This paper addresses the problem of channel allocation in multi-static passive radar (MPR) system for multiple maneuvering targets tracking with missed detection. To evaluate the performance of tracking with missed detection, a closed-form analytical expression for the predicted conditional Cramér-Rao lower bound (PC-CRLB) is derived, based on which, an optimization problem model with missed detection is established. To solve this problem, the penalty function and linearized alternating direction method of multi-pliers (L-ADMM) are introduced, and an adaptive channel allocation method with missed detection (ACAMD) is proposed. Simulation results indicate that the proposed algorithm can effectively improve the tracking accuracy while meeting the specified system requirements with missed detection compared to other algorithms.

In this paper, we consider the detection of range-spread targets with unknown steering vectors for radar systems. Based on the Rao test and Wald test, we proposed two novel adaptive detectors of range-spread targets with unknown steering vectors. And we exploit the persymmetric property of the noise covariance matrix, which enables the two proposed detectors robust in the situation of limited training data. Moreover, we exploit a series of equivalent transformations to transform the test statistics into the real domain to prove the CFAR property concisely. Finally, the Monte Carlo simulations verify the effective detection performance of the proposed detectors. We found that the novel detectors perform well in a vast number of situations.

This paper deeply explores the detection of a signal belonging to a union of subspace (UoS) in the presence of interference subspace. We propose three detectors based on the generalized likelihood ratio test (GLRT), Rao criterion, and Wald criterion using the presymmetry structure of the received data. By taking advantage of the data’s presymmetric structure, we can detect the signal more effectively. Experimental results show that the proposed detectors perform better than the traditional detectors in the presence of interference subspace, especially when the training data is insufficient. In general, this paper provides an effective method for solving the problem of signal detection with interference subspace.

This paper presents a cognition-based transmitter polarization optimization method for the conformal array space-time adaptive processing. First, we introduce the signal model and working mechanism of the airborne conformal array radar. Then the polarization characteristics of the target are recognized through the least squares criterion. Leveraging the estimated target scattering matrix, we further design the transmitter polarization optimization problem. Finally, the semidefinite relaxation approach is adopted to obtain the optimized transmit weight vector. Simulation results demonstrate the effectiveness of the proposed method in clutter cancellation.

This paper deals with the target detection problem in nonzero-mean compound Gaussian (CG) sea clutter with the generalized inverse Gaussian (GIG) texture. With the improvement of radar resolution, the CG distribution is adopted to model the sea clutter. Then, considering the characteristics of real sea clutter, the CG model with the GIG texture is applied. Furthermore, sea clutter signals are assumed to be nonzero-mean. A novel adaptive two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) detection algorithm is proposed. Firstly, the test statistic of the proposed detector with known GIG texture, mean vector (MV), and covariance matrix (CM) is derived. Secondly, replacing with the estimates of GIG texture, MV, and CM, the adaptive detector can be acquired. The numerical results indicate the performance of the proposed detector.

This letter deals with the target detection problem for polarimetric marine radar. The sea clutter is modelled as the compound Gaussian (CG) distribution with lognormal texture. We propose three detectors based on the two-step generalized likelihood ratio test (GLRT), the complex value Rao, and Wald tests by utilizing the persymmetric properties of the polarimetric speckle covariance matrix (CM). The lognormal texture component and the speckle CM are estimated by the maximuma posteriori(MAP) criterion and the polarimetric persymmetric fixed-point estimator, respectively. In addition, we provide proof of the constant false alarm rate (CFAR) properties of the designed polarimetric detectors. Moreover, we evaluate the detection performance of the proposed detectors in the simulated data and the measured sea clutter data, and the simulation results show the proposed detector outperforms the competitors more than 1dB in different situations.

Adaptive monopulse algorithm always faces low angle measurement performance in multi-interference environments with low signal-to-noise ratio. To clarify the impact factors, we analyze the performance of monopulse ratio estimation and further derive the Cramer-Rao bound (CRB) for angle estimation. Based on the analysis, a constraint-relaxed adaptive monopulse (RCAM) optimization problem is proposed, and the closed-form solution of the problem is obtained by alternative-direction-method-of-multipliers (ADMM) with fast convergence. Numerical simulations have been provided to demonstrate the effectiveness of the proposed algorithm in improving beamforming and angle estimation.

Radio frequency-based (RF-based) detection methods are currently the main means of countering drones. However, these prevalent approaches frequently exhibit deficiencies in effectively addressing noise and interference, making them potentially unsuitable for application in realistic urban environments. This paper proposes a generalized RF signal-enhanced framework that explicitly addresses noise and interference. We decompose the RF signal into three components and uniformly integrate them into the proposed framework for decomposition. To accomplish this, three innovative loss functions and two appropriate neural networks are devised. To validate our framework, we create a real-world drone RF dataset sampled from urban surroundings, faithfully representing drone RF signals in real-world scenarios. Experimental results demonstrate that our framework exhibits satisfactory denoising and interferenceremoval performance, significantly improving the accuracy of multiple detection methods.

