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In this paper, to track maneuvering unmanned surface vehicles (USVs) in scenarios with clutter, we propose a novel method based on the probabilistic data association (PDA) algorithm and Rao-Blackwellized particle filter (RBPF) algorithm, and we further improve the tracking performance by Max-Q criterion-based waveform selection. This work develops a maneuvering target model in the context of clutter, integrating linear and nonlinear states as well as observations with false alarms. In order to jointly tackle the mixed-state tracking problem, the PDA algorithm is integrated into the RBPF framework. This allows it to be used with the complex nonlinear and linear hybrid system and helps to minimize the state dimensions of conventional particle filtering (PF). Additionally, by utilizing Q-learning principles, we provide a Max-Q-based criterion to select the waveform parameters, which guarantees low measurement errors and efficiently handles measurement uncertainties. Our simulation results show that the PDA-RBPF algorithm, which has a more appropriate tracking mechanism, produces results that are more accurate than those of the EKF or PF algorithms alone. Furthermore, the RMSE derived by the Max-Q-based criterion is smaller and more robust than that of other selection methods, as well as yielding a fixed waveform. Our proposed mechanism, which combines the concepts of PDA-RBPF and Max-Q waveform selection, performs well in target tracking tasks and exhibits relatively good performance over some existing ones. [ABSTRACT FROM AUTHOR]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD). [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array. [ABSTRACT FROM AUTHOR]

Small‐array high‐frequency surface wave radar (HFSWR) is widely used to monitor maritime targets as it can be used to save on‐land resources. In small‐array HFSWR systems, the main lobe of the receiving angle spectrum is significantly broadened. In complex clutter backgrounds, an extremely wide beam severely influences clutter suppression performance; consequently, targets with a low signal‐to‐clutter ratio (SCR) may be eliminated, or the angle may be barely estimated. This study proposes a space‐time adaptive processing (STAP) algorithm based on hyper beamforming (HBF) to improve the clutter suppression performance of small‐array HFSWR. In addition, HBF can obtain more independent identical distributed training samples than the conventional beamforming; thus, the STAP algorithm can extract the clutter information with high accuracy in the covariance matrix estimation. Moreover, this study combines an efficient STAP algorithm with a joint domain localised (JDL) algorithm to improve clutter suppression. Based on the experimental results, the proposed HBF‐JDL algorithm performs satisfactorily and significantly improves the SCR. Moreover, HBF‐JDL is still applicable at lower SCRs of the target compared with JDL. [ABSTRACT FROM AUTHOR]

This letter proposes an efficient tensor model for Direction of Departure (DOD) and Direction of Arrival (DOA) estimation based on bistatic nested Multiple‐Input Multiple‐Output (MIMO) radar. Tensor offers a robust solution in characterizing multi‐dimensional data structures, ensuring data integrity, and has demonstrated remarkable effectiveness in the realm of multi‐dimensional parameter estimation. However, the complexity of traditional methods for constructing tensors is a major obstacle to practical applications. In response, this research introduces a novel approach that utilizes an iterative two‐dimensional Toeplitz matrix reconstruction method to rapidly derive expanded virtual covariance matrices prior to tensor construction. This streamlines the algorithm and improves efficiency. Numerical simulations have verified the superiority of this algorithm. [ABSTRACT FROM AUTHOR]

In the case of multipath propagation for passive bistatic radar (PBR) using uncooperative frequency agile-phased array radar as an illuminator, a new direction-finding method is proposed to deal with the scenario where the coherent and uncorrelated signals are closely spaced or in the same direction. Firstly, spatial difference technique is used to eliminate uncorrelated signals. Then, in order to avoid the cross-terms effect and improve the resolution of coherent signal, the iterative adaptive method (IAA) is adopted for the rearranged spatial difference matrix. Finally, the direction of arrival (DOA) of the target signal is obtained by the reconstruction of the interference-plus-noise covariance matrix. Compared with previous studies, this method has better performance in the case of low signal-to-noise ratio (SNR) and limited number of snapshots. [ABSTRACT FROM AUTHOR]

