Coprime array enables derivation of an extended array with the number of virtual elements beyond the number of physical sensors, resolving the underdetermined direction of-arrival (DOA) estimation problem while mitigating mutual coupling effects. However, the virtual array is discontinuous and contains some holes. To fully exploit the information of the virtual array, conventional methods typically employ complex matrix completion (MC) to iteratively recover the data of the covariance matrix, resulting in high complexity, particularly with large-scale coprime arrays. In this letter, a real-valued DOA estimation via the construction of Toeplitz covariance matrix and spline interpolation is proposed. The algorithm complexity is reduced by real-valued Toeplitz matrix, and then the holes are filled by spline interpolation. The recovered matrix can be used for DOA estimation by conventional subspace approaches. The superior computational performance of our approach is validated through comparative simulations with established methods.

This letter investigates the multi-target direction of arrival (DOA) tracking problem under wireless sensor networks (WSNs), where each node is equipped with a uniform linear array (ULA). A WSN probability hypothesis density (WSN-PHD) tracking algorithm is proposed with two key innovations: 1) an exponential accumulation (EA) fusion strategy that aggregates multi-node measurements to resolve occlusions in WSNs, thereby improving tracking continuity under time-varying targets, and 2) a non-circular phase-assisted likelihood function to enhance target tracking accuracy. Numerical results show that the proposed algorithm achieves average optimal sub-pattern assignment (OSPA) distance and location errors reduction of 78.18% and 76.82% compared to the algorithm without EA fusion.

The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.

Time-delay estimation (TDE) holds great significance in pavement surveys, especially in the modern transportation system. Established on the ideal Dirac pulse and white Gaussian noise, the performance of the existing TDE methods may degrade in practical ground penetrating radar (GPR) detection, where radar pulse and noise distribution are diverse. In this letter, we develop a gridless TDE method considering the radar pulse and noise pattern in GPR detection. Co-prime sampling strategy is applied to reduce the number of frequency samples compared with conventional uniform sampling. With the prior knowledge of radar pulse and noise distribution, an enhanced measurement vector is generated from the data covariance matrix, thus improving the signal quality compared with the conventional methods which are based on ideal Dirac pulse and white Gaussian noise. Subsequently, the time-delays are estimated by the proposed atomic norm minimization (ANM) method, where the complexity is further reduced compared with the previous works using multiple measurements. Simulation results show the advantages of the proposed method in terms of running time, weak echo detection, and estimation accuracy.

Multi-beam frequency beam scanning leaky-wave antennas (FBS-LWAs) enable a compact system with reduced bandwidth but introduce parasitic interference that impairs direction-of-arrival (DOA) estimation. To solve this issue, this letter proposes a novel DOA estimation method tailored for multi-beam FBS-LWAs. Theoretical analysis reveals that the true DOAs align with multiple peaks in the radiation pattern. Based on this insight, a cropped hypercomplete set is constructed via grid refinement in the frequency-space domain. The sparse Bayesian learning algorithm, enhanced by singular value decomposition (SVD-SBL), is then employed to suppress parasitic peaks and accurately recover the true DOAs. Simulation results demonstrate the proposed method’s effectiveness and robustness.

Existing techniques for joint direction-of-arrival (DOA) and range estimation, which often assume that the incoming signals are uncorrelated, suffer from performance degradation when dealing with coherent sources. To overcome this limitation, we propose a novel algorithm based on the moving time-modulated frequency diverse coprime array (MTFDCA). The proposed method constructs a covariance matrix by selectively processing data vectors at specific time instants, effectively addressing signal coherency and enabling robust joint estimation in both DOA and range. Inspired by the forward and backward spatial smoothing (FBSS) concept, we refine the covariance structure to enhance the resolution and employ 2D-MUSIC for high-precision estimation. This approach achieves superior performance with lower computation complexity. Simulation results demonstrate the effectiveness of the algorithm and highlight its ability to handle coherent signals with improved accuracy and efficiency.