This paper addresses the problem of subspace detection in the compound Gaussian sea clutter with lognormal texture and structured interference. We proposed three novel subspace detectors by two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT), the Rao test, and the Wald test. In the first step, we assume the texture component and speckle covariance matrix (CM) are known, and we derive the test statistics of the proposed detectors. Then, in the second step, we substitute the estimated texture component and speckle CM to obtain the adaptive detectors. Further, we exploit the persymmetric property of the speckle CM to improve the detection performance of the proposed detectors. Moreover, we prove the constant false alarm rate (CFAR) properties of the novel subspace detectors with respect to the speckle covariance matrix and the scale parameter of the texture component of the non-Gaussian sea clutter. Besides, we verify the detection performance of the proposed subspace detectors by numerical experiments in both simulated and measured sea clutter. The simulation results show that the novel subspace detectors perform better than the comparison detectors in the case of limited training data, mismatched signals, and structured interference.

This paper proposes a reduced-dimension polarization-space-time adaptive processing (RD-PSTAP) algorithm for airborne conformal array (CFA) radar to achieve better clutter suppression performance at lower computational complexities. Firstly, we take polarization information into signal modeling. Subsequently, the dimension reduction matrix is derived based on the generalized sidelobe cancellation (GSC) structure, which incorporates the polarization information of the target and clutter into constructing the main channel and auxiliary channels, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm compared with the conventional RD algorithm.

This paper deals with the target detection problem in nonzero-mean compound Gaussian sea clutter with inverse Gamma texture. Considering the improvement of radar resolution and the time-varying characteristics of real sea clutter, the compound Gaussian distribution with the inverse Gamma texture is adopted to model the sea clutter. Moreover, the mean of sea clutter signals is assumed to be nonzero and unknown. The novel adaptive detector based on the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is proposed. Firstly, we derive the test statistic of the proposed detector under the condition that the inverse Gamma texture, the mean vector (MV), and the covariance matrix (CM) are assumed to be known. Secondly, the adaptive detector can be obtained by replacing them with their estimates. Simulation experiments are conducted using the synthetic nonzero-mean compound Gaussian sea clutter data. The numerical results indicate the performance of the proposed detector.

In the paper, we propose a new direction-of-arrival (DOA) estimation algorithm with low complexity. With the assumption of the independent noise, we analyze the sparse structure of the noise covariance matrix. Then, we address the sparse recovery optimization problem by using the co-sparse characteristic of signal and noise. Numerical simulation results verify the efficiency and advantage of the proposed method compared with some other existing sparse methods.

The novel near-field sources localization methods based on sparse reconstruction are presented to estimate direction-of-arrival (DOA) and range efficiently. A special covariance matrix is first constructed, which decouples the DOA and range, and then we can obtain DOA estimations by solving the MUSIC-like weighting sparse minimization problem with l2-norm. After obtaining the estimated DOAs, the related range parameters can be obtained by the same sparse reconstruction method as the DOA estimate. Furthermore, an improved weighting sparse optimization problem based on l1-norm is proposed to enhance the estimator's robustness. Finally, several simulations illustrate the advantages of the proposed algorithms by comparison.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

In this paper, an algorithm to obtain a minimum mainlobe beamwidth and control sidelobe level for broadband arrays is presented. The proposed algorithm has two steps. At the first step, for the reference frequency, a minimum mainlobe width approach is firstly proposed to obtain the reference pattern which has minimum mainlobe width with the given side-lobe level. At the second step, for other frequencies, the sidelobe levels are controlled to enlarge their corresponding beam width to be equal, resulting a constant beamwidth for broadband arrays. Simulations are provided to validate the effectiveness and superiority of the proposed algorithm.

This paper presents a flexible and accurate wideband beam pattern synthesis algorithm utilizing oblique projection. The modified flexible array response control via oblique projection (MFARCOP) is firstly proposed with a novel weight vector construction manner, which is more computationally efficient for adjusting multi-points accurate array responses. Then, the wideband pattern synthesis utilizing the proposed MFARCOP algorithm is developed via the space-frequency scheme. Hence, the space-frequency response can be flexibly and accurately synthesized. Numerical simulations demonstrate the performance of the proposed algorithm.

