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Penetration path planning for stealth unmanned aerial vehicles (SUAVs) in the integrated air defense system (IADS) has been a hot research topic in recent years. The present study examines penetration path planning in different threat environments. Firstly, for the complex terrain and static radar threats, a modified A-Star algorithm containing the bidirectional sector expansion and variable step search strategy is proposed to elude static threats rapidly. Then, with regard to bandit threats, the minimal radar cross-section (RCS) tactics are presented to achieve path replanning. Furthermore, the combinatorial methodology of the minimum RCS tactics and the modified A-Star algorithm is applied to achieve the dynamic path planning for SUAV. The simulation results indicate that the modified A-Star algorithm and minimal RCS tactics can significantly reduce the probability of radar system, which has better superiority in calculation efficiency, path cost and safety. And the minimal RCS tactics have better real-time performance and are more convenient in dealing with dynamic threats, which enhances the survivability of SUAV and verifies the effectiveness of the proposed methodology.

In this paper, we propose a space-time allocation strategy for collocated multiple-input multiple-output (MIMO) radar to determine the beam directions to multiple targets, to be tracked. The transmit beams are allocated to different targets with distinct time units in one illumination. The basis of our strategy is to achieve the optimal probing through the feedback information in the tracking recursion cycle, with the objective of improving the overall tracking accuracy of multiple targets. We derive the posterior Cramer-Rao lower bound (PCRLB), used as the optimization criterion, to quantify the target state estimate accuracy. By introducing the illumination probability in each time unit, the space-time allocation problem is converted to a convex one. Therefore, a sufficient solution is provided, which aims at the real-time demand of radar system. Simulation results demonstrate the superiority of the proposed strategy to the uniform allocation, space-only allocation and time-only allocation methods.

Detection of moving targets under range-dependent clutter background is a challenging task in conventional bistatic space-time adaptive processing (STAP) radar. It is more intractable in the presence of range ambiguity, which also results in parameter estimation ambiguities. To tackle these issues, this paper proposes a novel framework of bistatic multiple-input multiple-output (MIMO) STAP radar with frequency diverse array (FDA) as the transmit array. The FDA employs a micro frequency increment across the array elements. Therefore, it is capable of providing additional degrees-of-freedom (DOFs) in range domain in the transmit end. After combining the DOFs in transmit, receive and pulse dimensions, the clutter or target of bistatic FDA-MIMO radar possesses the following properties: (i) for a particular range region, the clutter ridges of all range bins are located in a common plane of the three-dimensional (3D) space. (ii) By properly designing the frequency increment of FDA, the clutter ridges from different range regions are located in distinct 3D planes and thus are discriminable in 3D space. (iii) As long as the sum of the target radial velocities relative to the transmitter and the receiver is unequal to zero, the target will not be located in these clutter planes. By exploiting these new properties, a 3D localized space-time-range adaptive processing (STRAP) approach is presented to realize clutter suppression. It evidently reduces the computational complexity and the requirement for the amount of training data. Several numerical examples are provided to demonstrate the superiority of the proposed technique over the existing techniques.

This paper proposes a method for estimating the joint direction of departure (DOD) and direction of arrival (DOA) of coherent targets in multiple-input multiple-output (MIMO) radar with electromagnetic vector sensors. The method does not require any additional processing for both decorrelating the coherent targets and pairing the DOD and DOA, and exhibits real-valued operations. Compared to the existing polarization smoothing and unitary ESPRIT, the proposed method shows improved performance which is validated by the simulation results.

This paper considers the target detection problem in multiple-input multiple-output radar where the disturbance covariance matrix is unknown. Based on the Rao criterion, a persymmetric Rao test is designed by exploiting the persymmetric structure of the disturbance covariance matrix. Particularly, the proposed detector does not require training data, and exhibits strong selectivity to mismatched signals. Another contribution in this paper is to propose a two-stage detector whose second stage coincides with the proposed persymmetric Rao test. The selectivity or robustness of the two-stage detector can be tuned by selecting different threshold pairs.