This study developed a novel S-band radar system for planar location applications. High-resolution range imaging and target angle estimation were achieved by using a stepped frequency continuous wave (SFCW) signal and single input multiple output (SIMO) architecture with a linear sparse array layout, respectively. An improved time-frequency method was utilized to link the independent range profile and angle spectrum results to obtain the plane positions of the targets. The radar hardware was composed of the antenna array with one transmit element and five receive elements, an RF transceiver, and a signal processing component. Under the proposed waveform parameters and signal processing scheme, a 16-ms process cycle, 0.3-m ranging error, and 0.4° angle estimation error for target positioning were achieved in field tests. These results demonstrate the effectiveness and advantages of the proposed radar system. [ABSTRACT FROM AUTHOR]

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement. [ABSTRACT FROM AUTHOR]

In this paper, the design of low probability of intercept (LPI) radar waveforms considers not only the performance of passive interception systems (PISs), but also radar detection and resolution performance. Waveform design is an important considerations for the LPI ability of radar. Since information theory has a powerful performance-bound description ability from the perspective of information flow, LPI waveforms are designed in this paper within the constraints of the detection performance metrics of radar and PISs, both of which are measured by the Kullback–Leibler divergence, and the resolution performance metric, which is measured by joint entropy. The designed optimization model of LPI waveforms can be solved using the sequential quadratic programming (SQP) method. Simulation results verify that the designed LPI waveforms not only have satisfactory target-detecting and resolution performance, but also have a superior low interception performance against PISs. [ABSTRACT FROM AUTHOR]

In this article, semidefinite programming (SDP) solutions are proposed for the elliptic localization problem in asynchronous radar networks, where the transmitters are subject to clock offsets. Relaxing the nonconvex problem into different convex forms, we design tight SDP (TSDP) and global SDP (GSDP) solutions for this problem. The TSDP solution includes several SDP cones to solve the problem, and the correlation is not considered. Hence, the TSDP solution performs poorly in the presence of correlated noise. We further put forward the GSDP solution to improve the performance by introducing a new GSDP form, which includes only one SDP cone to handle the correlated noise. We also theoretically prove that the GSDP problem is tight enough so that its performance is able to sufficiently approach the Cramér–Rao lower bound (CRLB) accuracy. The simulated results show that the TSDP solution can provide the comparable performance with the GSDP in the absence of related noise. The performance of the GSDP solution can almost attain the CRLB accuracy using less receivers. [ABSTRACT FROM AUTHOR]

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]

Sparse frequency agile linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) radar can improve anti-interference ability as well as reduce the sampling rate of the radar system. However, the frequency agility makes it difficult to realize coherent processing by using the fast Fourier transform. To overcome this problem, a signal processing scheme for the sparse frequency agile LFM-OFDM radar is proposed to obtain the high range and Doppler resolution in this study. The subcarriers are synthesised to an LFM signal in the time domain at first. Then, within the framework of the compressed sensing theory, an improved orthogonal matching pursuit algorithm is proposed to obtain the high-resolution range-velocity profile while reducing the computation complexity. Finally, several numerical simulations are provided to verify the effectiveness of the proposed method for moving targets. [ABSTRACT FROM AUTHOR]

The height measurement algorithm of metre wave multiple input multiple output (MIMO) radar based on uniform linear array (ULA) has some problems, such as a sharp decline of angle measurement accuracy of ultra‐low altitude targets and large fluctuation of height measurement with the change of elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, coprime array (CPA) is applied to the height measurement of monostatic metre wave MIMO radar. In this paper, the signal model of monostatic CPA metre wave MIMO radar is established under the condition of flat ground, a height measurement method using physical array is proposed based on the derivation that it is infeasible to use virtual array method for height measurement, it fills the gap of low elevation estimation method based on sparse array metre wave MIMO radar. The proposed method calculates the echo data according to the characteristics of the steering vector of CPA, then obtains the accurate low elevation by using the generalised multiple signal classification algorithm, and finally the target height is obtained by using the geometric relationship. Simulation experiments compare three typical CPAs with ULA using equal array elements, and verify the superiority of height measurement performance of monostatic metre wave MIMO radar based on CPA and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a method to address the problem of the joint direction of departure (DOD) and direction of arrival (DOA) estimation with augmented coprime array (CPA) bistatic multiple input multiple output (MIMO) radar using interpolating sensors. At first, we deduce the regular pattern of hole positions in the virtual array and interpolate a small number of sensors to augmented CPA to form a partially contiguous virtual array. Then, we use the diversity smoothing algorithm to reconstruct the Toeplitz matrix to obtain a spatial smoothing matrix. Finally, we combine the RD-MUSIC algorithm with the spatial smoothing matrix to estimate the spatial spectrum and achieve automatic matching of DODs and DOAs for the targets. Simulation results clearly illustrate the superiority of the method. [ABSTRACT FROM AUTHOR]