This paper considers transmit beamforming and space-time adaptive processing (STAP) filter design in airborne radar for clutter suppression. We suppose that there are some strong clutters, which have a different distribution than the clutter in the cell under test (CUT) based on the database containing a geographical information system (GIS). Hence, we devise a novel algorithm for the joint design of transmit beamforming and STAP filter which aims to maximize the signal-to-clutter-plus-noise ratio (SCNR). To tackle the nonconvex problem, we convert the joint optimization problem into an equivalent beamforming optimization problem and find the optimal so-lution based on the minorization-maximization (MM) technique iteratively. Numerical simulations are provided to val-idate the proposed method and demonstrate its high performance.

In this paper, for the application of multi-target tracking (MT-T), a joint node and resource scheduling (JNRS) strategy based on convex programming is proposed for the distributed multiple-input multiple-output (MIMO) radar network. To be more precise, the JNRS strategy is first formulated as a mathematic optimization problem, which is NP-hard. Then, an iterative and efficient solution technique incorporating the sequential convex programming (SCP) and the semi-definite programming (SDP) is proposed to tackle the NP-hard problem. Finally, numerical simulation results are provided to verify the effectiveness of the proposed JNRS strategy. Furthermore, the proposed JNRS strategy can achieve better MTT performance than other existing benchmark strategies.

In this paper, we propose a collaborative resource allocation and beampattern optimization (CRABO) strategy for multiple maneuvering targets tracking (MMTT) in the network of multiple colocated MIMO radar (CMR) nodes. Specifically, the CRABO strategy is formulated as a nonconvex optimization problem, where the node-target assignment, beampattern synthesis and bandwidth allocation are jointly optimized for the first time. To tackle the nonconvex problem efficiently, a fast sequential and decoupling-based three-stage solution algorithm that fully meets real-time requirement is developed. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed CRABO strategy.

In this paper, we consider the detection problem with the polarization diversity technique in the compound Gaussian sea clutter with Gamma texture. We derive the polarimetric detector based on the maximum a posterior (MAP) and two-step generalized likelihood ratio test (GLRT). In the first step, we assume that the polarimetric clutter covariance matrix (PCCM) and the Gamma textures are known, and we develop the test statistics of the proposed detector. In the second step, we obtain the adaptive polarimetric detector by using the fixed point covariance estimator (FPCE) to estimate the PCCM and the MAP method to estimate the Gamma textures. Then we prove the constant false alarm rate (CFAR) property of the proposed adaptive polarimetric detector. Finally, we take advantage of the simulated data and the real sea clutter data to evaluate the performance of the proposed polarimetric detector.

In this paper, we address the polarization diversity detection problem in the non-Gaussian sea clutter environment. Considering the heterogeneous characteristic of the sea clutter, we model it as the compound Gaussian distribution. Based on the two-step generalized likelihood ratio test (GLRT), Rao test, and Wald test, we propose three persymmetric and polarimetric detectors. In detail, we first assume that the polarimetric clutter covariance matrix (PCCM) is known, and we develop three test statistics of the proposed detectors. In addition, we use persymmetric property to estimate the PCCM, and obtain the adaptive persymmetric detectors by inserting the estimate of PCCM into first-step test statistics. The experiments are conducted by using the simulated data and the real sea clutter data to verify the performance of the proposed persymmetric polarimetric detectors.

This paper addresses the adaptive persymmetric detection of range-spread targets problem in compound Gaussian sea clut-ter with Gamma texture. A new compound Gaussian detector based on two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is developed. Specifically, the first step is to acquire the test statistic of GLRT on the as-sumption that the texture components and the clutter covari-ance matrix (CCM) are known. The second step is to employ the MAP approach to acquire the estimates of texture components and persymmetric method to obtain the estimate of CCM. Remarkably, the novel detector ensures the constant false alarm rate (CFAR) property with respect to the covari-ance matrix structure. The effectiveness of the proposed de-tector is verified by using simulated and real sea clutter data.

This paper considers the adaptive detection problem of range-spread targets in Gaussian environment. Model the Gaussian disturbance with unknown covariance matrix as an auto-regressive (AR) process with known AR order but unknown parameters. In order to solve this problem, we propose a novel generalised likelihood ratio test (GLRT)-based detector, which estimates the unknown parameters with secondary data support by maximum likelihood estimate (MLE). The simulation experiments are conducted by comparing the proposed detector with the traditional detectors on both synthetic data and real sea clutter data. And simulation results demonstrate the effectiveness of the proposed detector.

This paper deals with the problem of the hybrid beamforming design of wideband orthogonal frequency division multiplexing (OFDM) dual-function radar-communication (DFRC) system, which is expected to achieve a satisfactory user's spectral efficiency and form excellent space-frequency spectrum behavior as well as spatial nulls on the directions of strong signal-dependent interference (such as clutters) simultaneously. For such purpose, we formulate our problem by maximizing the communication spectral efficiency subject to the constraints of radar integrated sidelobe to mainlobe ratio (ISMR) and spatial spectrum nulling (SSN). Due to the fact that the analog beamformer for all subcarriers and digital beamformer for each subcarrier are simultaneously optimized in the wideband OFDM system, the resultant problem is difficult to solve. Towards that end, an efficient algorithm is devised based on the consensus alternating direction method of multipliers (CADMM) framework. Numerical simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm.

