Hard thresholding-based algorithms have demonstrated outstanding ability in sparse signal recovery. To further enhance their performance, this letter proposes a momentum-based hard thresholding pursuit (MHTP) algorithm, which integrates a momentum-accelerated gradient update with a pursuit step that solves constrained least-squares problems on the estimated support. A sufficient condition for exact sparse recovery under the restricted isometry property (RIP) framework, guaranteeing the convergence of MHTP, is derived. Experimental results demonstrate that MHTP achieves faster convergence and higher recovery accuracy compared to existing methods.

In dual-function radar-communication (DFRC) systems, precise and concurrent estimations of channel state information (CSI) and target directions are imperative to ensure simultaneous communications and sensing. This paper delves into a massive MIMO system employing hybrid beamforming (HBF), identified as a viable solution for achieving significant antenna gains while maintaining manageable hardware costs. The study focuses on a MIMO-DFRC system with a subarray-connection HBF architecture, proposing a preamble-by-preamble methodology to enable simultaneous wireless communications and target tracking on a per-frame basis. In the initial frames, we exploit the Doppler discrepancies between communication paths and fast-moving targets to segregate them. This sets the stage for iterative refinements of Doppler frequencies, angles of departure (AoDs), and angles of arrival (AoAs) estimations in the least square manner. Utilizing these estimations accrued from previous frames, the hybrid precoder and combiner of the subsequent frames are designed to boost a weighted signal-to-noise ratio (SNR), safeguarding the target tracking accuracy while concurrently refining the CSI estimation. Numerical simulations validate the proposed algorithm, demonstrating effective tracking performance with low overhead in MIMO-DFRC systems.

For multichannel synthetic aperture radar (SAR) ground moving target identification (GMTI) in heterogeneous and strong clutter backgrounds, it is challenging to accurately detect slow and weak targets when relying solely on the magnitude of clutter-suppression residuals, due to the significant clutter residuals and signal-to-noise losses. To address this, a detector that leverages both the magnitude and phase information in multichannel SAR-GMTI clutter suppression is proposed. For aM-channel SAR system, the detection test is formulated as the product of the residual magnitude fromM-channel clutter suppression and a phase factor derived from the interferometric phase between two residuals from the first and lastM- 1 channels. This phase factor captures the dissimilarity from the clutter, enabling the suppression of strong clutter residuals and improving the signal-to-clutter-plus-noise ratio (SCNR). Using the product clutter model, a constant false alarm ratio detection framework is designed. The receiver operator characteristic metrics, obtained from simulations and real-data experiments, validate the proposed method’s superiority over the state-of-the-art techniques, and the detection sensitivity on the clutter heterogeneity, correlation coefficients between pairs of clutter-suppression residuals, and target parameters is analyzed for practicality. In the X-band airborne radar GMTI experiments, the minimum discernible input SCNR of -6 dB for target radial velocity of 4 m/s and 0 dB for 2 m/s demonstrate the effectiveness in detecting the dim targets within strong clutter.

In ground-based L-band radar sea clutter, Bragg scattering caused by short gravity waves on the sea surface frequently exhibits azimuthal dependence, with higher order Bragg-scattering spectra clearly visible alongside the first-order spectrum. Recent measurements from an L-band airborne moving target detection (MTD) radar, operating in side-looking mode with HH polarization at medium incident angles (30°–60°), near the Zhoushan Fishing Ground in Ningbo, China, also reveal azimuth-dependent and multipeak characteristics, that pose challenges for target detection within the endo-clutter region. To better understand the clutter characteristics in L-band airborne MTD radar, this article investigates the modeling of sea clutter Doppler spectra under medium incident angles ranging from 30° to 60°. Using the small slope approximation (SSA) incorporating a time-varying rough sea surface with spikes, a systemic expression for the Doppler spectrum, accounting for the sea clutter space-time coupling, is derived. Specifically, the Doppler spectrum related to the sea surface is expressed as an azimuth-dependent underlying spectrum weighted by the antenna beam (i.e., the spatial spectrum), while the spectrum due to spikes is represented as a convolution of the spatial spectrum with an azimuth-independent underlying spectrum. Each individual spectrum is characterized with Gaussian profiles, forming the basis of a comprehensive spectrum model that can successfully capture an azimuth-independent peak and approximately 2–7 or more azimuth-dependent peaks in the real-world sea-clutter Doppler spectra. Note that the proposed model is directed against sea echoes with azimuth-dependent scattering properties from the main lobe of the two-way antenna pattern, and thus, suitably characterizes the corresponding sea-clutter Doppler features.