Orthogonal matching pursuit (OMP) is a popular greedy algorithm because of its simplicity and low computational cost. In the application of OMP, one of the most important issue is to design a stopping condition according to some prior information, such as sparsity and noise level. In this paper, we provide a blind stopping condition for OMP in the presence of Gaussian noise. We theoretically analyze the stopping condition in terms of the mutual incoherence and the minimum component signal-to-noise ratio (SNR), with which OMP can stop with correct support recovery. Different from other alternatives, the proposed stopping condition can be explicitly determined regardless of prior information. So it is much suitable for practical applications. In the application of compressive sensing pulse-Doppler radar, we further analyze the support recovery performance of the proposed condition. By separately recovering the targets belonging to different Doppler frequencies, we remarkably reduce the requirement on the minimum component SNR of all targets, and thus our stopping condition is applicable to low SNR scenes. Numerical results illustrate that the proposed condition has comparable performance to the alternative with a priori information.

Focusing on the signal to interference-plus-noise ratio (SINR) maximization in colocated multiple-input multiple-output (MIMO) radars, using the transmit covariance matrix (TCM) design of transmit waveforms, we have proposed a TCM Rpm with the form of symmetrical Toeplitz matrix, where m is the control parameter to generate different TCM. The main features include (a) full rank, to exploit the waveform diversity advantage of MIMO radar and to further suppress the maximum number of interfering sources, (b) positive semi-definition of sin ((π/2)Rpm) to guarantee that Rpm can be synthesized with binary phase shift keying (BPSK) waveforms in closed form, (c) higher output SINR level using the Rpm with smaller m in the receiver, with the prior knowledge of target and interference locations, (d) lower receive sidelobe levels (SLLs) using the Rpm with certain larger m (e.g. m = 1.5 or 2) for the unwanted and unknown sidelobe interference suppression. Simulation results validate the better performance of our proposed TCM compared to the phased-array, omnidirectional MIMO radar, phased-MIMO radar and the recently proposed TCMs.

Low signal-to-noise ratio (SNR) in wideband radar reconnaissance degrades performance of existing signal type recognition methods. To achieve higher correct recognition rate in low SNR levels, this paper proposes a recognition approach containing feature extraction and automatic estimation for signal type number. First, feature vectors of radar pulses are constructed from maximum energy angle (MEA) and maximum energy slice (MES) of ambiguity function (AF). To improve applicability in practical implementing, AF grids method is developed to obtain MEA and MES. A novel algorithm for denoising MES with geometric shape constraint is also proposed to improve validity of features with low SNR. Second, for realistic reconnaissance lacking prior knowledge, a type number estimation method is developed that costs little extra processing burden. Simulation results indicate that the proposed denoising algorithm brings high stability and robustness to extracted features in low SNR levels. The recognition approach in this paper is applicable to more realistic scenarios and achieves higher correct recognition rate with respect to low SNR compared with existing methods.

In this paper, we propose a novel angle estimation method in bistatic multiple-input multiple-output (MIMO) radar, which combines the transmit beamspace (TB) technique with the unitary ESPRIT (U-ESPRIT) model. The TB matrix is designed to focus the transmitted energy within the desired spatial sector, thus achieving the transmit coherent gain. Due to the special data structure of the TB-based bistatic MIMO radar, a corresponding method is proposed to construct the real-valued model. Unfortunately, the existed interpolation error inevitably degrades the direction-of-departure (DOD) estimation performance. To compensate the DOD estimation error, a look-up table is created, which establishes a one-to-one mapping relationship for DOD. The Cramer-Rao bound (CRB) on angle estimation in TB-based bistatic MIMO radar is also derived for performance analysis. Compared with existing methods, the proposed algorithm has better estimation performance due to the improved signal-to-noise ratio (SNR) gain and requires less computational complexity. Numerical simulations are presented to demonstrate the estimation performance of the proposed algorithm.