Abstract This study discusses the integrated system of full‐duplex (FD) multiple‐input‐multiple‐output (MIMO) communication and MIMO radar, which has been rarely considered in published studies. Such an integrated system is composed of a base station and several uplink (UL) and downlink (DL) users, and is supposed to simultaneously implement three functionalities, that is, target detection, UL and DL communications. The degrees of freedom of system design involve the precoding and combining schemes for UL and DL communications, as well as the receiving filter for radar echo. An optimization problem is developed to maximise a compound communication sum‐rate metric subject to the SINR constraint imposed on target detection performance. The resulted non‐convex and NP‐hard problem is first divided into two sub‐problems. Then, for one of the sub‐problems that is still intractable, its solution is iteratively obtained based on the fractional programing technique and the consensus alternating direction method of multipliers algorithm. In the simulation part, the convergence performance and computational efficiency of the proposed algorithm are illustrated. In addition, the target detection performance, DL and UL communication performance of the integrated system are also evaluated.

Abstract In this study, a communication‐awareness adaptive resource scheduling (CARS) strategy for multiple target tracking in a multiple radar system (MRS) is proposed. The CARS strategy aims to maximise the tracking performance of MRS, whilst minimising the interference from MRS to communication systems (CSs), whose mechanism is to simultaneously control the revisit frame interval of each target and determine the activation of radar nodes, the radar‐target assignment and the allocation of transmitted power. Mathematically, the CARS strategy is formulated as an optimization problem, which contains both the continuous variable and the discrete (integer) variable. To tackle the resultant mixed‐integer, non‐convex, and non‐linear problem efficiently, incorporating with the proposed hybrid particle swarm optimization algorithm based on the Kullback–Leibler divergence, a two‐stage solution technique is developed to obtain the near‐optimal solution. Numerical simulation results are provided to validate the proposed CARS strategy and demonstrate its superiority over the traditional scheduling strategies.

This letter addresses the persymmetric adaptive detection problem of range-spread targets in compound Gaussian sea clutter. Based on the generalized likelihood ratio test (GLRT), Rao test, and Wald test, three novel compound Gaussian detectors are developed. Specifically, the sea clutter is modeled as compound Gaussian with inverse Gaussian distribution, and the detectors are derived by the two-step maximuma posterior(MAP) procedures. In the first step, we assume the clutter covariance matrix (CCM) and the inverse Gaussian texture are known and derive the proposed detectors’ test statistics. In the second step, we use the persymmetric property and MAP criterion to estimate the CCM and inverse Gaussian texture parameters. Then the proposed detectors are proved to be constant false alarm rate (CFAR) detectors with respect to the real CCM. The simulation experiments are conducted by comparing the proposed detectors with their counterparts on both synthetic data and real sea clutter data. The numerical results indicate that the novel detectors exhibit better detection performance than their competitors.

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximuma posteriori(MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximuma posteriori(MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.

This letter presents the derivation of the generalized likelihood ratio test (GLRT)-based polarimetric detector in the non-Gaussian clutter. We model the non-Gaussian sea clutter as compound-Gaussian distribution with inverse-Gaussian texture (IG-CG), which has better goodness-of-fit for the high-resolution sea clutter. Based on the two-step GLRT criterion, we develop the test statistic of the proposed detector by assuming the texture component is known in the first step. In the second step, with the texture of the secondary data estimated as the power of the clutter, we insert the maximuma posterioriestimate (MAPE) of the texture of the primary data into the test statistic to achieve the fully adaptive detection. Furthermore, the model-based polarimetric detector, which exploits the independence between the co-polarized and the cross-polarized component, is derived. Finally, we conduct experiments with simulated clutter and real sea clutter to evaluate the performance of the proposed detector. The simulation results verify that the proposed detectors have better detection performance and the MAPE under different hypotheses leads to different levels of robustness to the mismatched signal.

This letter proposes a limited-memory receive filter design to maximize the output signal-to-interference-plus-noise ratio (SINR) of massive multiple-input multiple-output (MIMO) radar. The associated problem is a minimum variance distortionless response (MVDR) one which can be analytical solved, but the computation of the MVDR solution would be prohibitive in the context of massive MIMO radar, due to the huge memory consumption. To this end, we propose a low-complexity algorithm that exploits a reparameterization method to avoid the significant memory consumption, and solves the resulting problem with several simple algebraic operations. We show that the memory consumption of the proposed algorithm depends on the number of interference sources. Therefore, for the scenario with a large number of interference sources, we extend the proposed algorithm by the alternating direction method of multipliers (ADMM), which promotes a further memory reduction. Finally, numerical simulations are conducted to demonstrate the efficiency of the proposed algorithm.