This paper considers hybrid beamforming in multiple-input multiple-output integrated sensing and communications systems. In particular, the transmit hybrid beamformers are jointly designed with digital receive beamformers of users by maximizing the ratio of minimum mainlobe level to peak sidelobe level of the transmit beampattern, under constraints on the transmission power and signal-to-interference-plus-noise ratio requirements of users. In order to deal with the resulting nonconvex problem, a computationally efficient algorithm is developed under the framework of consensus alternating direction method of multipliers, by deriving the solution of each subproblem. The proposed design is verified by simulation results.

In this paper, we present a new synthetic aperture method for direction-of-arrival (DOA) estimation using a passive towed sonar array that is deformed during platform maneuver. With certain prior knowledge of source-array geometry, we propose to find the optimal maximum likelihood estimates of DOAs and sensor positions by maximizing the model evidence of these parameters and the array observations. In order to tackle the resulting complex problem, the variational Bayesian Expectation-Maximization framework is employed. The proposed method is capable of achieving improved angular resolution by aperture synthesis and is robust to perturbations in the array manifold. Numerical simulations illustrate statistical efficiency of the proposed technique.

In this article, an optimal fusion-based target detector with multichannel along-track interferometry (ATI) synthetic aperture radar (SAR) is investigated for ground moving target indication (GMTI) in a heterogeneous background. In the proposed method, a group of multibaseline ATI-phase tests for fully exploiting the spatial degrees of freedom (DoF) and the adaptive matched filter (AMF) test are first exploited for local target detection. Then, the global detection is designed based on Chair and Varshney’s optimal fusion rule. The optimal local detection thresholds are determined by designing the global optimization in terms of the optimal fusion rule. Therefore, the maximum target detection performance under the constant probability of false alarm (Pfa) can be achieved. To facilitate the implementation, a low-complexity solution to the global optimization and a cognitive detection framework for the proposed detector without the prior target knowledge is developed. Simulation and experimental results of the receiver operator characteristic (ROC) demonstrate its significant advantages in detecting dim targets that have a low signal-to-clutter-plus-noise ratio (SCNR) and small radial velocity in heterogeneous clutter backgrounds.

This article focuses on power allocation and sampling rate control in a spectrum-sharing vehicle-to-vehicle (V2V) status update network, where data packets are sampled in batches at the transmitter of the V2V links. In particular, we aim to minimize the overall power consumption while satisfying the age of information (AoI) requirement of all links. First, we analyze the average AoI of the resulting queueing system with periodical packet batch arrivals and geometrically distributed packet service time. Then, the primal problem is decomposed into two subproblems, i.e., sampling rate optimization and transmit power optimization. Finally, a computationally efficient algorithm with polynomial-time complexity is developed that optimally solves the two subproblems and, thus, solves the original problem with global optimality. Simulation results validate our average AoI analysis and the proposed algorithm.