Range dependent clutter suppression is a challenging problem in non-side-looking airborne radar, especially in the presence of range ambiguity. To deal with this problem, this paper proposes a two-stage adaptive clutter suppression method. It utilizes the degrees of freedom in range, angle and time domains provided by a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. In the first stage, a novel multi-waveform based adaptive beamforming in joint transmit-receive (Tx–Rx) domain is proposed for range-ambiguous clutter suppression. Analysis on second-order statistic property shows that the clutter data obtained from multiple waveforms with disjoint frequency bands are (approximately) independent and identically distributed (IID) in joint Tx–Rx spatial domain. By exploiting this characteristic, a target-free Tx–Rx covariance matrix is estimated by the IID data, which are extracted from carefully designed secondary waveforms. Therefore, a near-optimal Tx–Rx response is achieved, and range-ambiguous clutter can be suppressed effectively. In the second stage, a new auxiliary channel based space-time adaptive processing approach incorporated with range dependence compensation is devised for residual clutter suppression. It takes full advantage of the prior knowledge of clutter spectrum. Thus, excellent clutter suppression performance can be obtained by the proposed approach. Simulation results demonstrate the effectiveness of the proposed method.

This paper revisits the problem of multiple parameters estimation for coherent targets in bistatic EMVS-MIMO radar. By extending the spatial smoothing mechanism to the EMVS-MIMO radar, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed. The killer idea of the proposed methodologies is to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector cross-product technique and the LS method. Unlike the state-of-the-art polarization smoothing methods, the proposed methods would not sacrifice the polarization information, so that TS, RS and TRS are capable of providing polarization information of the coherent targets. The proposed estimators do not require any constrain on the geometries of the Tx/Rx EMVS arrays. They are computationally friendly yet retain robustness to the sensor position error. The proposed estimators are analyzed in detail, and numerical simulations are provided to verify their theoretical advantages.

The problem of spatial resolution cell (SRC) based centralized target detection in multistatic radar is considered. Due to the fact that using the observation data from different radar sites can result in a much smaller uncertainty volume, the SRC of multistatic radar is usually an intersection of the overlapping range, azimuth and elevation resolution cells of all spatial diversity channels (SDCs). Then, the target detection problem in multistatic radar can be considered as deciding whether a target is present in each probed SRC. In this paper, a general scheme of the SRC based centralized target detection in multistatic radar is proposed. Considering that different neighboring SRCs of multistatic radar may correspond to the same range, azimuth and elevation resolution cell of one SDC, the desired signals or noises from these neighboring SRCs hence can be mutually dependent. Motivated by this, the symbiosis relationship of these neighboring SRCs in exceeding the test threshold during the process of centralized target detection is specifically studied. Furthermore, the performance of the peak search method to decide which SRC on earth contains the target of interest is accordingly analyzed.

Extended target probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-target tracking. However, in the classic ET-PHD filters, the measurements of different targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance.

We aim to address the problem of target detection in collocated multiple-input multiple-output (MIMO) radar where the disturbance covariance matrix is unknown. In practice, the disturbance covariance matrix has a persymmetric or Toeplitz structure, when the MIMO radar receiver is equipped with a symmetrically or uniformly spaced linear array. We exploit the persymmetric or Toeplitz structure of the disturbance covariance matrix to design two adaptive detectors that do not require training data. Analytical expressions for the probability of false alarm and detection probability are derived for the proposed detector that takes the persymmetry into account. Numerical examples are provided to show that the proposed detectors outperform the conventional counterparts.

This paper investigates the moving robot localisation problem using a Doppler–Azimuth radar array. The solution is formulated in the framework of nonlinear/non-Gaussian estimation using a particle filter and a random finite set (RFS) model of measurements. The proposed approach assumes the availability of a feature-based map, radar measurements and robot odometry data. The associations between the measurements and the features of the map (landmarks) are unknown. The RFS model is adopted to deal with false and missed detections and uses Murty’s algorithm to reduce computation when solving the association problem. The proposed particle filter incorporates the Kullback–Leibler Distance (KLD)-Sampling to reduce computational time. Monte-Carlo simulation results demonstrate the efficacy of the proposed algorithm.

