-art group sparse Bayesian learning algorithms.

A class of low-complexity compressive sensing-based direction-of-arrival (DOA) estimation methods for wideband co-prime arrays is proposed. It is based on a recently proposed narrowband estimation method, where a virtual array model is generated by directly vectorizing the covariance matrix and then using a sparse signal recovery method to obtain the estimation result. As there are a large number of redundant entries in both the auto-correlation and cross-correlation matrices of the two sub-arrays, they can be combined together to form a model with a significantly reduced dimension, thereby leading to a solution with much lower computational complexity without sacrificing performance. A further reduction in complexity is achieved by removing noise power estimation from the formulation. Then, the two proposed low-complexity methods are extended to the wideband realm utilizing a group sparsity based signal reconstruction method. A particular advantage of group sparsity is that it allows a much larger unit inter-element spacing than the standard co-prime array and therefore leads to further improved performance.

A new approach for sparse nonstationary signal reconstruction based on multiple windows is introduced. Signals which are localizable in the time-frequency (TF) domain give rise to sparsity in the same domain. When combined, sparse reconstructions, applied to randomly sampled data and corresponding to different selected windows, provide enhanced TF signature estimation. Among possible orthogonal windows, we consider those which characterize the eigen-decomposition of reduced-interference quadratic time-frequency distribution kernels. The highly overlapping TF support of the windows' full-data spectrograms inspires the use of the multiple measurement vectors, in lieu of individual windowed signal recovery. It is shown that the proposed approach outperforms other reconstruction methods when only a single window is applied and is superior to reduced interference time-frequency distributions of random observations.

The problem of estimating motion parameters of multiple closely located ground moving targets in a multistatic passive radar system is considered, with a focus on weak signal conditions. The proposed method provides a means of combining signal energy from all available, spatially separated, illuminators of opportunity to achieve multistatic diversity and overall signal enhancement. The proposed technique is based on sparse signal recovery and exploits a two-step process that sequentially estimates the acceleration and velocity vectors in order to reduce the dimensionality of parameter search space.

An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into independent real and imaginary components, does not take into account the group sparsity of these two components and thus yields poor recovery performance. In this paper, we first introduce the CMT-BCS algorithm that jointly treats the real and imaginary components, and then derive a fast and accurate algorithm for the estimation of the prior parameters by solving a surrogate convex function. The proposed CMT-BCS algorithm achieves effective complex sparse signal recovery and outperforms MT-CS and complex group Lasso.

In this paper, we propose effective coprime array configurations in which the minimum interelement spacing is much larger than the typical half-wavelength requirement. Such configurations are important in many applications where the half-wavelength requirement cannot be met due to the physical sensors size or to avoid spatial oversampling in wideband operations. The application of such coprime arrays in direction-of-arrival estimations is examined using different algorithms.

The linear range walk yields a significant range-azimuth coupling effect in a highly squinted synthetic aperture radar (SAR). Although the linear range walk correction (LRWC) technique can effectively mitigate such coupling effect, it causes azimuth variation in the resulting signal and, as such, the so-called “azimuth-shift invariance” property becomes invalid. In order to eliminate the azimuth variation, a new spectrum processing approach based on azimuth resampling is proposed in this paper. After performing the LRWC, the azimuth resampling is carried out in the 2-D frequency domain and transforms the signal spectrum to be equivalent to that of a broadside SAR. For squinted beamsteering SAR (BS-SAR), e.g., spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans SAR, the azimuth resampling is combined with the azimuth signal reconstruction algorithm. As a result, both the azimuth variation, which is induced by the LRWC, and the aliasing, which is caused by antenna beam steering, can be avoided. Therefore, after the azimuth resampling, the squinted SAR data can be focused by exploiting a conventional broadside SAR imaging algorithm. An analysis of the motion error for airborne SAR data processing is also provided. Simulation and real data results show the effectiveness of the proposed algorithm.