Recently, direction-of-arrival (DOA) estimation in impulsive noise scenarios has been extensively investigated in the field of co-prime array signal processing. This paper proposes a combined enhanced bounded nonlinear covariance and phased fractional low-order moment (EBNC-PFLOM) method, which incorporates the advantages of both EBNC and PFLOM and mitigates the impulsive noise by constructing the equivalent data covariance matrix of the received signals. Furthermore, when dealing with a limited number of input signals, the proposed method is capable of directly estimating the signals’ DOA without spatial smoothing. Simulation results show that the proposed method outperforms the recently reported methods.

In this paper, we propose a nested sampling based off-grid block sparse representation method (Nested-OGBSR) for time-delay estimation (TDE) of coherent ground penetrating radar (GPR) backscattered echoes. Nested sampling strategy is taken to reduce the sampling rate and computational burden. The off-grid data model is adopted to eliminate the effect of basis mismatch caused by the predefined grids in sparse representation (SR) and thus improve the estimation accuracy. The non-circularity of GPR signals is also utilized to enhance the temporal resolution of sparse block representation (BSR). Numerical and experimental results are provided to show the superiority of the proposed method in terms of estimation accuracy, temporal resolution and computational complexity.

This paper introduces a novel approach in pavement inspection by applying high-resolution imaging techniques onto a Constant Offset Sliding Bistatic Ground-Based Synthetic Aperture Radar system operated in forward-scattering mode. The performance of this configuration, known for its minimal complexity, is significantly improved by incorporating the spectral analysis method. The integration enhances vertical discrimination and proves effective in complex scenarios with fewer actual sources than anticipated. This approach represents a alternative solution in non-destructive road infrastructure analysis, offering a powerful tool for precise pavement condition monitoring.

Orthogonal Time Frequency Space (OTFS) modulation can effectively combat the Doppler effects and exploit the time-frequency resources. The sparsity of the OTFS channel matrix in delay-Doppler domain plays a crucial role in message passing detection algorithms. This paper presents a hybrid detection approach that combines Gaussian mixture model (GMM) with Gaussian approximation (GS). According to the path gain, GMM is employed to deal with significant paths and GS for less significant paths. The proposed hybrid method achieves a balance between computational complexity and performance by indirectly computing messages and introducing a damping factor. Simulation results show that the proposed method exhibits noteworthy Bit Error Rate (BER) performance and reduces the computational complexity associated with GMM.

In cases involving a rough interface, the echo frequency behavior of ultrawideband GPR approximates a non-linear Gaussian function. This characteristic leads to poor robustness and high computational complexity in existing modified MUSIC (Mod-MUSIC) methods. To address this challenge, this letter proposes an enhanced modified MUSIC (E-Mod-MUSIC) approach to mitigate the interference of false peaks, thus enhancing robustness. Furthermore, to reduce computational complexity while maintaining high estimation accuracy comparable to dense uniform frequency sampling structures, a distributed co-prime frequency sampling structure is employed with E-Mod-MUSIC. Numerical simulations with different scenarios show the effectiveness of the proposed method.

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli ($\delta $-GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the$\delta $-GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.

Time-delay estimation (TDE) for thin top layers of asphalt pavement is a challenging task due to the limited resolution of ground penetrating radar (GPR) as well as small permittivity difference between top layers. Echoes backscattered from the interfaces of top layers with similar permittivity have usually much smaller amplitudes compared with other echoes, which can be called weak signals. The weak backscattered echoes are usually too sensitive to the noise and other strong echoes that current signal processing approaches (subspace-based methods and compressed sensing based methods) might have false estimation results even failures without proper processing of them. Therefore, in this paper, an enhanced orthogonal matching pursuit (OMP) method is proposed to deal with weak signals resulting from similar permittivity of adjacent asphalt layers. Based on the orthogonality between signal and noise subspaces, we firstly apply the truncated singular value decomposition (SVD) on the received signals, in order to reduce the noise impact. Secondly, we build an orthogonal matrix to the mode matrix of the pre-estimated strong backscattered echoes, and map it to the overcomplete dictionary matrix, such that the influence of the residual of the strong backscattered echoes can be reduced. Finally, the time-delays of backscattered echoes and layer thicknesses are estimated. Compared with conventional approaches, the proposed method is more suitable for TDE in thin asphalt pavement detection. The accuracy of the proposed method is validated by both numerical and experimental data.