Inview of the advantages of polarization diversity and waveform diversity, polarimetric multiple-input multiple-output (MIMO) radar, which combines the two advantages, is a subject worthy of in-depth study. Height measurement is one of the key problems to be solved for meter wave radar. Therefore, this paper focuses on height measurement with meter wave polarimetric MIMO radar. First, we derive the height measurement signal model for flat ground reflection combined with the two diversity techniques. Then, we properly classify the signal model and modify it to make it suitable for the classical super-resolution height measurement algorithms, including the generalized multiple signal classification(MUSIC) algorithm and steering vector synthesis (SVS) MUSIC algorithm. The split beam question in SVS-MUSIC is found and the resolution is provided. The proposed algorithms are also extended to long dipoles and large loop MIMO radar. Moreover, the computational complexity of the two algorithms and the derivation of Cramer Rao bound (CRB) for polarimetric MIMO radar are given. These methods do not require decoherence. The innovation of this paper is to provide a height measurement signal model for meter wave polarimetric MIMO radar along with its effective solution. The simulation results prove the accuracy of this approach.

This paper investigates the design of constant modulus waveforms with low auto- and cross-correlation sidelobes for multiple-input multiple-output (MIMO) radar. The resulting weighted integrated sidelobe level (WISL) minimization problem involves a fourth-order objective function and nonconvex constant modulus constraint and, hence, is NP-hard. Unlike existing methods which indirectly solve an “almost equivalent” problem to the original one, we equivalently reformulate the nonconvex WISL minimization problem appropriately and propose a new method to directly deal with the problem. Specifically, in the proposed algorithm, the fourth-order problem is first converted into a bi-quadratic problem via introducing an auxiliary primal variable, and then a primal-dual framework, named as “nonlinear alternating direction method of multipliers (nonlinear-ADMM)”, is derived to handle the modified problem. More importantly, both the convergence and termination conditions of this algorithm are discussed. Numerical simulation are carried out to assess the performance of the proposed algorithm in terms of the WISL metric, auto- and cross- correlation properties and computational complexity.

The gain and phase calibration of monostatic multiple-input multiple-output (MIMO) radars with partly calibrated arrays is addressed and a clutter-based orthogonal projection (OP) method is introduced. When the transmitter and receiver of a monostatic MIMO radar are both uniform linear arrays with the same array spacing, a non-zero OP matrix for the joint transmit-receive array steering vectors can be found. The clutter output power projected on the OP matrix is zero if array is perfectly calibrated. Hence, the transmitter gain and phase can be calibrated by minimizing clutter projection output power for a well-calibrated receiver subarray. Subsequently, the unknown receiver gain and phase errors can be estimated with a similar projection method. Numerical simulation results confirm the effectiveness of the proposed method.

The simultaneous orthogonal multi-beam transmitted by a collocated multiple-input multiple-output (MIMO) radar is very effective in multi-target tracking. Since this working mode can meet the requirement of low possibility of interception; meanwhile, gain higher sensitivity and resolution. Aiming at the resource management in such a scenario, a joint beam, power and waveform selection strategy is put forward. The optimization model is established subject to a certain number of beams, power budget and waveform parameters. The criterion, which is predicted by the Kalman recursive equation, is minimizing the posterior estimate errors of multiple targets in worst cases simultaneously. Thereby, the suitable number of utilized beams, the allocated power as well as the waveform parameter in each beam for the current time epoch can be adaptively turned according to the estimate errors from the previous time epoch. We then fully demonstrate such a non-convex problem can be transformed into several convex problems. As such, the solution can be provided efficiently to meet the real-time demand. Furthermore, through obtaining the posterior estimate error by the square-root cubature Kalman filter, prediction and then adapts the three resources for next time epoch, a closed loop feedback allocation scheme is established. The simulation results show that the proposed algorithm can significantly improve the tracking performance compared with the uniform and random resource allocation strategies.

In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.