In this paper, we propose co-prime arrays for effective direction-of-arrival (DOA) estimation. To fully utilize the virtual aperture achieved in the difference co-array constructed from a co-prime array structure, sparsity-based spatial spectrum estimation technique is exploited. Compared to existing techniques, the proposed technique achieves better utilization of the co-array aperture and thus results in increased degrees-of-freedom as well as improved DOA estimation performance.

Over-the-horizon radar (OTHR) systems provide wide-area surveillance capabilities to detect and track targets far beyond the range of conventional line-of-sight radars. Because of the narrowband waveforms, OTHR systems do not achieve reliable altitude estimation. In this paper, we develop a new technique to track the instantaneous altitude of maneuvering targets by exploiting the estimated multi-component Doppler signatures. The main contribution of this paper is to apply effective non-stationary signal analysis for estimating the time-varying Doppler signature of each individual multipath, which is then applied to an extended Kalman filter to reliably track the instantaneous target altitude.

Although partial relay selection (PRS) for amplify-and-forward relay systems requires only the knowledge of source-relay (S-R) channels, in general, it incurs a significant performance loss. In cooperative systems with all single-antenna nodes, irrespective of the number of relays, the diversity order of PRS is limited to only one. This paper proposes a novel relay selection method for improving the performance of PRS scheme. In particular, as in the conventional PRS scheme, the relay that gives the best first-hop signal-to-noise ratio (SNR) is selected. However, this selection is made from only a subset of relays, for which the corresponding S-R and relay-destination (R-D) links are not in outage. An R-D link is considered to be in outage if its SNR is below the predefined threshold value of the end-to-end SNR plus some adjustable margin. The additional overhead required for implementing the proposed scheme is comparable to that of the conventional PRS method. For conciseness and better exposition of the proposed method, we limit our theoretical analysis to a system with two to three relays. The exact expressions of the end-to-end outage probability are derived and it is shown that full diversity order is achieved. Simulation results verify theoretical analysis and show that the proposed method significantly outperforms the conventional PRS method. Moreover, the results demonstrate that, for properly selected margin, the performance of the proposed method is very close or comparable to the method with full channel state information.

This paper investigates performance limits of a two-hop multi-antenna amplify-and-forward (AF) relay system in the presence of a multi-antenna energy harvesting receiver. The source and relay nodes of the two-hop AF system employ orthogonal space-time block codes for data transmission. We derive joint optimal source and relay precoders to achieve different tradeoffs between the energy transfer capability and the information rate, which are characterized by the boundary of the so-called rate-energy (R-E) region. Numerical results demonstrate the effect of different parameters on the boundary of the R-E region.

Multipath signals arise in many active sensing modalities, such as radar and sonar. Moving targets cause Doppler effects that could vary for different paths. Target Doppler information corresponding to direct and non-direct paths is important for moving target localizations and classifications, particularly when narrowband signals are involved. This information, however, becomes difficult to reveal when dealing with nonlinear time-varying multi-component Doppler signals. In this paper, we introduce a new time-frequency analysis technique based on the local phase information to accurately extract complex Doppler signature of each signal arrival. This is achieved through short-time polynomial phase modeling of data segments. The global behavior is obtained by fusion of the phases across neighboring segments using the data phase continuity property. The offering of the proposed technique is demonstrated using synthetic data in an over-the-horizon radar platform.

In this paper, we examine the resource optimization problem in a broadcast relay network where a source broadcasts signals to the user receivers, which are distributed over a service region. The source does not have direct line-of-sight to the service area and the information is delivered through a relay. The objective of this paper is to jointly optimize the relay position as well as the power allocation between the source and relay so that the outage probability of the signal received at the user nodes is minimized. Two different optimization criteria, which respectively minimize the worst-case outage probability and the average outage probability, are used. The analyses are verified by simulation results.