Arrays with a large number of antennas can achieve outstanding performance for both communication and radar sensing. This motivates us to investigate the transmit beamforming design for massive multiple-input multiple-output integrated sensing and communications (mMIMO-ISAC) systems. Inspired by the facts that linear precoding methods can provide very good performance in mMIMO and that a dual-function ISAC transmit beamformer should be suitable for both communication and radar sensing, we propose an implementation-friendly mMIMO-ISAC transmit beamformer as a weighted combination of a linear precoder and a pre-designed array beamformer. The weights are selected and adjusted through the powers allocated to the beamformers, while the design problem is formulated as a total transmit power minimization, while satisfying the requirements for both sensing and communication. Leveraging the use-and-then-forget strategy, simplified performance metrics for communication and sensing under the proposed mMIMO-ISAC transmit model are derived, which yields linear programming (LP) problems for the design. It is shown that analytical solutions can be obtained and, hence, the proposed design is computationally very attractive. Simulations are carried out to demonstrate the effectiveness and performance of the proposed methods.

In this paper, we consider the problem of transmit beamforming design for massive multiple-input multiple-output integrated sensing and communications (MMIMO-ISAC) systems. Different from the existing designs for regular MIMO-ISAC systems, which directly optimize the dual-function beamformer matrix and are computationally expensive especially for large-scale antenna arrays, we propose to construct the beamformer as a weighted sum of the maximum ratio precoder and a desired sensing beamformer. This is motivated by the fact that maximum ratio precoding schemes are simple, and performs well in MMIMO systems. The weights involved in the beamformer are adjusted via optimizing the powers allocated to the users and sensing. A linear programming (LP) problem, which minimizes the total transmit power subject to the performance constraints of communication and sensing, is thus formulated. It is shown that the LP problem can be analytically solved. Therefore, the proposed transmit beamforming design has very low computational complexity. Its effectiveness and performance are illustrated by simulations.

In this paper, we investigate the problem of age-of-information (AoI)-aware dynamic user scheduling in vehicular networks based on soft reinforcement learning, where multiple vehicle-to-infrastructure (V2I) downlinks share the spectrum resource. To address the slow convergence and local optimality problem that is usually faced by traditional reinforcement learning, we formulate the AoI-aware user scheduling problem as a sequential decision making problem and then use the soft actor-critic (SAC) reinforcement learning algorithm to address it. The agent, i.e., the road side unit (RSU), chooses an appropriate V2I link to occupy the spectrum to send data at each slot to decrease the AoI outage probability of the status update system in vehicular networks. By maximizing the expected return and policy entropy, the agent can converge fast to an efficient solution while holding some robustness to cope with the differences between actual and training environments. Simulation results validate the proposed scheme in terms of convergence, effectiveness, and robustness.

In this paper, a non-orthogonal multiple access (NOMA) based downlink vehicle-to-infrastructure network is considered. Particularly, we focus on the specific case of two users, one of which requires reliable road-safety-critical data transmission while the other pursues high-capacity services, with extension to multi-user scenarios. Leveraging only slow fading of channel state information, the transmit powers and target rates are jointly optimized to maximize the expected sum throughput of the capacity hungry user, with consideration of the payload delivery outage probability of the reliability sensitive user. The optimization is formulated as an unconstrained single-objective sequential decision problem via introducing a dual variable. A dynamic programming based algorithm is then designed to derive the optimal policy that maximizes the Lagrangian. Afterwards, a bisection search based method is proposed to find the optimal dual variable. The proposed scheme is shown by numerical results to be superior to the baseline methods in terms of the expected return, performance region, and objective value.

Abstract Radar and communication are two key applications of radio technology, and they occupy a large portion of the frequency spectrum. Traditionally, radar and communication systems are operated at different frequencies, owing to their different functions and application areas. For instance, radar was mainly employed for sensing (target detection, localization, recognition, imaging, etc.) in the military field, while wireless communication was mainly for information delivery. However, along with the fast development of radio technologies and huge demand for information, the radio frequency (RF) spectrum is becoming increasingly congested, and the spectra of the radar system will be overlaid with those of wireless communication devices. Moreover, radar and communication are becoming increasingly merged in both technologies and applications. Besides the military field, radar has been widely employed in daily life including weather service, air traffic control, autonomous driving and security monitoring. Meanwhile, these applications rely Largely on information transmission through wireless communications. In this regard, integration of radar and communication (IRC) has proved to be a very promising development to address the spectrum congestion issue between radar and communications devices. This also brings us a number of key challenges in signal processing for both implementation of IRC and joint optimization between the two systems.