Passive radar capable of recognizing illumination of opportunities can improve the detection performance on account of its functional properties of environment adaptivity. Waveform recognition approaches based on Deep Learning can outperform traditional methods based on hand-crafted feature as shown in recent studies. In this paper, we propose a novel transferred deep learning waveform recognition method which makes use of multi-scale convolution and temporal dependency characteristics to improve the recognition performance. Firstly, we develop a two-channel convolutional neural networks combining with Bi-directional Long Short-Term Memory (TCNN-BL) architecture to extract features of different scales and merge past and future states. Then in order to solve the transferability problem across various sampling frequencies, we present a parameter transfer approach which initializes target domain classifier using source domain parameters. Based on our experiments on both public datasets and our own datasets, it can be demonstrated that the proposed approach significantly outperforms the state-of-the-art methods.

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

This paper considers the detection problem of radar maneuvering target with the range migration (RM) and Doppler frequency migration (DFM) and proposes an coherent integration method based on two steps scaling and fractional Fourier transform, i.e., TSS-FrFT. To be specific, this method corrects the quadratic RM (QRM) and DFM based on FrFT and a scaling processing of the rotation angle firstly. Then, it eliminates the coupling between the range frequency variable and FrFT domain (FrFD) variable via a second scaling processing. In the end, the coherent integration is achieved by the inverse Fourier transform (IFT) with respect to the range frequency variable. Compared with the existing methods, the proposed TSS-FrFT method has superior detection performance and low computational complexity. Moreover, considering the sudden change of the target acceleration within the coherent integration interval, this paper also presents an acceleration matched filter (AMF) to remove the Doppler frequency rate change (DFRC) caused by this acceleration change. Computer simulation results are also given to demonstrate the effectiveness of the proposed method from different aspects, i.e., computational complexity, coherent integration for a single target and multiple targets and detection performance for a uniform acceleration and non-uniform accelerations.

For airborne conformal array radar, it is difficult to suppress clutter using conventional space-time adaptive processing (STAP) methods because the geometry results in range-dependent clutter and nonuniform spatial steering vector. In order to improve the performance of conformal array clutter suppression, a knowledge aided method based on semiparametric/sparse iterative covariance-based estimation STAP, named with KASPICE-STAP, is proposed here. The KASPICE-STAP method requires the knowledge of the clutter ridge spread of the testing range cell. Based on this knowledge, we can construct a dictionary matrix which consists of the clutter space-time steering vectors. Subsequently, by using the dictionary matrix, a relatively accurate covariance matrix of clutter plus noise can be calculated with the KASPICE-STAP method. The proposed approach can work efficiently in complex environments only with the data of cell under test. Compared with other existing sparse recovery STAP methods, the proposed method has global convergence properties and it does not require making any difficult selection of hyperparameters. The improvement factor (IF) curves and range-Doppler images demonstrate the effectiveness of the proposed method.

The suppression of narrow-band interference (NBI) has received wide attention with Nyquist-sampling data. However, the developed techniques cannot be directly integrated into sub-Nyquist radar systems. In this paper, we study the NBI suppression for quadrature compressive sampling (QuadCS) radar having sub-Nyquist sampling rate. With slow time-varying property of the NBI envelope, we first define a short-time sinusoidal (STS) dictionary to represent the NBI signals and reveal that QuadCS NBI signals are not only sparse but also block sparse. Then we find that for sparse targets, QuadCS radar outputs a combined sparse signal consisting of sparse targets and block sparse NBI signals. Considering this structure and inherent property of the NBI signals, we finally develop a sparse reconstruction method for estimating both the target signals and the NBI signals. The proposed method is a cascade of the block sparse reconstruction and the sparse reconstruction with data-driven stopping conditions and therefore, it works without any a priori information on the sparsity and noise level. Computer simulations show that the STS dictionary approximates the NBI signals well and the cascade reconstruction derives high accurate estimation of the target signals with strong suppression of the NBI signals.