This paper investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximuma posteriori(MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then the proposed detectors are proved to have the constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

This article investigates regularized estimation of Kronecker-structured covariance matrices (CMs) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex elliptically symmetric (CES) distributions with a Kronecker-structured CM. To obtain a well-conditioned estimate of the CM, we add penalty terms of Kullback–Leibler divergence to the negative log-likelihood function of the associated complex angular Gaussian (CAG) distribution. This is shown to be equivalent to regularizing Tyler’s fixed-point equations by shrinkage. A sufficient condition that the solution exists is discussed. An iterative algorithm is applied to solve the resulting fixed-point iterations, and its convergence is proven. In order to solve the critical problem of tuning the shrinkage factors, we then introduce two methods by exploiting oracle approximating shrinkage (OAS) and cross-validation (CV). The proposed estimator, referred to as the robust shrinkage Kronecker estimator (RSKE), is shown to achieve better performance compared with several existing methods when the training samples are limited. Simulations are conducted for validating the RSKE and demonstrating its high performance by using the IPIX 1998 real sea data.

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

This paper deals with the adaptive persymmetric detection problem in compound Gaussian sea clutter with inverse Gaussian texture. We derive a novel compound Gaussian detector based on the two-step generalized likelihood ratio test (GLRT). In detail, we assume the texture component and the clutter covariance matrix are known in the first step; Then we employ the maximum a posterior (MAP) and persymmetric property method to acquire the estimates of texture component and the clutter covariance matrix, respectively. The new detector is proved to be constant false alarm rate (CFAR) detector. The experiments that compare the proposed detector with the non-persymmetric detection are conducted by the simulated data and the measured data. The results reveal that the proposed detector outperforms the non-persymmetric detector in detection performance.

This paper deals with the adaptive target detection in compound Gaussian sea clutter with inverse Gaussian texture. The detectors based on two-step Rao and Wald criteria are derived, and both of the proposed detectors possess the property of constant false alarm rate (CFAR). Specifically, we assume that the texture parameter and clutter covariance matrix are known and derive the Rao and Wald tests' test statistics in the first step. Then we utilize the maximum a posteriori (MAP) method and fixed point covariance estimator (FPCE) to estimate the parameter of texture and clutter covariance matrix, respectively. Finally, we conduct the simulation experiments by comparing with its counterpart generalized likelihood ratio test (GLRT) using simulated data and real sea clutter data, i.e., IPIX 1998. The performance assessment results indicate that the proposed Rao detector outperforms the GLRT, and the proposed Wald detector holds better selectivity than GLRT.

This paper considers the best channel selection for reduced-dimension space-time adaptive processing (STAP) in the presence of multiple targets. An algorithm based on semidefinite programming (SDP) is proposed to achieve the best channel selection by optimizing the worst case of the detection performance of the different targets. Compared with some existing algorithms which only consider the single-target case, the proposed algorithm can provide considerable performance improvements for detecting several different targets. Simulations are conducted for validating the proposed method and demonstrating their high performance.

In this paper, the Multiple-input Multiple-output(MIMO) radar waveform design under constant modulus constraint is considered. The process of current transmit beampattern design is conducted by approximating the desired transmit beampattern and minimizing the cross-correlation sidelobes. This leads to hard optimization problem due to the nonconvexity of the objective function and constant modulus constraint. In this work, an efficient method is proposed to address those problem based on Majorization-Minimization(MM) method. Finally, we evaluate the performance of the proposed MM-based algorithm via numerical simulations.

As training samples are not always target-free in space-time processing for airborne radar, the traditional methods usually use the sample covariance matrix (SCM) as the test covariance matrix (TCM) to censor contaminated training samples. However, the SCM cannot represent the property of the cell under test (CUT) accurately, resulting in low selection efficiency. To deal with this problem, this letter proposes a novel training sample selection method based on covariance matrix whitening. Specifically, we utilize the reconstructed subaperture's clutter covariance matrix (RSCCM) of the CUT as the TCM. The RSCCM is only determined by the CUT and can characterize the CUT directly. Then, we use the RSCCM to whiten the subaperture's covariance matrix of the training sample. A criterion for selecting the training samples is derived based on the maximum eigenvalue of the whitened subaperture's covariance matrix, which is related to the energy of the outliers and more stable than the statistic of the generalized inner product method. Simulations are conducted to evaluate the performance of the proposed method.

