This article provides a comprehensive investigation into reconfigurable integrated sensing and communications (RISAC), an emerging paradigm designed to maximize the performance–cost tradeoff by exploiting the inherent spatial sparsity of multiple-input multiple-output (MIMO) arrays. Deviating from conventional static ISAC, RISAC adopts a cognitive “perception–action” cycle, enabling the adaptive reconfiguration of array geometries and beamforming weights in response to dynamic environmental feedback. Central to RISAC is the exploitation of two intertwined layers of degrees of freedom (DoFs): element-space sparsity via sparse MIMO array design and beam-space sparsity via hybrid beamforming (HBF). We further demonstrate that by synergistically co-designing these coupled DoFs, sparse MIMO HBF can rival the performance of fully digital systems when accounting for practical mutual coupling, at a significantly reduced hardware cost. This work analyzed both far-and near-field propagation regimes across various ISAC frameworks, including radar-centric, communication-centric, and joint co-design. In addition to downlink co-design, we also examine uplink coexistence via shared waveform design. Finally, this article outlines promising research directions, positioning RISAC as a critical evolution toward adaptive, hardware-efficient, and dual-functional systems.

Unmanned aerial vehicle (UAV) networks, which carry vital information, are prone to various attacks, and hence security issues are a major concern. In this paper, we design and implement a novel covert attack detection and secure control scheme, which operates between the UAV (the physical layer) and ground control station (GCS) (the cyber layer). Covert attacks can alter the UAV states, and yet cause the signals seen by the controller to appear unchanged, resulting in these attacks being more difficult to detect, and hence more dangerous compared to other types of attacks. To unmask the covert attacks, we construct and inject auxiliary signals to both the controller output and the UAV input. The auxiliary signals cause information of the attack to appear in the controller input, which is then fed to a detection observer to detect the attack. Next, we propose an integrated estimation and secure control scheme, comprising a reconstruction observer (which is a sliding mode observer (SMO)) that estimates the system states and attack signal, and an output-feedback controller that utilizes the estimated signals. We perform a series of transformations to the system, such that the design parameters of both reconstruction observer and secure controller are placed in a framework that is solvable using Linear Matrix Inequalities (LMIs). We also prove that the proposed integrated secure controller causes the output tracking errors to satisfy an${{\mathcal {H}}}_{\infty }$performance index. We also rigorously analyze the system performance, and present the necessary conditions for the scheme to be feasible. Finally, simulations are conducted to verify the effectiveness of the proposed scheme. Note to Practitioners—This paper presents a method to detect covert attacks in the UAV network, and to mitigate against those attacks. Covert attacks are more malicious since they are difficult to detect. The proposed method in this paper consists of auxiliary signal injection and a detection observer that will expose and detect the attacks, and a reconstruction observer and secure controller that will estimate the attacks and mitigate its effect on the plant. The controller and observers are designed using Linear Matrix Inequalities to minimize the${{\mathcal {H}}}_{\infty }$gain from the attack on the plant performance. In addition, this paper also investigates the conditions that the plant must satisfy such that the proposed scheme is feasible, and presents them in an easily verifiable form. Finally, the paper also analyses the performance of the proposed scheme in all scenarios - namely before the attack occurs, when the attack occurs but is not yet detected, and after the attack is detected.

Integrated sensing and communications (ISAC) is emerging as one of the six application scenarios for future wireless networks. Characterizing the Pareto boundary is an urgent issue in multiple-input multiple-output (MIMO) ISAC systems. The lack of unified sensing metrics and the neglect of the instantaneous worst-case sensing requirement in the existing works present challenges to this issue. In this paper, we propose a more universal and operable theoretical limit analysis framework where the high-signal-to-noise ratio (SNR) channel capacity is characterized under instantaneous covariance mismatch constraint. We use the covariance mismatch that implies the distance to optimal covariance as the sensing metric. The optimal covariance can be computed by optimizing any key sensing metric. An MIMO ISAC Pareto boundary can be obtained by computing channel capacity under fine-grained sensing thresholds, below which the mismatch must be constrained. In the experiments, three radar modes are considered, and the results show that different radar modes affect capacity performance and a trade-off exists between communication and sensing. In addition, pure communication capacity is the upper bound of the communication capacity in ISAC. Moreover, capacity under instantaneous constraint approaches that under average one in pure MIMO communications when signal length approaches infinity.