Abstract In this paper, we investigate the joint design of transmit waveform and receive filter for colocated Multiple‐Input Multiple‐Output radar equipped with one‐bit digital‐to‐analogue converters (DACs). The problem is formulated as maximising the output signal‐to‐interference‐plus‐noise ratio in the presence of signal‐dependent interferences, subject to a discrete constraint imposed by the waveform quantised with one‐bit DACs. To cope with the challenging non‐convex problem, an alternating maximisation framework is developed to optimise the transmit waveform and receive filter vectors in an iterative manner. More specifically, for a given transmit waveform vector, the analytical expression of the receive filter vector is first derived. Then, by fixing the receive filter, a biconvex relaxation method is employed to tackle the non‐convex problem with respect to the transmit waveform vector. The performance of the proposed approach is demonstrated by numerical simulations in different circumstances.

Abstract In this paper, the problem of hybrid beamforming design in an orthogonal frequency division multiplexing dual‐function radar‐communication system to achieve optimised transmit beampattern subject to the constraints of communication performance and transmit power is investigated. Specifically, the analogue beamformer and digital beamformer for each subcarrier at the transmitter are designed by maximising the integrated mainlobe‐to‐sidelobe ratio (IMSR) of the transmit beampattern under the restriction of the communication signal‐to‐interference‐plus‐noise ratio (SINR). Since the design problem is a complicated fractional programing problem, the quadratic transform technique is introduced for problem simplification. Under the framework of a consensus‐alternating direction multiplier method, the solutions to the subproblems in the iterations are presented in detail. Finally, simulation results are demonstrated to verify the effectiveness of the proposed algorithm.

In this paper, the issues of transmit hybrid beamforming (HBF) design and direction-of-arrival (DOA) estimation in multi-carrier dual-function radar-communication (DFRC) systems, in consideration of quality-of-service (QoS) for multiple users (MUs), are investigated. In the designed system, communication symbols are embedded into radar pulse interval with multiple orthogonal waveforms, and the HBF is optimized to focus the transmit energy within the spatial sectors of interest by taking the QoS requirement for MUs into account. The problem involving these considerations is formulated as the minimization of mean squared error (MSE) between the achieved spatial spectrum and a desired one, subject to constraints of communication QoS, constant modulus, power and orthogonality. Accordingly, a consensus alternating direction method of multipliers (consensus-ADMM) framework based on weighted mean-square error minimization (WMMSE) is devised to tackle the resultant nonconvex problem. Further, the closed-form solutions of the primal variables are derived. Additionally, the MUltiple SIgnal Classification (MUSIC)-based DOA estimation with the designed HBF architecture is presented, and the corresponding Cramér-Rao bound is derived. Numerical simulations are performed to demonstrate the effectiveness of the proposed designs.

This paper considers the hybrid beamforming design for wideband OFDM-DFRC system serving multiple users (MUs). The analog beamformer for the whole bandwidth and the digital beamformers for each subcarrier and are jointly optimized by considering flexible performance trade-off between the radar and communication. To deal with the resulting optimization problem, a consensus alternating direction method of multipliers (consensus-ADMM) framework based on the weighted mean-square error minimization (WMMSE) approach is proposed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.

In this paper, a single source points (SSPs) detection method based on local dominance is devised for mixing matrix estimation in underdetermined blind source separation (UBSS). In the proposed detector, time-frequency (TF) points of mixed signals are firstly divided into different groups, and the local covariance matrix of each group is calculated. Taking advantage of the local dominance property, the groups with rank-one local covariance matrices are then identified as single source groups, i.e., the TF points of mixed signals in single source groups are regarded as SSPs. Finally, the obtained SSPs are clustered by the hierarchical clustering algorithm to achieve mixing matrix estimation. Simulations with real audio sources show that the proposed method yields competitive robustness, efficiency and effectiveness, in comparison with the traditional methods.