This paper investigates and compares different imaging configurations for roadway inspection. Ground-penetrating radar is generally used to detect and characterize the vertical structure of the underground medium, whereas Off-Nadir Synthetic Aperture Radar can work both in back-scattering and forward-scattering modes. Back-scattering mode provides a capacity of discrimination in azimuth and range directions, while forward-scattering mode converts range resolution into vertical resolution. Vertical sampling is then used to realize tomography. 3D-focusing imaging of roadways is thus shown to be able to adequately and efficiently evaluate the underground structure.

Pavement survey is one of the most important applications for ground penetrating radar (GPR) in civil engineering. In the case of centimeter scale of GPR waves, the influence of interface roughness cannot be neglected and should be taken into account in the radar data model. The objective of this article is to estimate the time-delay in the presence of interface roughness by GPR. Using the property of noncircular signals, we propose a modified orthogonal matching pursuit method to estimate the pavement parameters for both overlapped and nonoverlapped echoes. Compared with subspace-based methods in coherent scenarios, the proposed method can estimate the time delays of backscattered echoes without applying the cumbersome interpolation and spatial smoothing procedures, which are more practical in real applications. The performance of the proposed method is tested on both simulated and experimental data. The estimation results show the good performance of the proposed method.

Higher order cumulants based direction of arrival (DOA) estimation has attracted increasing attention since several years. However, higher order cumulants for the active DOA estimation has not been thoroughly studied. In this paper, we investigate the fourth order cumulant for active DOA estimation. Our work can lead to higher degrees of freedom without modifying the sparse array geometry. This is achieved by constructing a fourth order difference coarray of a sum coarray. The fourth order difference coarray will be shown to be able to further increase the effective array aperture in the active DOA estimation. We also investigate the property of the fourth order difference coarray by considering coprime arrays geometry. Simulation results verify the improved performance of the proposed strategy.

In active direction of arrivals (DOA) sensing, the multiple-input multiple-output (MIMO) radar can lead to more degrees of freedom (DOFs). The DOFs can be further increased when the fourth order cumulants are used in MIMO. However, most array geometries are not designed for fourth order cumulants based MIMO and this array geometry optimization problem is still an open issue. To have a simple guideline for this optimization problem, we reformulate it into two separate problems, which are a fourth order sum coarray optimization problem and a second order difference coarray problem. The fourth order sum coarray optimization is realized by solving a postage stamp problem and a virtual nested array can be obtained. The number of elements in this virtual nested array can be significantly increased with only a few physical sensors. The corresponding inter-element spacing of virtual nested array is expanded simultaneously. Then the second order difference coarray is adopted to the virtual nested array to further increase the DOFs. Numerical results validate the high DOFs achieved by our proposition.

Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.

Ground Penetrating Radar (GPR) is a widely used tool in the management and monitoring of pavement structures. In this paper, we focus on the detection of thin inter-layer debondings between the hot mix asphalt layers of pavement structures. A Stepped-Frequency Radar (SFR) associated with a Support Vector Regression based Linear Prediction (LP-SVR) method is used to detect thin debondings. The performance of SFR with the LP-SVR method is analyzed according to various used frequency bandwidths on the experimental data.

In civil engineering, roadway structure evaluation is an important application which can be carried out by ground penetrating radar. This paper focuses on the estimation of the time delay and interface roughness of civil engineering structure, like pavements. The influence of interface roughness is taken into account in the signal model. Therefore, we propose a new method which allows to efficiently estimate the time delay and interface roughness. Like in [1], the modified MUSIC is used for time delay estimation. In interface roughness estimation, we propose a modified orthogonal propagator method (OPM) to estimate the interface roughness with estimated time delay. While in [1], maximum likelihood method is applied, which needs multiple dimensional search. The proposed method is tested on the experimental data. The experimental results show the performance of the proposed method.