The target detection problem in multi-static passive radar systems (MS-PRS) is investigated, where multiple transmitters operate at a single frequency and a single multi-antenna radar receiver processes the received signals. In contrast to multi-frequency MS-PRS, the detector design is challenging due to the fact that the detector sees a mixture of transmitted signals which may not be separately treated. We propose the design of generalized likelihood ratio test (GLRT) receivers to optimize the detection performance. The beamformers at the radar receiver are optimized so that the received signals can be seen as statistically independent signals from multiple reference channels and a surveillance channel. The detector optimization problem is accurately approximated with a convex optimization algorithm. It is analytically shown that the performance of the proposed GLRT receiver approaches that of the active radar. Computer simulations verify the analytical results.

Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory., 39 pages, 20 figures. Accepted in Signal Processing (Elsevier)

Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer.

Adaptive weight matrix design and parameter estimation via sparse modeling are proposed for colocated multiple-input multiple-output radar.The sensing matrix design and transmit weight matrix are implemented in an iterative cyclic way.The angle-amplitude estimation performance of the proposed approach is examined by analyzing the CramerRao lower bound.Numerical results demonstrate the significant performance improvement by the proposed design approach. Although sparse representation and sparse recovery algorithms for colocated multiple-input multiple-output (MIMO) radar have received much attention, incoherence of the sensing matrix received few discussions. In this paper, we propose adaptive weight matrix design and parameter estimation via sparse modeling to improve the recovery performance for MIMO radar. First, a sparse framework is formulated for the MIMO array with decoupled transmit weight matrix and steering matrix. Next, a two stage method is proposed to optimize the two matrices to improve the DOA estimation performance. Finally, a sparse recovery approach based on lq (0

In this paper, we propose an effective scheme for angle estimation and array mutual coupling (MC) self-calibration in uniform linear arrays (ULA)-based bistatic multiple-input multiple-output (MIMO) radar. By exploiting the multidimensional inherent structure, the array data is formulated into a trilinear model. The transmit and receive direction matrices are primarily estimated via trilinear decomposition, after which the least square (LS) method is applied to obtain a rough angle estimation. Refined angles are achieved via two one-dimensional local searches. The MC coefficients are obtained via LS by utilizing the estimated angle prior. The proposed scheme can achieve automatic pairing of the estimated angles without any virtual aperture loss, thus it has better angle and MC estimation performance than existing methods. Numerical simulations verify the improvement of our scheme.

Frequency diverse array (FDA) can produce a range-angle-dependent beampattern due to the employment of a small frequency increment across the array elements. By utilizing this characteristic, this letter devises an FDA and multiple-input multiple-output hybrid radar to jointly estimate the ranges and angles of multiple targets via optimal transmit beamspace design. A uniform elemental power constraint is adopted as the optimization criterion is to minimize the mutual coherence of the sensing matrix using a sparse model and to achieve high-resolution estimation in both range and angle dimensions. A cyclic optimization approach based on power method-like is further developed to solve the non-convex design problem. The excellent performance of the proposed approach is demonstrated via computer simulations.

In this paper, we consider robust adaptive beamformer design for multiple-input multiple-output (MIMO) radar systems. The desired transmit-receive steering vector is estimated through maximizing the output power subject to constraints upon correlation coefficient and steering vector norm. The original nonconvex problem is reformulated as two reduced dimension semi-definite programming (SDP) problems. An iterative procedure is devised to tackle the two SDP problems, whose convergence is analytically proven. Based on the estimated desired signal, we are then able to obtain the interference covariance matrix via the matrix rank-constrained minimization method. Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy. Simulation results are presented to confirm the effectiveness and robustness of the proposed approach.

This paper investigates the problem of locating a moving target using a passive radar system with multiple transmitters and multiple receivers. The bistatic range and bistatic range rate between each transmitter and receiver are used as the measurements. A novel algebraic solution employing two-step weighted least squares (2WLS) minimizations is proposed. In the first step, the measurement equations are linearized by introducing multiple additional parameters and a WLS minimization is used to obtain a rough estimate; then in the second step, the known relation between the additional parameters and the target location parameters is utilized to refine the estimate. Theoretical accuracy analysis indicates that the proposed algorithm achieves the Cramer-Row lower bound, and Monte-Carlo simulations demonstrate that the proposed algorithm outperforms existing algorithms.