In this paper, the problem of transmit sequence design for a dual-function radar-communication (DFRC) system equipped with one-bit digital-to-analog converters (DACs) is investigated. More specifically, a nonconvex problem is formulated by minimizing the symbol mean-square error, while ensuring the target localization performance for radar. A direct one-bit sequence design tackled by an alternating minimization (AM) approach, which involves a simple unconstrained quadratic sub-problem with closed-form solution and a quadratically constrained nonconvex sub-problem tackled by the alternating direction method of multipliers (ADMM) algorithm, is developed. The solutions of primal variables under the ADMM framework are provided and the convergence is discussed. Additionally, an indirect but computationally more efficient design, which is realized by transmit beamforming based on an accelerated primal gradient (APG) method, is presented. For better understanding of this design, both the Cramér-Rao Bound (CRB) and symbol error probability of the resulting beamformer with one-bit quantization using the Bussgang theorem are analyzed. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

In general, weighted compressive sensing recovery needs to solve optimisation problems with the objective function being the sum of a weighted ‐norm and a regularised differentiable convex function. Note that the weights in the weight vector are assumed to be positive. In fact, it is possible to achieve outstanding signal recovery performance even if some of the weights are appropriately designed to be negative. However, the negative weights lead to the non‐convexity of the optimisation problem, which would place a barrier to attain the optimal solution. To deal with this issue, in this study, the authors propose to solve the problem by applying a well‐established algorithm, namely, the alternating direction method of multipliers (ADMM) algorithm. It is shown that the optimal solution can be obtained by taking advantage of this optimisation scheme. The performance of the proposed algorithm is demonstrated by numerical results.

Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit-receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit-receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

This paper focuses on the problem of coexistence between a colocated multiple-in multiple-out (MIMO) radar and down-link communication systems. We propose an iterative algorithm to minimize the Cramer-Rao Bound (CRB) on the direction of arrival (DOA) of a target, accounting for the energy and similarity constraints. More Specifically, at each iteration of this algorithm, the radar waveform is obtained with the aid of the alternating direction method of multipliers (ADMM) algorithm, and then the communication weights are obtained by exploiting the block successive upper-bound minimization method (BSUM). Finally, numerical results are presented to evaluate the effectiveness of the proposed algorithm.

In this paper, we investigate the problem of transmit beam-pattern design for MIMO radar with one-bit DACs. Based on the Fast Fourier Transform (FFT), we propose an alternating minimization (AM) method to minimize the weighted squared-error (WSE) between the designed beampattern and a given one. In particular, we propose an approximation approach to achieve a high-quality solution with one-bit constraint. Finally, numerical simulations demonstrate that the effectiveness of the proposed method.

A modified nested linear array (MNLA) has been reported recently for a greater potential in increasing the degree-of-freedom. However, there exist some "holes" in the difference co-array, which results in missing "lags" and limited performance of direction-of-arrival (DOA) estimation. In order to tackle this problem, this paper applies a Toeplitz matrix completion technique to MNLA, and investigates the performance of DOA estimation on this basis. Particularly, a semidefinite program with trace minimization is derived to obtain the covariance matrix with Hermitian and Toeplitz structure. The recovered Toeplitz covariance matrix is then utilized to perform DOA estimation. Various numerical examples are provided to verify the effectiveness and superiority of the proposed method.

The problem of transmit beampattern synthesis in multiple input multiple output (MIMO) radar for target localization is investigated in this paper. By appropriately designing the cross correlation matrix of the transmitted signal waveforms, we can focus the transmit energy into the sector(s) of interest where targets are likely to be located. The proposed energy focusing approach is capable of enhancing the intensity of signals which are reflected from the targets and hence the preferable performance of target localization can be attained. Comparing with the existing energy focusing techniques, our new method realizes a desired pattern via designing the waveform cross correlation matrix rather than the transmit weight vector. Moreover, it does not impose additional transmit power constraints or require a prescribed beampattern to be approximated. Simulation results demonstrate that an improvement of the target localization performance can be offered by the proposed MIMO radar transmit beampattern design technique compared with existing approaches.