In this paper, we consider ultrasound imaging of flaws in a metallic alloy where the presence of strong bottom surface reflection and other interference signals constitutes a challenging problem. A subspace-based approach is developed for removing, or significantly reducing, bottom surface reflections to enhance ultrasound imaging. In constructing the surface reflection, or clutter, subspace, we account for rough surface scatterings which, due to various possible propagation time delays between the transmitter and receiver, expand the subspace dimension beyond that corresponding to ideal propagation. We also estimate and compensate, using signal correlation methods, for changes in the same time delays due to imperfect sensor displacements on the top surface of the alloy. Experimental results show that substantial clutter suppression can be achieved with negligible effects to the flaw signals.

The source and relay precoders are jointly optimized for an amplify-and-forward multiple-input multiple-output relay system. Both the source and relay nodes employ orthogonal space-time block codes (OSTBC), and have imperfect channel state information of the source-relay and relay-destination channels, respectively. Using the worst-case robust design approach, we show that the problem of maximizing the minimum signal-to-noise ratio at the destination can be exactly reformulated as a convex optimization problem. Further, we provide an approximate semi-analytical approach which significantly reduces the computational cost of solving the convex problem. Numerical results show that this approximation is accurate and the proposed design outperforms OSTBC with eigen beamforming, OSTBC with equal power allocation, and currently available worst-case robust beamforming design without OSTBC.

Anti-jamming techniques are critical to maintain the integrity and functionality of GPS systems in various applications. One of the major problems with existing array-based anti-jamming GPS receivers is the errors introduced in the carrier phase, affecting the GPS solution. In this letter, we propose a novel anti-jamming GPS receiver structure that preserves the GPS signal phase continuity. The effectiveness of the proposed technique is verified by simulation results.

In this paper we consider the exploitation of spatial time-frequency distribution (STFD) in multiple-input multiple-output (MIMO) radar systems. STFD has been found useful in solving various array processing problems, such as direction finding and blind separation, where nonstationary signals are involved. Such treatment has been primarily limited to traditional array processing whereas its use in MIMO radar scenario has received less attention. The emphasis of this paper lies in the reexamination of the STFD framework in an MIMO radar platform for the processing of maneuvering targets with nonstationary signatures. Within this framework, we consider the use of joint transmit and receive apertures for the enhancement and improved estimation of time-frequency signature and the application of STFD in joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimations. As a result, it becomes clear that STFD is effective in MIMO radar processing when the targets are of signatures that are highly localized in the time-frequency domain.

This paper considers the maximization of the sum capacity of a multiuser cooperative wireless network through the joint optimization of power allocation among source nodes and distributed beamforming weights across the relay nodes. The distributed beamforming techniques offer the capability of enhancing the sum network capacity by achieving spatial multiplexing to support concurrent communications of multiple source-destination pairs. In this paper, we consider a two-hop cooperative wireless network consisting of single-antenna nodes in which multiple concurrent links are relayed by a number of cooperative nodes. When a large number of relay nodes are available, the channels of the different source-destination pairs can be orthogonalized, yielding enhanced sum network capacity. Such an advantage is particularly significant in high signal-to-noise ratio (SNR) regime, in which the capacity follows a logarithm law with the SNR, whereas exploiting spatial multiplexing of multiple links yields capacity increment linear to the number of users. However, the capacity performance is compromised when the input SNR is low and/or when the number of relay nodes is limited. Joint optimization of source power allocation and distributed relay beamforming is important when the input SNR and/or the number of relay nodes are moderate or wireless channels experience different channel variances. In these cases, the joint optimization of source power and distributed beamforming weights achieves significant capacity increment over both source selection and equal source power spatial multiplexing schemes.

Cooperative wireless network medium access schemes can achieve high throughput through collision resolution. By using a multi-beam adaptive array (MBAA) at a base station or access point, it can concurrently communicate with multiple nodes/users and thus the network performance can be further enhanced. In this paper, we provide an efficient packet resolution method and analyze the throughput of cooperative wireless medium access scheme exploiting MBAAs.