Joint sensing and communication (JSAC) has emerged as a promising technology in autonomous driving, as it allows simultaneous road sensing and two-way communication using a single shared platform. Meanwhile, intelligent reflective surface (IRS) enables sensing enhancement and communication with targets in a blind zone. In this paper, we propose an IRS-assisted JSAC design to address two issues of the limited sensing range of automotive radar and the likely occlusion among road targets. We co-design the IRS’ reflection coefficient vector to steer the beam towards the directions of radar targets as well as embed the communication symbols into the reflected signals. Considering the phase-only property of the passive IRS, we establish a constant modulus co-design problem. We seek to optimize the covariance matrix first and then obtain the optimal reflection coefficient vector via matrix decomposition. Subsequently we transform the constant modulus constraint into a rank-1 semidefinite programing (SDP) problem and solve it iteratively. Simulation results demonstrate the effectiveness of the proposed IRS-assisted JSAC design.

The instantaneous velocity of any moving object can be decomposed into two orthogonal components with reference to the observing radar, namely, radial velocity along the radar line of sight (LoS) and transversal velocity perpendicular to the LoS. It has been shown that the measurement of transversal velocity can significantly improve the performance of both radar target tracking and classification. Furthermore, the precision of transversal velocity estimation is proportional to the baseline length using static interferometry. However, the large baseline is impractical in applications, such as automotive radar with restrictions on the packaging size. This letter proposes synthetic interferometry exploiting radar motions. A large virtual baseline can be synthesized by moving the side-looking radar and synchronizing the received signals at two locations, thus improving the accuracy of transversal velocity measurement. We derive the conditions of time synchronization for successful interferometry in terms of the maximum moving distance and the maximum observation time. Both simulations and experiments have been conducted to validate the feasibility and effectiveness of the proposed synthetic interferometry.

This letter proposes a novel window function design to synthesize asymmetric beampatterns while minimizing the gain loss for clutter/interference suppression as required in practical applications, such as airborne phased array radar (APAR). Compared with beampattern synthesis in terms of designing the weight vector directly, the proposed method can achieve asymmetric sidelobes for multiple steering directions resort to designing one window while circumventing repetitive computations. To tackle the resultant non-convex optimization problem, an iterative alternating optimization (IAO) algorithm is introduced where each iteration involves multiple subproblems that can be solved via either convex approximation (CA) approach or closed-form solutions. Finally, numerical simulations are provided to verify the effectiveness of the proposed method comparing with the classical adjustable window functions (CAWFs).

This paper proposes a novel active fault tolerant control (FTC) scheme for a 3-degree-of-freedom (3-DOF) helicopter with sensor faults. As a challenge, only attitude angles are considered available, so that when the sensors measuring the elevation/travel angles are faulty, the system with respect to the remaining healthy outputs is not detectable. To circumvent this issue, a new interval observer (IO) with adaptive parameters is formulated, providing good estimates of both disturbances and unmeasurable states. This IO acts not only as a state estimator for nominal controller but also as a fault detection and isolation (FDI) observer for the fault occurrence and location. After the fault location is determined, two different fault estimation (FE) schemes are developed according to whether or not the system is detectable. Using the fault estimates, a fault tolerant controller is constructed to ensure the acceptable performance of the faulty system. Finally, experiments on the 3-DOF helicopter platform are conducted to verify the effectiveness of the proposed scheme.

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting conditions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed.

Massive multi-input-multi-output (MMIMO) cognitive radar can enhance the target detection ability in a dynamic environment via a continuous "perception-action" cycle. In our previous work, we proposed a reinforcement learning (RL) based approach for multi-target detection in MMIMO. However, this method shows poor detection performance for weak targets attributed to its imperfect action and reward mechanisms. In this paper, we propose an improved RL based method to enhance the detection probability of weak targets. In the action stage, the transmit power is divided into omni-directional and directional components, the former significantly reduces the missed detection probability of weak targets and the latter improves the detection probability by focusing more power on weak targets. Moreover, the reward mechanism of RL is modified to further improve the detection performance. In addition, the transmit weight matrix is designed by an optimum combination of the beampatterns of all unit orthogonal transmit waveforms, thus greatly reducing the computational complexity. Simulation results are provided to demonstrate the effectiveness of the improved RL based method for weak target detection.

Beampattern synthesis of frequency diverse arrays (FDAs) has recently raised increased attention attributed to their range-dependent beampattern. The transmit beampattern of uniform FDA appears$S$-shaped, which implies coupling in the range-angle domain and thus causing unwanted energy leakage into the area of non-interest. In this work, we propose a joint optimization of sparse FDAs to synthesize a decoupled transmit beampattern from the perspective of spatial-frequency virtual array. Specifically, both spatial and spectral configuration of FDAs are optimized via joint antenna-frequency selection. In order to solve the resultant NP-hard combinatorial optimization problem, we propose an iterative reweighting strategy to transform the original problem into a convex optimization. Further, we proceed to synthesize a time-invariant decoupled beampattern by designing a time-varying unit frequency step. Comparative simulations are provided to manifest the superior performance of the proposed FDA in the metric of normalized peak sidelobe level (NPSLL) of transmit beampatterns.