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression. [ABSTRACT FROM AUTHOR]

A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and the location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of joint direction of departure (DoD) and direction of arrival (DoA) estimation with bistatic MIMO radar. Specifically, a weaved co-prime array (WCA) bistatic MIMO (WCA-bMIMO) radar is proposed to produce a hole-free sum-difference co-array, which enables to offer significantly increased degrees of freedom (DoFs) and enlarged array aperture. Moreover, the two-dimensional spatial smoothing method is introduced to construct a smoothed received signal, which helps to tackle the rank deduction problem rooted in the equivalent one-snapshot received signal from the sum-difference co-array. Subsequently, the joint DoD and DoA estimates can be obtained by extracting the three-way tensor model. Furthermore, co-array Cramer-Rao Bound (CRB) for the bistatic co-prime MIMO radar is provided. Finally, the superiorities of the proposed bistatic co-prime MIMO radar in accuracy and resolution are proved by numerical simulation results. [ABSTRACT FROM AUTHOR]

A low complexity unitary estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for angle estimation in bistatic multiple-input-multiple-output (MIMO) radar. The devised algorithm only requires calculating two submatrices covariance matrix, which reduces the computation cost in comparison with subspace methods. Moreover, the signal subspace can be efficiently acquired by exploiting the NystrÖm method, which only needs O M N K 2 flops. Thus, the presented algorithm has an essentially diminished computational effort, especially useful when K ≪ M N , while it can achieve efficient angle estimation accuracy as well as the existing algorithms. Several theoretical analysis and simulation results are provided to demonstrate the usefulness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor (EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS–MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS–MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generally ground clutter has serious range dependence and range ambiguity in the forward-looking array airborne radar system under a high pulse repetition frequency (PRF) system. In this work, a range ambiguous clutter suppression method based on transmitting beam main lobe correction for the FDA MIMO radar is proposed. Firstly, this method uses the designed transmission pulse weights to compensate for the initial phases formed by the accumulation of frequency increments between pulses, so as to solve the problem that the main lobe high gain illuminated angle is inconsistent between the MIMO equivalent transmitting beampattern pulses synthesized at the receiving in the traditional FDA MIMO system. Secondly, by restoring and compensating received echoes, the influence of the main lobe correction weights of the transmitting signal is eliminated. Finally, through the characteristics of transmitter correction compensation to distinguish different range ambiguous clutters, the echoes from the range ambiguous areas can be discretely distributed in the power spectrum while preserving the signal echoes in the observation area. Thus range ambiguous clutter suppression is realized. The simulation results show that the proposed method can improve the clutter suppression performance compared with the MIMO and linear frequency increment FDA radar. [ABSTRACT FROM AUTHOR]

Radar technology plays a vital role in enhancing surveillance of the battlefields and physical borders, disease detection, and weather prediction. However, there exist certain challenges like detecting fast-moving targets. When targets are moving at a significantly high speed, it is not easy to detect them compared to static and slow-moving targets, which are easily detected. In this regard, the research community has, over the years, tried to improve radar technology to detect fast moving targets in presence of Doppler noise. In this paper, various binary codes have been proposed to minimize the noise peaks below the radar threshold limit to create multiple windows that enable precise information of the moving targets. These sequences also have good autocorrelation attributes to get a higher range resolution and compression ratio. Validation of the proposed method is performed using Matlab simulations. Results show that the noise amplitudes are reduced to 0.2 dB well below the prescribed threshold. [ABSTRACT FROM AUTHOR]