Accurate target localization is an important task in various commercial and military applications. One way to achieve this goal is to use the time-of-arrival (TOA) or time-delay-of-arrival (TDOA) information observed at multiple distributed sensors. On the other hand, there is a great need to use moving sensors to form a radar platform with synthetic apertures. In this paper, we consider the problem of target localization based on the range information estimated from two-way time-of-flight (TW-TOF) at multiple synthetic array locations, where the position of these synthetic array locations is subject to certain random errors. The nonconvex estimation problem is approximated by a convex optimization problem using the semidefinite relaxation (SDR) approach. Simulation results show that the proposed estimator provides mean square position error performance close to the Cramer-Rao lower bound.

An important task in urban sensing applications is to accurately localize moving and vibrating targets in the presence of significant background clutter. A dual-frequency CW radar, which estimates the range of a target based on the phase difference between two closely spaced frequencies, has been shown to be a cost-effective approach for range estimation of a moving target. Previous work has shown that the use of Fourier transform and time-frequency analysis techniques provides an estimate of Doppler signature and enhanced signal-to-noise ratio (SNR), thereby enabling the range estimation of moving targets and significantly improving the estimation accuracy. In this paper, we consider the combined use of these technologies with a synthetic aperture array for the localization of inanimate moving targets. The synthetic array aperture provides the capability of high-resolution spatial localization of inanimate moving targets, as well as determining the orientation of the rotation or vibration.

Multibeam antennas (MBAs) can be used in ad hoc networks to improve throughput performance as a result of increased spatial reuse and extended coverage. For random access scheduling (RAS) protocol, contention resolution is required to overcome the potential collisions. Multipath propagation increases the contention, particularly when an MBA is implemented using the multiple fixed-beam antenna (MFBA) technique. It is desirable to incorporate priority-based channel access scheduling (CAS) into RAS for contention resolution and service differentiation. In this paper, we introduce two priority-based CAS algorithms for contention resolution respectively in single-path and multipath propagation environments, and analyze the one-hop throughput performance of the CAS schemes in an analytical framework. The impact of both quasi-stationary multipath propagation and the CAS algorithms is rigourously examined.

Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.

A dual-frequency radar, which estimates the range of a target based on the phase difference between two closely spaced frequencies, has been shown to be a cost-effective approach to accomplish both range-to-motion estimation and tracking. This approach, however, suffers from two drawbacks: it cannot deal with multiple moving targets, and it has poor performance in noisy environments. In this letter, we propose the use of time-frequency signal representations to overcome these drawbacks. The phase, and subsequently the range information, is obtained based on the moving target instantaneous Doppler frequency law, which is provided through time-frequency signal representations. The case of multiple moving targets is handled by separating the different Doppler signatures prior to phase estimation.

A novel thresholding algorithm is presented in this paper to improve image segmentation performance at a low computational cost. The proposed algorithm uses a normalized graph-cut measure as thresholding principle to distinguish an object from the background. The weight matrices used in evaluating the graph cuts are based on the gray levels of the image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, the proposed algorithm requires much smaller storage space and lower computational complexity than other image segmentation algorithms based on graph cuts. This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition. Several examples are presented, assessing the superior performance of the proposed thresholding algorithm compared with the existing ones. Numerical results also show that the normalized-cut measure is a better thresholding principle compared with other graph-cut measures, such as average-cut and average-association ones.

In this correspondence, we develop a novel approach that provides effective and robust segmentation of color images. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real-time image segmentation processing. It preprocesses an image by using the MS algorithm to form segmented regions that preserve the desirable discontinuity characteristics of the image. The segmented regions are then represented by using the graph structures, and the Ncut method is applied to perform globally optimized clustering. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with significant reduction of the complexity compared to conventional graph-partitioning methods that are directly applied to the image pixels. In addition, the image clustering using the segmented regions, instead of the image pixels, also reduces the sensitivity to noise and results in enhanced image segmentation performance. Furthermore, to avoid some inappropriate partitioning when considering every region as only one graph node, we develop an improved segmentation strategy using multiple child nodes for each region. The superiority of the proposed method is examined and demonstrated through a large number of experiments using color natural scene images.