In this paper, considering multiple regions for interference simultaneously, an optimal antenna deployment problem for distributed multistatic radar is investigated. The optimal antenna deployment problem is solved by proposing an antenna deployment method based on Multi-Objective Particle Swarm Optimization (MOPSO). Firstly, we construct a multi-objective optimization problem for multistatic radar antenna deployment by choosing the interference power densities of different regions as objective functions. Then, to obtain the optimal deployment result without wasting time and computational resources, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the MOPSO optimization process efficiently when the optimal antenna deployment algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

A novel method of layout optimization.Pareto optimal layout of multistatic radar.Available volume is proposed as performance metric.Pareto optimal results based on dynamic game.Low computational complexity. Layouts influence the performance of location accuracy greatly in multistatic radar. The layout in classical regular shape may not be an excellent choice. In this paper, Pareto optimal layout of multistatic radar is proposed based on dynamic game theory. The available volume associated with the Cramer-Rao lower bound (CRLB) of location accuracy is proposed as the utility metric to evaluate the performance of the layout. Simulation results show that different initial layouts may lead to different final layouts. Although these final layouts are different they are all Pareto optimal and similar in performance.

In the 2015 paper entitled “Deceptive jamming suppression with frequency diverse MIMO radar”, a procedure to discriminate the true and false targets in the joint transmit-receive domain of the frequency diverse MIMO radar has been proposed based on the derived signals of the true and the false targets. This note indicates an error made during these derivations in the above paper.

Conventional high resolution range profile (HRRP) based radar automatic target recognition (RATR) methods are based on a complete template library assumption. However, such an assumption can barely hold in real world applications since it is difficult to observe target echoes from all aspects, especially for noncooperative targets. To alleviate the target-aspect missing problem, we develop a domain-aware meta network (DOAMN) for HRRP-based RATR. Specifically, the DOAMN first uses a domain-aware (DOM) module to distinguish whether HRRPs come from the seen or unseen target aspects, then a meta network (MNet) is employed to learn a generalized parameter initialization that is able to achieve fast adaptation among target aspects. To enable the proposed DOAMN to be trained in an end-to-end manner, we further present an effective iterative hybrid optimization method. Experiments on simulated HRRP dataset demonstrate the effectiveness and efficiency of the proposed model.

To improve the availability of limited spectral resources and construct a cost-efficient platform simultaneously performing both radar and communication functions, the orthogonal frequency division multiplexing (OFDM) integrated radar and communication system (IRCS), which is referred to as OFDM-IRCS, is provided. For the frequency sensitive target and frequency selective fading channel, it is able to improve the radar and communication performance by efficiently employing the limited transmit power in the OFDM-IRCS. To this end, we propose two optimal waveform design methods that meet different users’ demands. First, the Cramer-Rao bounds (CRBs) for estimating range, velocity and target scattering coefficients with integrated OFDM waveform are derived. And the channel capacity in communication is formulated. Then, the multiobjective optimization problem is devised, and the adaptive weighted-optimal and Pareto-optimal waveform design approaches are proposed to simultaneously improve the estimation accuracy of range and velocity in radar and the channel capacity in communication. Finally, several numerical examples are presented to demonstrate the effectiveness of the proposed design methods.

This paper presents a new state estimation technology grounded in the unscented Kalman filtering for nonlinear continuous-time stochastic systems. The resulting accurate continuous–discrete unscented Kalman filter is based on adaptive solvers with automatic global error control for treating numerically the moment differential equations arising in the mean and covariance calculation of propagated Gaussian density. It is intended for an accurate and robust state estimation in nonlinear continuous–discrete stochastic systems of various sorts, including in radar tracking models. This new filter is examined in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn. The latter is considered to be a challenging one for testing nonlinear filtering algorithms. For comparison, we also examine such efficient state estimators as the accurate continuous–discrete extended Kalman filter, the continuous–discrete unscented Kalman filter and the mixed-type accurate continuous–discrete extended-unscented Kalman filter designed earlier, but further modified in the present study. The comparison is fulfilled in terms of accuracy and efficiency of estimating the state in the mentioned air traffic control scenario.