This article considers the waveform design problem for airborne multiple-input-multiple-output (MIMO) radar systems with space-time adaptive processing (STAP). We choose the mutual information between the received signal and the target impulse response as the design metric to achieve enhanced detection performance under the influences from neighboring range cells. In order to solve the resulting optimization problem, we first decompose the objective function into two parts and consider their optimization individually. We optimize the first part by relaxing it using an upper bound and optimize the second part using a simple optimization procedure based on the majorization-minimization (MM) algorithm. The alternation direction method of multipliers (ADMM) framework is then adopted to derive the overall solution, where the MM algorithm is employed again to handle the fourth-order term of the waveform vector. Numerical results are provided to show that the proposed algorithm has better detection performance than that of the existing methods.

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

In this paper, a joint node selection and space-time resource allocation (JNSSTRA) strategy is proposed to address the resource management problem for multiple targets tracking (MTT) in the netted radar system (NRS). In order to minimize the total resource consumption of the NRS while guaranteeing predetermined tracking performance, a novel resource management optimization model is put forth, where the system resource consumption and the overall tracking performance of multiple targets are taken into consideration comprehensively. Based on the proposed model, the JNSSTRA algorithm for MTT in NRS is obtained. In the JNSSTRA algorithm, the system sampling period, the activated radar nodes of the NRS, and the transmit energy of each activated node can be adjusted adaptively. Finally, simulation results demonstrate the superiority of the proposed strategy to the space-only allocation and time-only allocation methods.

The sky-wave over-the-horizon radar is easily interfered with by radio frequency interference (RFI) since the high-frequency band is shared by many radio devices. Various RFI suppression techniques have been developed by both spatial and temporal processing. However, in practice, where the interference characteristic is usually unknown, it is still hard to evaluate the effects of interference suppression. In this letter, the Tamura coarseness is introduced as an indicator to evaluate the RFI and its suppression algorithms. By analyzing the range-Doppler maps with RFI, the relationship between coarseness and signal-to-interference-plus-noise ratio is established. An application example of coarseness is also given, in which the diagonal loading factor of adaptive beamformer is optimized. The analysis of coarseness and performance of proposed scheme are demonstrated by experimental data.

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

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

This paper deals with the grating notch cancellation problem in the uniform subarray configuration of airborne radar system. Grating notches, caused by the receive beamforming at the subarray level alone, make the main lobe distorted in the presence of signal-dependent interference. A transmit prefilter is designed to zero the radar radiant energy of the grating-lobe area to eliminate the grating notches. Numerical results verify the effectiveness of the proposed method.

This paper deals with the detection problem of airborne phased array radar in known and unknown prior spectrum knowledge scenarios. In the former case, several novel knowledge-aided (KA) detectors under the generalized likelihood ratio test (GLRT) framework are proposed, e.g., two detectors based on structured clutter covariance matrix (CCM) and two step least square (TSLS) algorithm without samples. We further present another two improved KA detectors on the basis of training data. In the latter case, we develop compressive sensing (CS) detectors, e.g., Bayesian compressive sensing (BCS) detector without using samples. We further propose block sparse Bayesian compressive sensing (BSBCS) detector with training data available. Finally, we compare the several proposed detectors with each other and numerical results indicate that the proposed detectors exhibit more significant performances than the traditional detector.

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

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

This paper investigates the problem of the spectrally compatible waveform design for multiple-in multiple-out (MIMO) radar transmit beampattern formation, subject to peak-to-average-power ratio (PAR) and waveform similarity constraints. Since the formulated optimization problem of minimizing the Integrate Sidelobe Level (ISL) and the waveform energy of the stop-band frequencies is NP-hard, an auxiliary variable is first introduced to modify the non-convex problem into a bi-quasiconvex problem, and then the approximated alternating directions method of multipliers (A-ADMM) algorithm is proposed to tackle the resulting bi-quasiconvex problem. Finally, numerical results are presented to evaluate the effectiveness of the proposed algorithm.

Exploiting Spatial and Doppler nulls is a straightforward way to construct a valid blocking matrix(BM) in the generalized sidelobe canceller(GSC)-based Space-time adaptive processing(STAP). However, spatial nulls of the airborne collocated MIMO radar are not uniformly distributed and their unknown frequencies remain to be estimated. In this work, we develop estimation rules of spatial null for the airborne collocated MIMO radars. The proposed rules provide closed-form solutions of both the number and frequencies of spatial nulls. By using the proposed estimated rules to construct the BM, the resulting GSC-based STAP processor exhibits near-optimum performance and outperforms the conventional estimation rules. Simulations are provided to verify the accuracy of the proposed estimation rule and demanstrate the effectiveness of its application to the GSC-based STAP.