It is well known that a multiple-input-multiple-output (MIMO) system can provide spatial diversity gain as well as spatial multiplexing capability. The MIMO concent has been extended to cooperative wireless networks to form distributed MIMO systems using virtual antennas located at cooperating terminals. The primary interest of cooperative MIMO networks, however, has been focused on the cooperative diversity (C-DIV) approaches to achieve spatial diversity gain. Recent work proposed cooperative spatial multiplexing (C-SM) to simplify the transmit and receive processing requirement on the relay nodes while providing significant energy savings. So far C-SM has been only considered for single-hop relaying. In this paper, we propose the use of multi-hop relaying C-SM systems for transmit energy reduction and performance improvement.

In this paper, we develop a cooperative diversity scheme that supports decode-and-forward cooperative diversity in the absence of channel state information (CSI) at either user or destination terminals. The proposed scheme employs differential modulation in the broadcast phase, whereas in the relay phase, the information is retransmitted from relay terminals using differential space-time codes. The proposed scheme has a simple structure. In forming a differential space-time code in the relay phase, in addition to the information to be relayed, a relay terminal requires only the portion of the previous codeword transmitted from the same terminal. When different users have different channel quality to the destination, it is pointed out that unitary codes remain the optimum in high signal-to-noise ratio (SNR) scenarios.

In this paper, we propose a known-host-state methodology for designing image watermarks that are particularly robust to compression. The proposed approach outperforms traditional spread spectrum watermarking across all JPEG quality factors. The fundamental approach uses 2D chirps as spreading functions, followed by a chirp transform, to recover the watermark. Because this method can spectrally shape the chirp to match image content and JPEG quantization, its performance is greatly enhanced. The energy localization of the chirp is exploited to embed low power watermark per image blocks while maintaining reliable detection performance.

This paper presents an approach for tracking nonstationary moving sources with both time-varying directions-of-arrival (DOA) and time-varying polarization signatures. The proposed approach is based on the spatial polarimetric time-frequency distributions (SPTFD). Unlike the conventional correlation matrix based approaches that sacrifice the source signal polarization properties and are not properly structured to utilize polarization diversity, the proposed approach uses the signal instantaneous polarization and instantaneous frequency information for improved target tracking and polarization estimation.

The design of a linear space-time code with full rate, large diversity product, and non-vanishing minimum determinant of codewords continues to attract great attention. However, in most available no-vanishing determinant space-time codes for three, four, and six transmitter antennas, the average power at each layer is different, which results in a high peak to average power ratio. In this paper, a new cyclic algebraic space-time design scheme is proposed and the optimal codes in this class are provided by using some specific cyclic field extensions. Our proposed codes not only include the available non-vanishing determinant cyclotomic space-time codes for three, four, and six transmitter antennas, but also have the desirable property that the optimal codes can be achieved with the same average power at each layer.

Bilinear synthesis of nonstationary signals impinging on a multi-antenna receiver has been recently introduced. The distinction in the spatial signatures of the sources provides a vehicle to reduce noise and source signal interactions in the time-frequency domain, and hence improves signal synthesis. In addition to the spatial domain information, we utilize polarization diversity for enhanced source time-frequency signal representations. It is shown that dual-polarization antennas call be used to mitigate cross-terms via combined spatial and polarization averaging. Significant reduction in cross-terms can be realized by providing large spatial diversity, large polarization diversity, or combined moderate values of the respective spatial and polarization correlations.

An enhanced technique for estimating the range and direction-of-arrival (DOA) of narrowband near-field sources is presented. This technique utilizes fourth-order cumulants of the received signal across an array of two orthogonally polarized sensors. It is shown that the incorporation of the source polarization in an ESPRIT-based angle and range estimation technique provides improved performance over the case where the polarization information is absent in the problem formulation.

Bilinear synthesis of nonstationary signals impinging on a multiantenna receiver has been recently introduced. The distinction in the spatial signatures of the sources provides a vehicle to reduce noise and source signal interactions in the time-frequency domain, and hence improves signal synthesis. In this letter, we utilize another form of diversity for enhanced source time-frequency signal representations. It is shown that cross-polarization antennas can be used to mitigate cross terms via simple polarization averaging.

