This work explores the relationship between sensing accuracy and precoding coefficients for edge artificial intelligence (AI) inference in integrated sensing, communication and computation (ISCC) networks. We start by constructing a system model of an over-the-air-empowered ISCC network for edge AI inference, involving distributed edge sensors for feature extraction and an edge server for classification. Based on this model, we introduce a discriminant gain (DG) to characterize sensing accuracy and novelly derive an explicit function of the DG about precoding coefficients, giving valuable insights into precoding design. Guided by this, we propose an effective precoding algorithm to solve a non-convex DG-maximization problem. Simulation results demonstrate that the proposed design achieves up to$15\%$and$10\%$sensing accuracy improvements on synthetic and real-world datasets, respectively, over the conventional scheme at low SNR, thereby validating its effectiveness and superiority for edge AI inference in ISCC networks.

This paper proposes a cooperative cell-free integrated sensing and communication network (CoCF-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are three-fold. First, we introduce a CoCF-ISACNet with energy-efficient HBF architecture. To show the benefits of proposed CoCF-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem. Second, to facilitate the joint design, we propose a novel distributed optimization framework called Proximal grAdieNt Decentralized Alternating direction method of multipliers (PANDA). Third, we further adopt the proposed PANDA framework to solve the joint HBF design problem for the CoCF-ISACNet. By using the proposed PANDA framework, all access points (APs) optimize the HBF in parallel, where each AP only requires local channel state information and limited message exchange among the APs. Such framework reduces significantly the computational complexity and thus has pronounced benefits in practical scenarios. Simulation results verify the effectiveness of the proposed algorithm compared with the conventional centralized algorithm and show the remarkable performance improvement of radar sensing and communication by deploying CoCF-ISACNet.

In recent years, security has emerged as a critical aspect of integrated sensing and communication (ISAC) systems. While significant research has focused on secure communications, particularly in ensuring physical layer security, the issue of sensing security has received comparatively less attention. This paper addresses the sensing security problem in ISAC, particularly under the threat of multi-intercept adversaries. We consider a realistic scenario in which the sensing target is an advanced electronic reconnaissance aircraft capable of employing multiple signal interception techniques, such as power detection (PD) and cyclostationary analysis (CA). To evaluate sensing security under such sophisticated threats, we analyze two critical features of the transmitted signal: (i) power distribution and (ii) cyclic spectrum. Further, we introduce a novel ergodic cyclic spectrum metric which leverages the intrinsic mathematical structure of cyclostationary signals to more comprehensively characterize their behavior. Building on this analysis, we formulate a new ISAC design problem that explicitly considers sensing security, and we develop a low-complexity, efficient optimization approach to solve it. Simulation results demonstrate that the proposed metric is both effective and insightful, and that our ISAC design significantly enhances sensing security performance in the presence of multi-intercept threats.

This paper deals with the security problem of covert transmission in integrated sensing and communication (ISAC) systems, which aims to probe a target while simultaneously transmitting confidential signals to a covert communication user in the presence of multiple public communication users. Specifically, the target angular location is unknown and random, and among the public users, there is a warden hostile to the covert user. To realize covert transmission and target localization, we propose a covert beamforming strategy by maximizing the covert rate subject to public communication quality-of-service, transmission covertness, direction-of-angle (DoA) estimation, and power constraints. To tackle the formulated non-convex design problem, we devise a covert beamforming algorithm based on the alternating direction method of multipliers. Simulation results verify the effectiveness and feasibility of the proposed ISAC design in transmission security and DoA estimation.

This work focuses on the synergy of physical layer covert transmission and millimeter wave (mmWave) integrated sensing and communication (ISAC) to improve the performance, and enable secure internet of things (IoT) systems. Specifically, we employ a physical layer covert transmission as a prism, which can achieve simultaneously transmitting confidential signals to a covert communication user equipment (UE) in the presence of a warden and regular communication UEs. We design the transmit beamforming to guarantee information transmission security, communication quality-of-service (QoS) and sensing accuracy. By considering two different beamforming architectures, i.e., fully digital beamforming (FDBF) and hybrid beamforming (HBF), an optimal design method and a low-cost beamforming scheme are proposed to address the corresponding problems, respectively. Furthermore, building on the previously derived algorithm, two robust variants are proposed to address the more challenging case where the warden’s CSI is imperfect. Numerical simulations validate the effectiveness and superiority of the proposed FDBF/HBF algorithms compared with traditional algorithms in terms of information transmission security, communication QoS and target detection performance.

The orthogonal frequency division multiplexing (OFDM) sequences are widely used in 4 G, 5 G and integrated sensing and communication (ISAC). In this paper, we address the challenge of designing orthogonal OFDM sequences. The weighted sum of the auto-correlation and cross-correlation is minimized. Considering the base station (BS) hardware constraints, the length of OFDM sequence in time domain and frequency domain is inconsistent. Furthermore, we consider the limitation of peak to average power ratio (PAPR). To solve the non-convex problem, we have developed an efficient alternating direction method of multipliers (ADMM) algorithm. Numerical simulations confirm the effectiveness of the proposed algorithm.