This paper considers the constrained waveform design for Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integration Sidelobe Level (ISL) to optimize the waveform covariance matrix enforcing a uniform elemental power restriction as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we present the existing Cyclic Algorithm (CA) and a new Sequential Iterative Algorithm (SIA) to devise the waveform under a constant modulus constraint, and a similarity constraint to allow the designed waveform sharing the similarity feature with a given reference waveform. In particular, the proposed SIA directly optimizes the objective function and its each iteration turns the multidimensional optimization problem into multiple one-dimensional optimization problems with closed-form solutions. Finally, we assess the effectiveness of the proposed technique through numerical simulations in comparison with CA.

This paper studies adaptive detection of radar targets embedded in generalized Pareto clutter on the condition with the limited secondary data. In order to alleviate the effects of the non-Gaussian characteristic of the clutter, a-priori knowledge of the non-Gaussian clutter is considered in the designed detector. More precisely, we consider that the texture of clutter obeys the inverse gamma distribution and the inverse covariance matrix of speckle is a combination of multiple a-priori spectral models. Within these considerations, we obtain an adaptive detector based on the generalized likelihood ratio test. Finally, the performance of the proposed detector is evaluated via the Monte-Carlo technique. The experiments results indicate that the proposed detector outperforms the 1S-GLRT detector in limited secondary data scenarios.

This paper addresses the radar waveform design problem in spectrally crowded environments. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) of the waveform under spectral and similarity constraints. The existing algorithm proposes to tackle such a problem via semidefinite relaxation (SDR) and rank-one decomposition, resulting in high complexity and limited applications. Motivated by the decomposability and superior convergence properties of alternating direction method of multipliers (ADMM), we propose a novel algorithm to tackle the waveform optimization problem. Since simpler subproblems are involved at each iteration and they can be tackled efficiently, the proposed algorithm has a much lower computational complexity than the existing algorithm. Numerical examples demonstrate the effectiveness of the proposed algorithm.

Artificial intelligence techniques were applied for detecting small moving targets in maritime clutter environments. Neural detectors are considered to approximate the Neyman–Pearson (NP) in composite hypothesis testing problems. Sub-optimum approaches based on the Constrained Generalized Likelihood Ratio (CGLR) were analysed, and compared to conventional implementations based on Doppler filtering that are designed to filter clutter and improve the Signal-to-Interference Ratio, and Constant False Alarm Rate techniques. The CGLR performance was significantly better at the expense of a high computational cost. As a solution, neural network training sets were designed for approximating the NP detector. The detection of small boats in Gaussian clutter was the defined case study in order to assume the design hypothesis of the conventional solutions and to study their performance under their most favourable conditions. Detection schemes were evaluated using real radar data. Neural solutions based on Second Order Neural Networks provide the best results, being able to approximate the CGLR with a significantly low computational cost compatible with real-time operations.

The frequency diverse array-multi-input multi-output (FDA-MIMO) radar can locate target in angle and range dimensions by transmitting little frequency offset across the transmit sensors. In this paper, a joint space-time-frequency domain optimization scheme is designed to estimate the direction of departure (DOD), direction of arrival (DOA) and range of target. We propose synthetic aperture-multi coprime frequency offset (SA-MCFO) framework which synthesizes all subarrays generated by array motion to expand array aperture and signal bandwidth. We also propose reduce dimension multiple signal classification with decoupling (RDMD) algorithm to detect target by combining reduce dimension (RD) transformation and subarray based estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which results in decoupling of DOD and range as well as fine estimation accuracy. The proposed SA-MCFO framwork enjoys remarkable system flexibility but with lower system cost. The proposed RDMD algorithm significantly reduce computational complexity but with ignorable performance degradation compared with the conventional 3D multiple signal classification (3D-MUSIC) algorithm. We also provide the Cramer-Rao bounds (CRBs) of DOD, DOA and range as performance benchmark, and numerical simulations are conducted to verify the superiorities and effectiveness of the proposed SA-MCFO framework and RDMD algorithm.