A novel high-resolution time-frequency representation method is proposed for source detection and classification in over-the-horizon radar (OTHR) systems. A data-dependent kernel is applied in the ambiguity domain to capture the target signal components, which are then resolved using the root-MUSIC based coherent spectrum estimation. This method is particularly effective to analyze a multi-component signal with time-varying time-Doppler signatures. By using the different time-Doppler signatures embedded in the multipath signals, this proposed method can reveal important target maneuvering information, whereas other linear and bilinear time-frequency representation methods fail.

In over-the-horizon radar (OTHR) target detection, the signal-to-clutter ratio (SCR) is very low, typically from -50 dB to -60 dB. Furthermore, for maneuvering targets, such as aircraft and missiles, Doppler frequencies of their radar return signals may be time-varying. In this case, the Fourier transform based techniques and super resolution spectrum estimation techniques may not work well since they use sinusoidal signal models. We propose a signal subspace clutter rejection algorithm combined with an adaptive chirplet transform technique for maneuvering target detection with OTHR. Simulation results of adding simulated maneuvering targets into raw OTHR clutter data are presented to illustrate the effectiveness of the proposed algorithm. The simulation results show that moving targets with -53.5 dB SCR can be detected.

In this paper, we present the formulation of the super-exponential blind adaptive beamforming algorithm; which is an extension of the cumulant-based super-exponential blind deconvolution theory presented by Shalvi and Weinstein. Simulation results show the efficacy of the algorithm.

In this paper, we propose a new nonstationary jammer suppression technique for DS/SS communications. This technique is based on combined operations of bilinear signal synthesis and projections. The time-frequency distributions (TFDs) is used to define the jammer time-frequency (t-f) signature. Multi-sensor array at the receiver allows both the spatial and temporal signatures of the signal arrivals to be utilized. To estimate the waveform of a jammer, a mask is constructed and applied such that the masked t-f region captures the jammer energy, but leaves out most of the DS/SS signals. The jammer signals are then synthesized from the masked TFDs that can be removed from the received signal by orthogonal projection.

Multiple source signals impinging on an antenna array can be separated by time-frequency synthesis techniques. Averaging of the time-frequency distributions of the data across the array permits the spatial signatures of sources to play a fundamental role in improving the synthesis performance. This improvement is achieved independent of the temporal characteristics of the source signals and without causing any smearing of the signal terms. Unlike the recently devised blind source separation methods using spatial time-frequency distributions, the proposed method does not require whitening or retrieval of the source directional matrix.

Blind source separation based on spatial time-frequency distributions (STFDs) has been previously introduced. This method provides improved performance over blind source separation methods based on second-order statistics, when dealing with nonstationary signals that are localizable in the time-frequency domain. In the STFD method, the covariance matrix is first used to whiten the signal vector, then the unitary matrix is estimated using high signal-to-noise ratio (SNR) time-frequency points. This paper modifies the STFD method by performing both whitening and estimation steps using STFD matrices. The eigenvectors of the signal subspace obtained from a properly selected STFD matrix are more robust to noise than those obtained from the covariance matrix and, therefore, are more appropriate to use, particularly for low SNR environments. Further, for small array apertures and high number of arrivals, the STFD whitening matrix can be used as means to reduce the number of signals considered by the blind source separation algorithm.

This letter introduces the spatial ambiguity functions (SAFs) and discusses their applications to direction finding and source separation problems. We emphasize two properties of SAFs that make them an attractive tool for array signal processing.

This paper presents a novel approach based on time-frequency distributions (TFDs) for separating signals received by a multiple antenna array. This approach provides a significant improvement in performance over the previously introduced spatial time-frequency distributions, specifically for signals with close time-frequency signatures. In this approach, spatial averaging of the time-frequency distributions of the sensor data is performed to eliminate the interactions of the sources signals in the time-frequency domain, and as such restore the realness property and the diagonal structure of the source TFDs, which are necessary for source separation. It is shown that the proposed approach yields improved performance over both cases of no spatial averaging and averaging using time-frequency smoothing kernels.