In this paper, we investigate the signaling strategy of communications embedding in frequency-hopping (FH) multiple input multiple output (MIMO) radar. Previous work that embeds communication symbols into the emission of MIMO radar with orthogonal FH waveforms via phase modulation, and waveform orthogonality compromises the transmit processing gain of the radar. Yet, the directional transmit pattern can be maintained via an appropriate design of the transmit beamforming weight vector associated with each orthogonal waveform. Moreover, communication symbols can be embedded into the complex transmit beampattern via both amplitude and phase. In this paper, we propose two extended signaling strategies, which fully employs the flexibility of complex beampattern in tandem with spatial modulation, to combine the merits provided by waveform diversity and transmit beamforming. As a result, the communication data rate is significantly increased, while directional transmit gain is simultaneously preserved. In order to permit the complex beampattern to be varied in accordance with communication symbols, we also propose a new approach to the beamformer design which circumvents the computationally-consuming optimization. Simulation results demonstrate the effectiveness of the proposed dual-function signaling strategies.

The performance of direction of arrival (DOA) estimation using antenna arrays is fundamentally limited by the Cramér-Rao bound (CRB), which is intimately tied to the array configuration. In systems where a subset of antennas can be selected from a larger array, the array configuration can be recruited to enhance the DOA estimation performance by choosing the optimal subarray that minimizes the CRB. This strategy has been demonstrated to provide performance improvements, albeit at a substantial computational cost. Here, we leverage the power of unsupervised learning to reduce the computational cost of antenna selection for enhanced DOA estimation resulting in a practical online implementation of the array reconfiguration. We formulate the changing DOA estimation problem as a game in the context of online convex optimization, and employ a gradient-based technique that makes a move at each step in order to minimize the total loss after T steps. We compare the performance of the proposed method with the exhaustive search and a Dinkelbach-type algorithm that provides an approximate solution. We show that the proposed method is able to provide a solution that is close to the exhaustive search at a fraction of the computational time, thus permitting online implementation of the selection strategy.

Sparse array design aided by emerging fast sensor switching technologies can lower the overall system overhead by reducing the number of expensive transceiver chains. In this paper, we examine the active sparse array design enabling the maximum signal to interference plus noise ratio (MaxSINR) beamforming at the MIMO radar receiver. The proposed approach entails an entwined design, i.e., jointly selecting the optimum transmit and receive sensor locations for accomplishing MaxSINR receive beamforming. Specifically, we consider a colocated multiple-input multiple-output (MIMO) radar platform with orthogonal transmitted waveforms, and examine antenna selections at the transmit and receive arrays. The optimum active sparse array transceiver design problem is formulated as successive convex approximation (SCA) alongside the two-dimensional group sparsity promoting regularization. Several examples are provided to demonstrate the effectiveness of the proposed approach in utilizing the given transmit/receive array aperture and degrees of freedom for achieving MaxSINR beamforming.

Adaptive beamforming of large antenna arrays is difficult to implement due to prohibitively high hardware cost and computational complexity. An antenna selection strategy was utilized to maximize the output signal-to-interference-plus- noise ratio (SINR) with fewer antennas by optimizing array configurations. However, antenna selection scheme exhibits high degradation in performance compared to the full array system. In this paper, we consider a reduced-dimensional beamspace beamformer, where analogue phase shifters adaptively synthesize a subset of orthogonal beams whose outputs are then processed in a beamspace beamformer. We examine the selection problem to adaptively identify the beams most relevant to achieving almost the full beamspace performance, especially in the generalized case without any prior information. Simulation results demonstrated that the beam selection enjoys the complexity advantages, while simultaneously enhancing the output SINR of antenna selection.

The problem of optimum sparse array beamformer design to maximize output signal-to-interference-plus-noise ratio (SINR) in the case of multiple narrowband sources was recently investigated. This was based on seeking both optimum sensor placement as well as optimum a single beamformer for all sources in the array field of view. In this paper, we consider multiple beamformers with a common sparse array. That is, we deal with a more prevalent case in radar and communications where each source is assigned its own beam. This could be the case for both switched and simultaneous or staring beams. The paper considers optimum sparse array design for both narrowband and wideband sources. Analysis and simulation examples demonstrate that the optimum sparse array configuration depends on both the arrival angle and the frequency of the incoming signal and it plays a vital role in determining the performance of multiple beamformer receivers.