In the field of civil engineering, ground penetrating radar (GPR) is a highly efficient nondestructive testing tool for sustainable management of pavement infrastructures. GPR allows to evaluate the structure of the roadway over large distances (with contactless configurations) and to detect significant subsurface defects. This letter presents a new method to detect thin debondings within pavement structures with the step-frequency GPR. The proposed method enables us to carry out the detection with only a small number of frequency samples and A-scans. It is based on the linear prediction and support vector regression theories. Two experimental results show its effectiveness.

The development of methods and tools for the road infrastructure sustainable management is a research challenge, especially for nondestructive testing methods. This letter focuses on the estimation of the thickness of civil engineering structures, like pavements, and more precisely, the time delay and interface roughness. We propose a modified Min-Norm algorithm which allows efficiently estimating the time delay and interface roughness without the eigenvalue decomposition. Therefore, it has a smaller computational load compared with subspace-based methods. The experimental results show the efficiency of the proposed algorithm.

In this paper, we propose a new direction of arrival (DoA) finding algorithm based on the multiple signal classification (MUSIC) method by exploiting the conjugate symmetry of M-ary phase-shift keying (MPSK) signals. According to the conjugate symmetry, we classify the received vectors into subsets. In each subset, an extended model is constructed to estimate the DoA of incoming signals. The final DoA estimates are obtained by synthesizing the results over all subsets. Simulation results show that our proposed method outperforms the existing ones. In this paper, the proposed method is illustrated with MPSK signals, it can be extended straightforwardly to M-ary quadrature amplitude modulation (MQAM) signals or the mixed cases of both MPSK and MQAM signals.

Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets.

Ground-penetrating radars (GPR) are widely used in media parameters' estimation and targets' localization. This paper focuses on time-delay estimation (TDE) using the GPR signal, which contains important information about the probed media structure. However, TDE tends to be a challenging task in GPR applications, in the scenarios of overlapping, coherent signals and limited snapshots. Forward-backward linear prediction (FBLP) is a high time-resolution method, which is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. Therefore, we propose to combine the theory of FBLP and SVR together to enhance the robustness of TDE in the case of coherent, overlapping signals as well as limited snapshots. The proposed method is tested with both numerical and experimental data. Both the results demonstrate the effectiveness of the proposed method.

In civil engineering, ground penetrating radar is a common technique for evaluating the structure and quality of road pavement. This paper focuses on the estimation of the time delay and interface roughness of civil engineering structure, like pavements. The influence of interface roughness is taken into account in the signal model. A modified estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm combined with an interpolated spatial smoothing technique is proposed. It allows us to jointly and efficiently estimate the time delay and interface roughness by ultrawideband radar (the upper frequency up to 8-10 GHz) with low computational complexity. The proposed algorithm is tested on both numerical and experimental data. Simulation and experimental results show the good performance of the proposed algorithm.

Most of the existing near‐field sources localisation methods are based on an approximated model. Making use of such an approximation brings degradation in the estimation accuracy. In this study, the authors propose a correction method to mitigate this problem and improve the estimation performance of the approximated model based methods. Simulation results show that the proposed technique can significantly improve the performance of the classical approximated model based near‐field sources localisation methods.

In this letter, a new method is proposed to enhance the ground-penetrating radar (GPR) signal for time-delay estimation in a low signal-to-noise ratio. It is based on a subspace method and a clustering technique. The proposed method makes it possible to improve the estimation accuracy in a noisy context. It is used with a compressive sensing method to estimate the time delay of layered media backscattered echoes coming from the GPR signal. Several simulations and an experiment are presented to show the effectiveness of signal enhancement.

In the electromagnetic field, radar is widely used to measure or estimate the media parameters or to detect targets through obstructions. For horizontally stratified media, the layer thickness can be deduced from the time delays of backscattered echoes and the dielectric constants. The high-resolution method estimation of signal parameters via rotation invariance techniques (ESPRIT) has been proposed for time-delay estimation. In practice with a radar, backscattered echoes are correlated. In order to apply the ESPRIT method, in this letter, we propose to use two adaptive improved spatial smoothing techniques with the propagator method for fighting against the correlation between the echoes. The proposed solution does not use any approximation. Numerical examples are provided to show the performance of the algorithm.