This article addresses the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation with nested bistatic multiple input multiple output (MIMO) radar using tensor decomposition. We first employ the two-level nested transmit and receive arrays to develop the sum-difference coarray for constructing the Toeplitz and spatial smoothing matrices. We then generalize the three-way tensor model from DOD and DOA dimensions, and derive the optimized tensor by maximizing the number of detectable targets, where the existing COMFAC technique is exploited for angle estimation. We show that the proposed method can identify more targets and achieve better performance by enforcing the three-way structure information compared with the subspace-based algorithms. We also show that the conventional tensor model is just a special case. Finally, we derive the coarray Cramér–Rao Bound (CRB) for the nested MIMO radar, and also conduct a study for the conditions under which the CRB exists. Numerical simulations are provided to validate the theoretical analysis and demonstrate the performance improvement. [ABSTRACT FROM AUTHOR]

At present, multiple input multiple output radars offer accurate target detection and better target parameter estimation with higher resolution in high‐speed wireless communication systems. This study focuses primarily on power allocation to improve the performance of radars owing to the sparsity of targets in the spatial velocity domain. First, the radars are clustered using the kernel fuzzy C‐means algorithm. Next, cooperative and noncooperative clusters are extracted based on the distance measured using the kernel fuzzy C‐means algorithm. The power is allocated to cooperative clusters using the Pareto optimality particle swarm optimization algorithm. In addition, the Nash equilibrium particle swarm optimization algorithm is used for allocating power in the noncooperative clusters. The process of allocating power to cooperative and noncooperative clusters reduces the overall transmission power of the radars. In the experimental section, the proposed method obtained the power consumption of 0.014 to 0.0119 at K = 2, M = 3 and K = 2, M = 3, which is better compared to the existing methodologies—generalized Nash game and cooperative and noncooperative game theory. [ABSTRACT FROM AUTHOR]

Next-generation radar sensors require imaging capabilities with high angular resolution. As for a single sensor, the aperture, and thus the achievable resolution, is limited due to the constraints of the front end, radar networks consisting of multiple sensors are a possible solution. However, their incoherency usually makes joint angle estimation impossible. This article presents a network concept consisting of an orthogonal frequency-division multiplexing (OFDM) radar and repeater elements, which receive the reflections from targets and retransmit them back to the radar. Thereby, any frequency conversion from radio frequency to baseband and vice versa is omitted such that the signal remains coherent to the initial transmit signal. To distinguish the bistatic signal transmitted by the repeater from the monostatic one of the OFDM radar, the orthogonal subcarrier structure of OFDM waveforms is exploited by combining a sparse radar transmit signal with a low-frequency modulation in the repeater. This allows to evaluate the bistatic signals at the radar with standard multiple-input–multiple-output (MIMO)-OFDM signal processing, leading to separate range–Doppler images for each virtual channel. Finally, it is shown that this method offers a coherent angular estimation based on the extended aperture of the network. For this purpose, a method to establish phase coherency by a reconstruction of the modulation phase is presented. The network concept is proved with measurements at 77 GHz. [ABSTRACT FROM AUTHOR]

This paper considers utilizing radar multipath returns to locate a target hidden behind a corner. A novel target localization algorithm based on grid matching is proposed for non-line of sight (NLOS) environment. The algorithm first establishes a multipath propagation model based on real data from an L-band ultra-wideband (UWB) radar. Then, it calculates the times of arrival (TOAs) of each grid based on the multipath propagation model and matches the grid which is closest to the measured TOAs of round-trip multipath returns. Both simulation and real-data experiment results validate the effectiveness of the multipath model and the proposed localization algorithm. [ABSTRACT FROM AUTHOR]

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there still remain considerable challenges in mitigating target-dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this article, we focus on mitigating wall effects using a machine learning-based solution—denoising autoencoders—that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with 2-D array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with 2-D array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images. [ABSTRACT FROM AUTHOR]