The sparse array design for adaptive beamforming has been recently formulated into combinatorial antenna selection problems, which belong to notorious NP-hard problems. As the commonly deployed convex relaxation algorithms are susceptible to local optima, several trials with different initial points are conducted for the global optima. Moreover, the high computational load of optimization techniques prohibits the real-time adaptive array reconfiguration. In this work, we propose to utilize machine learning algorithms, specifically support vector machine (SVM) and artificial neural network (ANN), for solving combinatorial antenna selection problems. Numerical examples are presented to validate the effectiveness and efficiency of machine learning algorithms for sparse array design. Moreover, the SVM based antenna selection is robust against DOA estimate uncertainties.

In this paper, we consider optimum array configurations for multiple satellite signals in interference-free environment. The two measures of maximum output signal-to-noise ratio (SNR) and equal gains towards all sources incident on the array are considered for the array design. As it is computationally exhaustive to enumerate all configurations and implement eigenvalue decomposition to compare respective maximum eigenvalues, we resort to the relaxation of maximizing the lower bound of the output SNR. Subsequently, an iterative linear fractional programming method is proposed to maximize the spectral norm of the source covariance matrix. Simulation examples confirm that the array configuration plays a vital role in determining the array processing performance in interference-free scenarios. The selected optimum subarrays achieve maximum performance preservations with a dramatically reduced cost.

This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio-temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.

In this paper, we consider antenna selection and array reconfiguration in the presence of multiple interferences based on the spatial correlation coefficient (SCC) which characterizes the spatial separation between the desired signal and interference subspace. Minimizing the SCC increases the separation between these two subspaces and leads to enhanced beamforming performance. We formulate this problem as a difference of two concave functions, which we solve through the convex-concave procedure (CCP). We derive the lower bound of the SCC as a function of the number of selected antennas which permits us to determine the required number for achieving the desired performance. We suggest two algorithms for implementing the antenna selection and present simulation results to validate the effectiveness of the proposed strategy.

In this paper, we utilize Bayesian Compressive Sensing (BCS) for direction-of-arrival (DOA) estimation based on the coarray. This enables estimation of more sources than the number of physical antennas. We adopt the covariance vectorization technique to construct the received signal vectors of coarrays for both fully and partially augmentable arrays. We then apply the single measurement vector BCS (SMV-BCS) for DOA estimation. Supporting simulation results for both sparse linear arrays and circular arrays demonstrate the effectiveness of the proposed approach in terms of high resolution and estimation accuracy compared to the MUSIC and sparse signal reconstruction based methods.

Antenna array configurations play an important role in direction of arrival (DOA) estimation. In this letter, performance enhancement of DOA estimation is achieved by reconfiguring the multi-antenna receiver through an antenna selection strategy. We derive the Cramer-Rao Bound (CRB) in terms of the selected antennas and associated subarray for both peak sidelobe level (PSL) constrained isotropic and directional arrays in single source cases. Since directional arrays are angle dependent, a Dinklebach type algorithm and convex relaxation are introduced to maintain the optimum selection by adaptively reconfiguring the directional subarrays using semi-definite programming. Simulation results validate the effectiveness of the proposed antenna selection strategy.

Space-time adaptive processing (STAP) is an effective strategy for clutter suppression in airborne radar systems. Limited training data, high computational load and the heterogeneity of training data constitute the main challenges in STAP. In this letter, we propose a new detection strategy based on selecting an optimum subset of antenna-pulse pairs associated with maximum separation between the target and the clutter trajectory. The proposed strategy reduces redundancy while addressing the above three interlinked challenges for detecting slow-moving targets especially in heterogeneous cases. An iterative Min-Max algorithm is proposed to solve the antenna-pulse selection problem, which is NP-hard combinatorial optimization. Extensive simulation results confirm the effectiveness of the proposed strategy.

The configuration of an antenna array plays a fundamental role in the ability of array signal processing to mitigate interference. We propose in this paper a novel reconfigurable adaptive linear array scheme to overcome the drawbacks of traditional array processing. We employ the effective carrier to noise density ratio (C/N0), which is a reliable measure of the performance in the Global Navigation Satellite Systems (GNSS) applications, expressing its dependence on the spatial separation through the Spatial Correlation Coefficient (SCC), with a lower SCC giving better interference mitigation performance. We then formulate the problem of determining the optimal orientation of a linear array in terms of the minimization of SCC. Simulation results show that the proposed method is effective in reducing the SCC and improving the effective C/N0. Finally, we propose a practical implementation where the array orientation is chosen from a number of present orientations.