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

This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions. In contrast to most of the existing RIS aided DFRC works where the RIS is modeled as a diagonal phase shift matrix and can only reflect signals to half space, we propose a novel beyond diagonal RIS (BD-RIS) aided DFRC system. Specifically, the proposed BD-RIS supports the hybrid reflecting and transmitting mode, and is compatible with flexible architectures, enabling the system to realize full-space coverage and to achieve enhanced performance. To achieve the expected benefits, we jointly optimize the transmit waveform, the BD-RIS matrices, and sensing receive filters, by maximizing the minimum signal-to-clutter-plus-noise ratio for fair target detection, subject to the constraints of the communication quality of service, different BD-RIS architectures and power budget. To solve the non-convex and non-smooth max-min problem, a general solution based on the alternating direction method of multipliers is provided. Numerical simulations validate the efficacy of the proposed algorithm and show the superiority of the BD-RIS aided DFRC system in terms of both communication and sensing compared to conventional RIS aided DFRC.

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.

In this paper, we consider the problem of direction of arrival(DOA) estimation with one-bit quantized datas. Based on the existing one-bit multiple signal classification(MUSIC) algorithm, this paper gets datas form a uniform rectangular array(URA) then lets datas quantized by one-bit Analog to Digital Converters(ADCs) and introduces a switching matrix. The covariance matrix of one-bit datas is reconstructed by conjugation and product with the switching matrix, and then added to itself to obtain a new one-bit covariance matrix. This paper proposes an improved MUSIC algorithm based on the new covariance matrix, which is the one-bit modified multiple signal classification(MMUSIC) algorithm, and proves in simulation that when the number of snapshots is small, the signal-to-noise ratio is small, and the number of arrays is appropriate, the angle measurement performance of the one-bit MMUSIC algorithm is better than the existing one-bit MU-SIC algorithm, and the Cramer-Rao Bound(CRB) is given as a reference lower bound for the angle measurement performance.

The over-the-horizon (OTH) radar systems are seriously affected by sea clutter. The precise estimation of sea clutter covariance (SCC) plays an important role in suppressing the sea clutter, especially in the case of limited samples. Towards this end, this letter proposes a robust dictionary learning (DL) based on the SCC reconstruction method to suppress sea clutter with a single observation sample. The DL procedure is formulated as a non-convex optimization problem, which is known to be NP-hard. To address this problem and reconstruct the SCC effectively, we propose a novel alternating proximal method designed for solving ℓ0-norm based problems. Extensive analyses demonstrate that our proposed algorithm exhibits global convergence with a sub-linear convergence rate. Through simulation results, we verify the effectiveness of our proposed algorithm, demonstrating a notable performance improvement compared to conventional methods using single samples. Additionally, we conduct experiments using real sea clutter datasets, further demonstrating the practical applicability of our method.

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.

Doppler ambiguity in missile-borne radar is usually caused by the low pulse-repetition-frequency (PRF) waveforms, which may result in a significant performance loss of target detection and location in the presence of clutter. To solve the issue of Doppler ambiguity clutter in missile-borne space-time adaptive processing (STAP) radar, this paper proposes a novel clutter suppression approach with the aid of designing the pulse interval (PI) of the waveform. Specifically, we formulate the optimization problem by considering the metric of minimizing the maximum sidelobe level in the Doppler domain, subject to the constraint of a given coherent processing interval (CPI).To solve the complicated problem, the modified genetic algorithm combining the simulated annealing algorithm (MGA-SA) is devised. Extensive simulations showcase that the proposed method surpasses SCNR by over 3dB in clutter suppression effectiveness when compared to conventional techniques, all while maintaining the same dwell time and frequency resources.

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.

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 investigates the RadCom system with multiple input multiple output (MIMO) structure under mutual spectrally crowded environment, where the radar system acts as the primary function, while the communication system is the secondary function. An alternative formulation aims at maximizing signal-to-interference-plus noise ratio (SINR) with multiple constraints to control the peak-to-average power ratio (PAR), spectrally compatibility and transmit power. The design degrees of freedom are the space-time waveform for radar and communication system. Then we develop an optimal scheme that tackles the original non-convex formulation into a series of sub-problems with alternating iteration, Taylor expansion, and the double alternating direction method of multipliers (DADMM) design. Numerical examples are performed to assess the merits of the derived solutions.

In the symbol-level precoding (SLP) based wireless systems, the reconfigurable intelligent surface (RIS) is usually configured on a block level, which causes a mismatch to the SLP design in terms of update rate. Although it is expected that updating both the RIS and precoding on the symbol level could boost the system performance, the requirements for synchronization and system overhead will become demanding inevitably. In this paper, we consider the RIS-aided dual-function radar-communication (DFRC) system and investigate the benefit of increasing the RIS updating frequency. We jointly design the SLP and RIS to maximize the target illumination power while satisfying the power budget and the multiuser multiple input single output (MU-MISO) communication quality of service (QoS) requirements, where the RIS is updated multiple times in a block (at a sub-block level). Through the simulation results, we demonstrate the optimized trade-off between system performance and RIS update rate.

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bEamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

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.

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

Abstract 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.

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

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

In this paper, 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.

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.

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.