In this paper, a new near‐field source localisation method based on signal reconstruction and clustering approach is proposed. The new method makes full use of the advantages of signal reconstruction, reconstructs the parameters related to the direction of arrival (DOA) and range of sources in cumulant domain separately, and pairs them with clustering technique. This would lead to higher resolution and better accuracy than traditional methods. By estimating the parameters separately, the authors avoid constructing a huge overcomplete basis, and reduce the computational burden. Simulation results show the effectiveness of the proposed method.

Widely linear processing (WLP) can provide attractive performance improvement in wireless communication systems. However, performance improvement brought by WLP is mainly achieved by exploiting signal's non-circularity, which leads to the fact that existing works on WLP are mainly confined to the cases related to non circular signals or improper signals with imbalanced I/Q components. In this paper, WLP for circular signals, such as PSK signals, is investigated by exploiting the hidden properties of PSK signals. The hidden properties of PSK signals are firstly studied, and a unified mathematical model is derived to describe the hidden properties of PSK signals. Furthermore, a widely linear sphere decoding (WLSD) algorithm exploiting PSK signals' hidden properties is proposed for MIMO systems. Compared to traditional sphere decoding (SD), WLSD has little performance loss, and it transforms the traditional SD searching for the true transmitted vector into the shrunk searching for the corresponding rotation vector, the candidate rotation vectors of WLSD are only (1/2)NTof SD candidate vectors. Simulation results show that the proposed algorithm can achieve quasi-optimal BER performance, while the computational complexity is significantly reduced by more than a half compared with Schnorr-Euchner sphere decoder.

In civil engineering, ground penetrating radar is widely used for road pavement surveys. In this paper, the influence of interface roughness is taken into account. We propose a Modified ESPRIT algorithm for the data processing of radar signal, in order to jointly estimate the time delay and the interface roughness. The algorithm is tested on simulated data from the propagation inside layer expansion (PILE) method. Numerical examples are provided to demonstrate the performance of the algorithm.

In the field of civil engineering, sounding the pavement layers is classically performed using standard ground-penetrating radar, whose vertical resolution is bandwidth dependent. The layer thicknesses are deduced from both the time delays of backscattered echoes and the permittivity of layers. In contrast with conventional spectral analysis approaches, this letter focuses on one of the machine learning algorithms, namely, the support vector machine, to perform time delay estimation and dielectric constant estimation of the medium from backscattered radar signals. This letter shows the super time resolution capability of such technique to resolve overlapping and fully correlated echoes within the context of thin pavement layer testing.

ABSTRACT An approach of superimposed training (ST)‐aided time‐varying (TV) channel estimation for multiple‐input multiple‐output orthogonal frequency division multiplexing systems is presented. By modeling the TV channel with the truncated discrete basis expansion model, a two‐step approach is adopted to estimate the TV channel. In addition, the mean square error (MSE) of the proposed channel estimation is analyzed, and its closed‐form expression is derived, which is a function of the data‐to‐ST power ratio. Using the developed channel MSE, we case the problem of ST power‐allocation by minimizing the lower bound on the average channel capacity. To enhance the performance of channel estimation, a low‐complexity decision feedback mechanism is introduced to iteratively mitigate the unknown data interference. Numerical results verify the performances of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

Traffic congestion has led to research on how to use the existing infrastructure more efficiently. The experimental platform SAROT was constructed to allow traffic analysis but also to test new algorithms for traffic modeling and management. Origin-Destination (OD) Matrix is one of the most important traffic information for Intelligent Traffic System. By using the experimental platform SAROT, we want to test algorithms for OD Matrix. To obtain OD matrix, we need to get useful and accurate data by tracking vehicles. Two approaches for tracking vehicles by using Inductive Loop Detector (ILD) are used. Both approaches are computed on real traffic data set and compared. We use threshold decision to improve the correct matching rate for tracking vehicles. We propose a new methodology by associating two algorithms for increasing the correct matching rate. The new methodology was used to perform target OD matrix.