A new method based on the matrices reconstruction is proposed to deal with coherent signals in the presence of the unknown mutual coupling. By using a novel expression of the spatial covariance matrix in the presence of mutual coupling, the interference‐plus‐noise covariance matrix and the desired signal covariance matrix can be reconstructed via estimating the autocorrelation matrix of the signal envelope with unknown mutual coupling in an iteration process. Based on the criterion of the maximum output signal‐to‐interference‐plus‐noise ratio, a subspace orthogonal to the interference subspace can be then found out by using these reconstructed matrices. Therefore, the desired signal and the noise can be let out by mapping this estimated subspace to the observed data. Finally, an optimal weight vector can be obtained by maximising the output power of the desired signal. The performance of the proposed method is quite close to the optimal beamforming. The simulations demonstrate the effectiveness of the proposed beamformer.

This letter proposes a new approach for knowledge-aided estimation of structured clutter covariance matrices (CCMs) in airborne radar systems with limited training data. First, we model the CCM in space-time adaptive processing (STAP) as a sum of low-rank Kronecker products. We then apply a permutation operation to convert the Kronecker factors into linear structures and propose a novel CCM estimation method under the maximum-likelihood framework. Employing a proximal gradient algorithm, the proposed method simultaneously exploits the knowledge about the clutter and the Kronecker structure of the CCM. We finally evaluate the performance of the proposed method using real data from airborne STAP.

The paper presents the average signal-to-clutter loss (SCRL) analysis for polarimetric space-time adaptive processing by exploiting the Kronecker structure of the clutter covariance matrix (CM). An expression for the average SCRL as a function of the mean square error of the corresponding CM estimator is derived. Based on that expression, one can determine how many samples are required in order to achieve a desired SCRL. The proposed average SCRL analysis methodology can be extended to more general scenarios, where closedform CM estimates are not available. Simulations indicate that even in the non-asymptotic regime, the proposed method can provide a good prediction of the average SCRL.

In this letter, we consider the problem of constant false alarm rate (CFAR) detection in heterogeneous scenarios. We assume that the covariance matrices of the training samples are different from that of the cell under test and employ prior statistical knowledge to describe the degree of heterogeneity. We derive an approximate expression of the average signal-to-clutter-noise ratio loss of the adaptive detector constructed with the maximum likelihood estimate of the covariance matrix obtained by solving the fixed-point equation. We validate the CFAR property of the detector and derive the asymptotic expression of the probability of false alarm. Exploiting such prior knowledge can significantly reduce the adverse effects of the heterogeneous training samples. Simulations validate the proposed theoretical results.

We address the problem of terrain-scattered jammer suppression in multiple-input multiple-output (MIMO) radar using space-(fast) time adaptive processing (SFTAP). The correlation function of jamming components after matched filtering at the receiving end of MIMO radar is derived, and its relationship to the correlation matrix of the transmitted waveforms is established. This correlation function serves as a theoretical measure of evaluating the matched filtering effect on the received jamming signals. We propose a minimum variance distortionless response (MVDR) type SFTAP design by taking into account the factors of waveform-introduced range sidelobes and cold clutter stationarity over different pulse intervals. A closed-form solution to this design is derived by means of the method of Lagrange multipliers. We also propose a relaxed SFTAP design by modifying the constraints of the MVDR type design. Both proposed SFTAP designs can support further slow-time Doppler processing procedure. Simulation results show the validity of our SFTAP designs.

The performance of high-frequency radars is known to suffer from transient interference and noise, such as cosmic noise, lightning, meteor echoes, man-made impulse bursts, etc. Traditional interference suppression methods need to detect the location of transient interferences before mitigating them, and they process the data of each range cell separately without exploiting the information of other range cells. On the other hand, the traditional methods cannot effectively suppress the weak transient interference and noise. In this letter, we propose a novel interference suppression method based on the supervised matrix completion. Unlike the traditional methods, the proposed method does not require the interference detection step and can use the prior information from other range cells. The proposed algorithm can suppress not only the interferences, including the strong and weak ones, but also the noise. Processing results of real data have demonstrated the effectiveness of the proposed method.

In this paper, the problem of joint hot and cold clutter mitigation in the context of transmit beamspace (TB)-based multipleinput multiple-output (MIMO) radar is studied. The TB-based MIMO radar enables special spatio-temporal structure and low rank of clutter covariance matrices. To efficiently mitigate the hot clutter such as terrain scattered multipath jamming concentrated in the sector-of-interest and the enhanced cold clutter due to transmit energy focusing, we resort to three-dimensional (3D) space-time adaptive processing (STAP) technique. A new 3D STAP method is proposed, which significantly reduces the computational complexity. We show from interference mitigation perspective that the TB-based MIMO radar enables superior output signal-to-interference-plus-noise ratio to that of its traditional MIMO radar counterpart.