It is known that deploying low-resolution (e.g., one-bit) analog-to-digital converters (ADCs) at the receive antennas can reduce the hardware cost and circuit power consumption, especially for large-scale systems. In this context, we investigate the performance of target detection for collocated multiple-input multiple-output (MIMO) radar with one-bit ADCs. Considering a scenario where the transmit-receive channel matrix suffers from uncertainty with a known distribution, a one-bit likelihood ratio test (LRT) detector is derived. Moreover, the asymptotic performance of this detector is analyzed. Numerical simulations are performed to demonstrate our theoretical analysis and evaluate the performance of the one-bit LRT detector.

In this letter, we consider the problem of direction-of-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters are unknown because of the extreme quantization, they do not affect the subspace-based DOA estimators. Specifically, the signal and noise subspaces can be straightforwardly determined through the eigendecomposition of the one-bit covariance matrix, without pre-processing such as unquantized covariance matrix reconstruction. With so-obtained subspaces, the most classical multiple signal classification (MUSIC) technique can be applied to determine the signal DOAs. The resulting method is thus termed as one-bit MUSIC. Thanks to the simplicity of this method, it can be very easily implemented in practical applications, whereas the DOA estimation performance is comparable to the case with unquantized covariance matrix reconstruction, as demonstrated by various simulations.

Deploying low-resolution (e.g. one-bit) digital-to-analog converters (DACs) is of great importance in the multiple-input multiple-output (MIMO) system equipped with a large-scale antenna array since such a hardware architecture brings low-cost and circuit power saving for each antenna. In this paper, the problem of transmit signal design in a large-scale MIMO system with 1-bit DACs is investigated. To ensure directional transmission, we propose to design the transmit signal by minimizing the weighted mean-squared error (MSE) between the formed beampattern and a given one. The resulting design problem, which involves a nonconvex fourth-order objective and a set of nonconvex discrete constraints, is NP-hard, and therefore, an alternating minimization (AM) method is devised. In order to obtain a high-quality 1-bit solution, we propose a continuous and differentiable function to approximate the 1-bit signal, such that the problem with discrete 1-bit constraint is recast to an unconstrained optimization problem with a penalty term, which can be effectively solved via the limited-memory Broyden, Fletcher, Goldfarb, and Shanno (L-BFGS) approach. Moreover, it is found that a closed-form solution can be obtained when equal weights are applied. In addition, low-complexity schemes are developed based on the fast Fourier transform (FFT). The numerical simulations are conducted to demonstrate the effectiveness and superiority of the proposed method.

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, geolocation, and smart cities. Wi-Fi-based indoor localization using received signal strength (RSS) has drawn much attention over the past decade because it does not require extra infrastructure and specialized hardware. It is well known that the localization accuracy using RSS is rather susceptible to the changing environment. Localization by fusing multiple fingerprint functions of RSS is a promising strategy to overcome the above drawback. However, the existing fusion techniques cannot make full use of the intrinsic complementarity among multiple fingerprint functions. It also fails to exploit the knowledge obtained in the offline phase and thus shows low accuracy in the complex environment. This paper proposes a knowledge aided adaptive localization (KAAL) approach by using a global fusion profile (GFP) to mitigate the above shortcomings. First, we propose a GFP construction algorithm by minimizing position errors over all fingerprint functions with weight constraints in the offline phase. Based on the knowledge from GFP and the trained multiple fingerprint models, we then derive two KAAL algorithms, namely, multiple function averaging and optimal function selection, to achieve highly accurate localization results. Experimental results demonstrate that our proposed localization approach is superior to the existing methods both in simulated and real environments.