This article deals with the problem of target detection in a passive multiple-input multiple-output (MIMO) radar network involving noncooperative illuminators of opportunity (IOs) and geographically dispersed multichannel receivers. The signal model considers the direct-path interference (DPI). We employ the Rao test to solve the problem and derive a closed-form detector. Then, by considering the properties of the ambiguity functions of most IO waveforms, an approximated version of the detector with a simpler form is derived. Two detectors in the case of an active MIMO network are also provided as the upper bounds of the passive situations. Moreover, considering target echo signal fluctuation, we have analyzed the performance of the detectors in terms of probability of miss-detection when being used in a multistatic configuration and have specified the conditions under which the full diversity gain is achieved. Numerical results verify the effectiveness of the proposed detectors and demonstrate that the proposed detectors have performance very close to the active situation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the code design problem of improving the detection performance of a moving target in the presence of nonhomogeneous signal-dependent clutter for moving target-detecting (MTD) radar systems. The optimization metric is constructed based on the signal to clutter and noise ratio (SCNR) of interpulse matched filtering. Under the frameworks of cyclic and majorization-minimization algorithms, we propose a novel algorithm, named CMMCODE, to tackle the code design optimization problem in the case of unknown precise target Doppler information and nonhomogeneous clutter. In the white-noise case, the simplified algorithm is also given based on CMMCODE algorithm. The presented algorithm is computationally efficient and convergent. Numerical examples show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a joint two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) strictly noncircular (NC) unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) method is proposed for an L-shaped bistatic multiple input multiple output (MIMO) radar. In the case that the incident signals are NC signals, we first utilize the received data vector and its conjugate counterparts to construct a new data vector, and then the unitary ESPRIT method is adopted to estimate the 2D-DODs and 2D-DOAs, which can automatically pair the four dimensional (4D) angle parameters. Simulation results are included to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input multiple-output radar with unknown spatially colored noise. By exploiting the sparse structure of the noise covariance matrix, a new de-noising scheme is designed. The signal covariance matrix is recast as a low-rank matrix with missing entries, which can be approximately tackled via solving an optimization problem. Thereafter, DODs and DOAs are obtained with the traditional subspace techniques. The proposed method does not bring any virtual aperture loss; thus, it achieves more accurate estimation performance than several state-of-the-art de-noising methods. Finally, the stochastic Cramer–Rao bound for joint direction finding is derived. Numerical computer simulations verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Through-the-wall radar imaging (TWRI) is a sensing technology that can be used for detecting, locating, and identifying targets inside enclosed building structures. Many of the existing target classification approaches focus on single-target scene. For multi-target classification, the radar signal has to be segregated into different target components. However, target separation in TWRI is a challenging problem since the radar signals consist of both strong wall reflections and weak target echoes. Furthermore, the target signals are attenuated and distorted when propagating through the wall. In this paper, a variational model with low-rank constraint is proposed for decomposing the radar signal into target components and removing the wall returns. Experimental results show that the proposed method can effectively separate the radar signal into different target components. [ABSTRACT FROM AUTHOR]

This paper investigates the joint direction of departure (DOD) and direction of arrival (DOA) estimation of coherent targets in bistatic multiple-input multiple-output (MIMO) radar under the presence of spatially correlated noise. Based on electromagnetic vector sensors at both transmitter and receiver of MIMO radar, a preprocessing method, namely polarization difference smoothing, is proposed to remove the coherence between targets and to suppress the spatially correlated noise. Then DODs and DOAs are estimated using the ESPRIT method. Further, this paper develops a simple approach for pair-matching between the estimated DODs and DOAs. Simulation results are compared with the receive polarization smoothing and transmit-receive polarization smoothing methods available in literature. Results show that the proposed approach improves the performance significantly. [ABSTRACT FROM AUTHOR]