In this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general. Several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI, the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region.

Sphere decoder (SD) has variable complexity, and the traditional fixed-complexity sphere decoder (FSD) is not applicable for rank-deficient (NT> NR) multiple input multiple output (MIMO) systems (i.e. the down link detection in MIMO systems, or systems with highly correlated MIMO channels). To overcome these difficulties, in this paper, robust fixed-complexity sphere decoders (RFSD-s) based on new preprocessing algorithms are proposed for rank-deficient MIMO systems. With respect to the cases without and with information on the level of noise, RFSD using zero-forcing technique (RFSD-ZF) and FSD using minimum mean square error technique (FSD-MMSE) are proposed respectively. To reduce the computational complexity of RFSD-ZF, a simplified RFSD-ZF (SRFSD-ZF) with little performance loss is also introduced, the theoretical proof is given to support the feasibility of SRFSD-ZF. Simulation results show that, besides better performance than the traditional FSD, the proposed techniques are robust to the configuration of MIMO antennas (both NT≤ NRand NT> NR), which is another advantage over the traditional FSD.

The purpose of this paper is to develop an algorithm achieving the performance of fixed-complexity decoder (FSD) with much lower complexity than FSD. An adaptive expansion strategy (AES) is proposed for MMSE Ordered Successive Interference Canceling (MMSE-OSIC). The expansion degree varies according to the channel condition, especially, partial expansion (PE) is proposed in this paper as a transition between Single expansion (SE) and full expansion (FE). Widely linear processing, which is usually confined to non circular signals, is extended to circular signals in PE. Simulation shows that the proposed AES can significantly reduce the complexity with little performance loss, compared with FSD.

In this paper, RFSD (Robust Fixed-complexity Sphere Decoder) based on a new preprocessing algorithm is proposed for MIMO (Multiple Input Multiple Output) detection. Unlike the original FSD (Fixed-complexity Sphere Decoder) ordering which determines the FE (Full expansion) stage by choosing the signal component with greatest post-processing noise amplification on itself, the proposed RFSD determines the FE stage by selecting the signal component with the smallest postprocessing impact on the undetected signal component in following SE (Single Expansion) stage. Besides better performance than the original FSD ordering, RFSD is robust to the configuration of MIMO antennas (both NT≤ = NRand NT>; NR), which is another advantage over original FSD ordering.

A wide-band near-field source localization method is presented in this paper. Based on a pre-estimated source location, we form a focusing matrix which is able to compensate the wavefront distortion (with respect to far-field wavefront) due to near-field propagation and frequency dependent phase shift simultaneously. The focused covariance matrix has been proved to have a partially far-field narrow band structure, which allows us to estimate the bearings of the sources by the well studied far-field DOA estimators. The range estimation is then carried out via peak searching of the 1D MUSIC spectral with the estimated bearings. The performance of the algorithm is tested by simulations.

In civil engineering, usually the methods used to estimate the thickness of thin pavements consider flat interfaces for simplification. In this paper, the roughness of the surfaces is taken into account. First, the amplitudes of the first two echoes from the rough thin pavement are calculated from a rigorous electromagnetic method, the PILE method. A comparison is then made with the flat interface case, and their differences in the electromagnetic backscattering are highlighted. Eventually, the influence of the pavement roughness on the pavement thickness estimation is investigated by using the Maximum Likelihood Method.

The purpose of this paper is to evaluate the spectral regrowth at the output of a power amplifier due to the nonlinearities of this component. An analytical expression of the power spectral density of the signal at the power amplifier output, corresponding to the general case of a multicarrier input signal, is derived. From this equation, we can easily find the close form of the power spectral density for monocarrier signals. Simulations are then carried out to confirm the results of the derived expressions.