When considering the problem of direction-of-arrival (DOA) estimation, uniform noise is often assumed and hence, the corresponding noise covariance matrix is diagonal and has identical diagonal entries. However, this does not always hold true since the noise is nonuniform in certain applications and a model of arbitrary diagonal noise covariance matrix should be adopted. To this end, a simple approach to handling the unknown nonuniform noise problem is proposed. In particular, an iterative procedure is developed to determine the signal subspace and noise covariance matrix. As a consequence, existing subspace-based DOA estimators such as MUSIC can be applied. Furthermore, the proposed method converges within very few iterations, in each of which closed-form estimates of the signal subspace and noise covariance matrix can be achieved. Hence, it is much more computationally attractive than conventional methods which rely on multi-dimensional search. It is shown that the proposed method enjoys good performance, simplicity and low computational cost, which are desirable in practical applications.

This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm, a new BKF-SGM with improved MS (BKF-SGM-IMS) algorithm with more robust tracking performance is also proposed. Experimental results show that our method can successfully handle complex scenarios with good performance and low arithmetic complexity.

A novel class of spatial four degree-of-freedom Schönflies-motion parallel manipulators with four identical subchains is presented. Their features are that each serial subchain undergoes the pure Schönflies motion without redundant joints. The parallel mechanisms possess the simplest topology and are suitable for pick-and-place operations. Kinematic analysis of the 4-PRPaR parallel manipulator, including its inverse and forward kinematics, singularity, and workspace, is discussed in detail. The analysis shows that the moving platform and the base must be in dissimilar dimension for good manipulability performance. The optimal design of the parallel manipulator is formulated as a multiobjective optimization problem. A novel performance index characterizing the approximation of the generated workspace to the prescribed regular workspace, the regular workspace share, is proposed to serve as one of the design objectives. The other objective is the global condition index, which measures the manipulability. The multiobjective optimization problem provides multiple optimal solutions for choice. Simulation verifies that the designed parallel manipulator can approximate the prescribed regular workspace with good condition index.

This paper addresses the problem of beamforming for antenna arrays in the presence of mismatches between the true and nominal steering vectors. A new method for robust beamforming is proposed by minimizing the array output power while controlling the array mainlobe response. Due to the presence of the non-convex response constraints, a new approach based on iteratively linearizing the non-convex constraints is proposed to reformulate the non-convex problem to a series of second-order cone programming (SOCP) subproblems, each of which can be optimally solved by well-established convex optimization techniques. Simulation results show that the proposed method offers better performance than conventional methods tested.

This paper proposes two subspace-based methods for direction-of-arrival (DOA) estimation of coherent signals for uniform linear arrays (ULAs) with unknown mutual coupling. Based on the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), the conventional spatial smoothing method can be exploited for DOA estimation of coherent signals. We firstly study a new scheme with spatial smoothing on the middle subarray, which can be considered as an ideal array without uncertainties based on the special property of mutual coupling of ULAs. Next, an alternative method with larger effective antenna aperture is proposed. Unlike the first method, the latter one smoothes on the whole array instead of the middle subarray and a better performance is achieved. Numerical examples are given to illustrate the performance of these two algorithms for coherent DOA estimation in the presence of mutual coupling.

This paper develops a subspace-based method for direction-of-arrival (DOA) estimation of uniform linear array (ULA) in the presence of mutual coupling. As the mutual coupling coefficient between two sensor elements is inversely related to their separation and is negligible when they are separated by a few wavelengths, the mutual coupling matrix (MCM) of a ULA can be well approximated as a banded symmetric Toeplitz matrix, which greatly reduces the number of unknown parameters to be estimated. Using the subspace principle, we propose a new method for joint estimation of the DOAs of incoming signals and banded symmetric Toeplitz MCM by reconstructing the steering vector to a specific matrix form. The proposed method achieves a better performance especially for weak signals than the method in, since the whole array, instead of the middle subarray in, is used for DOA estimation. Simulation results illustrate that both DOAs and mutual coupling coefficients can be estimated efficiently with the proposed method.