Taking into account the existence of multipath ionospheric propagation (MIP), this paper develops the received signal model for a non-point target for multiple-input multiple-output skywave over-the-horizon (MIMO-OTH) radar for the first time. The model describes the ionospheric state, the number of propagation paths between a radar antenna and the target center, as well as the statistics of the reflection coefficients. It is shown that varying system parameters, such as antenna positions and signal frequencies, can result in causing the model to change from a case with highly correlated reflection coefficients to a case with virtually uncorrelated reflection coefficients. The proposed model is used to solve a target detection problem. It is shown that it is possible to exploit the MIP to improve the detection performance of the MIMO-OTH radar.

Ionospheric information is required when estimating target parameters in skywave over-the-horizon (OTH) radar. Unlike the traditional OTH radar which uses only the measurements of ionospheric parameters obtained from an ionosonde to estimate the target parameters, the multiple-input multiple-output skywave OTH (MIMO-OTH) radar studied in this paper estimates the ionospheric and target parameters jointly by exploiting the data received by both the ionosonde and the radar receivers. Two scenarios where the prior distribution of the ionospheric parameters is either known or unknown are considered. For the case when ionospheric parameter prior distribution is unknown, the joint maximum likelihood (JML) estimator is investigated and the Cramér-Rao bound (CRB) is derived. For the case when the ionospheric parameter prior distribution is known, the hybrid maximum likelihood and the maximum a posteriori (ML/MAP) estimator is studied and the hybrid Cramér-Rao bound (HCRB) is developed.

The time difference localization suffers from the performance deterioration of time delay estimation (TDE) due to the presence of clock frequency error in incoherent systems. Based on the modified signal model of TDE, the joint maximum-likelihood (ML) estimation of time delay and system clock frequency error is proposed. Then, the Cramér-Rao lower bounds (CRLBs) of time delay and clock frequency error estimations are given. The performance of time delay estimation may be significantly improved to approach CRLB by proposed method. Further, the accuracy of proposed time delay estimator is unaffected by performance of system clock in moderate condition, that is verified by simulation results.

We study the waveform design problem for a multiple-input multiple-output over-the-horizon (MIMO-OTH) radar system faced with a combination of additive Gaussian noise and signal dependent clutter. Considering the operational frequency of the MIMO-OTH radar is generally limited to a certain frequency band due to propagation and implementation issues, the waveform transmitted at each antenna is constructed as a weighted sum of discrete prolate spheroidal (DPS) sequences which have good orthogonal and band-limited properties. Optimum waveforms (possibly nonorthogonal) are designed to maximize the target detection performance of the MIMO-OTH radar system with the constraint of fixed total transmitted energy. The performance of the proposed waveforms is analyzed.

We consider a statistical multiple-input multiple-output (MIMO) radar system in which the transmit antennas are sparsely spaced to achieve spatial diversity while the receive antennas are closely spaced for direction finding. We consider the scenario where the transmitted signals are unknown. A two-step procedure is introduced to accomplish the localization of multiple targets and the estimation of both the fading matrix and the transmitted signals. In the first step, the angles are estimated using the approximate maximum likelihood algorithm. In the second step, the fading matrix and the transmitted signals are estimated using the blind signal processing techniques. The performance of the proposed procedure is validated by the simulation results.

To circumvent the dilemma of range and Doppler ambiguity in the conventional pulsed Doppler radars, a stepped time interval pulse train (STIPT) scheme is proposed in this letter. The STIPT approach eliminates range and Doppler ambiguities by setting the pulse interval difference appropriately. The range sidelobes are removed through a modified aperiodic correlation algorithm. Furthermore, the fast Fourier transform can be used in the Doppler processing. Consequently, the newly proposed approach can be applied to the conventional pulsed radar systems.

In this letter, we propose a modified Hough transform (HT) algorithm for radar detection by shifting the parameter space cells as well as exploiting the phase information of signal. The proposed modified HT brings (1) significant improvement of radar target detection at low signal-to-noise ratios and (2) complexity reduction of HT implementation in radar detection applications compared to the conventional HT-based methods. Simulation results are presented showing the performance gain obtained by the proposed modified HT over the conventional methods.

For original paper see H. Deng, ibid., vol.11, no.2, p.179-182, (2004). This comment points out some errors in the correlation calculations and conclusions in the literature. The ambiguity function and cross-ambiguity function for discrete frequency-coding waveform (DFCW) with arbitrary firing order are analyzed in that letter to corroborate the judgement. It shows that range autocorrelation sidelobe peak (ASP) for DFCW fluctuates around 0.21, the Doppler tolerance has sinc function form, while the range cross-correlation peak (CP) between two DFCWs is heavily varied with the different firing order. The correlation values are recalculated.