In this paper, a new subspace-based algorithm for parametric estimation of angular parameters of multiple incoherently distributed sources is proposed. This method consists of using the subspace principle without any eigendecomposition of the covariance matrix, so that it does not require the knowledge of the effective dimension of the pseudosignal subspace and then the major difficulty of the existing subspace estimators can be avoided. The proposed idea relies on the use of the property of the inverse of the covariance matrix to exploit approximately the orthogonality property between column vectors of the noise-free covariance matrix and the sample pseudo-noise subspace. Simulation results show that, compared with other known methods, the proposed algorithm exhibits a better estimation performance.

In the field of civil engineering, sounding the top layer of carriageways, i.e., the pavement layer, is classically performed using standard ground-penetrating radar (GPR), whose resolution is bandwidth dependent. The layer thickness is deduced from both the time delays of backscattered echoes and the known dielectric constant of the medium. This paper focuses on superresolution and high-resolution techniques, which serve to improve the time resolution of GPR signals, and presents a parametric technique and five subspace methods, namely, estimation of signal parameters via rotational invariance techniques (ESPRIT), multiple-signal classification (MUSIC) algorithm, Min-Norm, and their polynomial versions root-MUSIC and root-Min-Norm. The performance of these algorithms will be compared in terms of resolution power as well as root-mean-square error on the estimated thickness. The paper also presents the results of computer tests and radar measurements in the far field.

The extended invariance principle (EXIP) has been applied to the structured covariance estimation of a zero mean Gaussian vector; the resulting method was named COMET (covariance matching estimation techniques). This technique has been recently used for estimating separately and efficiently the direction of arrival (DOA) and angular spread of a scattered source. Unfortunately this new technique presents an ambiguity that limits its utilization in practice. We show in this paper the existence and the origin of this ambiguity and we propose a solution to eliminate this problem without introducing bias. Our approach consists first to add a constraint to the original cost function, and then to replace the constrained problem by an unconstrained problem by using the penalty function method.

In this paper, we propose a new Root-MUSIC-like direction finding algorithm that exploits cyclostationarity in order to improve the direction-of-arrival estimation. The proposed cyclic method is signal selective, it allows us to increase the resolution power and the noise robustness significantly, and it is also able to handle more sources than the number of sensors. Computer simulations are used to show the performance of the algorithm.

In this paper, we propose a direction finding beamformer algorithm that exploits cyclostationarity (or periodic correlation). This new adaptive algorithm shows very attractive estimation performance over conventional beamforming methods, as depicted by simulation results.

Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. By exploiting cyclostationarity, the signal direction-of-arrival estimation can be significantly improved. In this paper, we propose a new MUSIC-like [1] direction finding algorithm that exploits cyclostationarity. Our method can be considered as an extension of the Cyclic MUSIC algorithm [2].

In cellular mobile radio systems, transmit beamforming can be used to reduce the interference in the downlink. In a time division duplex (TDD) system with sufficiently short duplex period, the uplink channel estimation can be used to design the downlink beamformer. The purpose of this paper is to analyse the sensitivity of several transmit techniques to the channel variations between uplink and downlink time-slots. This analysis is done using experimental data obtained from real environments.

We present in this paper a new direction finding algorithm for non circular sources that is based on polynomial rooting. Due to the non circularity characteristics of the impinging sources, the proposed method is able to handle more sources than sensors. By using a polynomial rooting instead of a searching technique the method is limited to linear uniformly spaced arrays. However, polynomial rooting reduces significantly computation cost and enhances resolution power. Computer simulations are used to show the performance of the algorithm.

We propose a new high resolution method with polarization diversity for the characterization of the scattering centers of a radar target, using a stepped-frequency radar system. The proposed method is based on the polynomial rooting technique. It allows one to optimally use the information contained in the polarization of the received wave while keeping the calculation cost moderate. Moreover, it can estimate the range and the polarization parameters of each scattering center in only one step. Simulation results are presented to show the performance of the algorithm.

An original eigenstructure-based method for direction finding in presence of sensor gain and phase uncertainties is presented. The method has been developed in order to estimate the directions of arrival of non-circular radiating sources for any geometrical array configuration. The gain and phase of each sensor are also provided by this technique. We show that this method works even in the case where the number of sources is larger than the number of sensors. The proposed procedure gives very good results as we show through two demonstrative computer simulations.