In this paper, a coherent multistatic radar network with a novel system architecture is presented, which circumvents the general problems of clock distribution and phase noise-related signal-to-noise ratio (SNR) issues. The proposed network consists of a variable number of multiple-input multiple-output (MIMO) radar sensors and a variable number of repeater tags, all of which operate incoherently on the hardware level. In a minimum configuration, the network only consists of one MIMO radar sensor and a repeater tag. The theory behind such a multistatic network is mathematically derived, and simulations are presented to show key aspects of the network, i.e., multistatic range and Doppler measurements, as well as high-resolution angle estimation, exploiting a very large virtual aperture spanning the whole network. Measurements with one sensor and one repeater tag at 77 GHz are carried out to verify the simulations. The measurements show that the bistatic path between the sensor and the repeater tag retains coherency. [ABSTRACT FROM AUTHOR]

We propose a millimeter-wave joint radar-communications (JRC) system comprising a bi-static automotive radar and vehicle-to-vehicle communications. We study the applicability of known phase-modulated-continuous-wave (PMCW) and orthogonal-frequency-division-multiple-access waveforms for bi-static-JRC. In both cases, we design multiplexing strategies to ensure the parameter identifiability, derive JRC statistical bounds and numerically demonstrate superior performance of proposed low-complexity JRC super-resolution algorithms over conventional two-dimensional fast Fourier transform/MUltiple SIgnal Classification. [ABSTRACT FROM AUTHOR]

Typical specular meteor radars (SMRs) use one transmitting antenna and at least a five-antenna interferometric configuration on reception to study the mesosphere and lower thermosphere (MLT) region. The interferometric configuration allows the measurement of the angle-of-arrival (AOA) of the detected meteor echoes, which in turn is needed to derive atmospheric parameters (e.g., mean winds, momentum fluxes, temperatures, and neutral densities). Recently, we have shown that coherent MIMO configurations in atmospheric radars, i.e., multiple input (transmitters) and multiple output (receivers), with proper diversity in transmission can be used to enhance interferometric atmospheric and ionospheric observations. In this study we present novel SMR systems using multiple transmitters in interferometric configuration, each of them employing orthogonal pseudorandom coded transmitted sequences. After proper decoding, the angle of departure (AOD) of the detected meteor echoes with respect to the transmitter site are obtained at each receiving antenna. We present successful bistatic implementations of (1) five transmitters and one receiver using coded continuous wave (CW) (MISO-CW), and (2) five transmitters and five receivers using coded CW (MIMO-CW). The latter system allows simultaneous independent observations of the specular meteor trails with respect to the transmitter (AOD) and with respect to the receiver (AOA). The quality of the obtained results is evaluated in terms of the resulting mean winds, the number of detections and the daily diffusion trail vs. altitude behavior. We show that the proposed configurations are good alternatives to explore the MLT region. When combined with multi-static approaches, they can increase the number of meteor detections, thereby improving the quality of atmospheric estimates and allowing the measurement of new atmospheric parameters (e.g., horizontal divergence, vorticity), The use of multiple collocated transmitters for interferometric AOD determination makes building a multi-static radar network easier logistically, as only one receiver per receiving site antenna is sufficient. [ABSTRACT FROM AUTHOR]

• Propose a model to estimate the maximum achievable resolution considering the waveguide bandwidth and acceptable signal-to-noise ratio (SNR) loss as parameters. • A comparison of performance between common radar waveforms and the ideal case using four different radar signals. • To prevent significant temperature and pressure difficulties, we recommend installing the radar on top of the well. However, a challenge arises as the signal weakens when it travels to and from the depths of the well. • Our contribution lies in designing the optimal signal, which provides the best resolution and SNR. Radar systems model the emission medium simply as a delay and attenuation, assuming that the received signal from a point target is identical to the transmitted signal. However, when it comes to oil well walls, which are long metal cylinders, they exhibit frequency-selective behaviour and have a specific frequency response. If the transmitted waveform exceeds the channel's bandwidth, no signal is received. A low channel bandwidth results in reduced signal resolution, making it challenging to locate oil well perforations. Therefore, this paper aims to discuss the properties of the optimal waveform in terms of both signal-to-noise ratio (SNR) and resolution objectives. Additionally, it investigates conventional radar waveforms to determine which one performs best in an oil well context. The study reveals that achieving the optimum SNR and the highest resolution simultaneously is not possible, and a trade-off must be considered during waveform design. [Display omitted] [ABSTRACT FROM AUTHOR]