Chenguang Shi

Low probability of interception (LPI) technology is critical for the self-protection of dual-function radar-communication (DFRC) systems in hostile environments. However, the range-dimensional uncontrollability of the beampattern imposes a bottleneck limitation on LPI development. This paper investigates a transceive beamforming for frequency diverse array (FDA)-multiple-input multiple-output (MIMO)-based DFRC systems. Initially, we innovatively construct the signal model for the FDA-MIMO DFRC system, which surmounts the inseparability between frequency offsets and transmitting antennas. Subsequently, the equivalent transmitted beampattern and communication channel are derived into two-dimensional range-angle expressions, aiming to implement joint range-angle beamforming. The communication symbols are strategically placed in predetermined range-angle positions of each user through the beampattern modulation technique. The LPI optimization problem is formulated as the minimization of transmitting beamforming power, subject to the constraints of radar SINR and communication sum-rate. To tackle the non-convex problem with high-dimensional nonlinear constraints, a distributed alternating algorithm is developed for fast solving, involving sequence convex approximation (SCA), weighted mean-square error minimization (WMMSE), and alternating direction method of multipliers (ADMM). Simulation results demonstrate that our method achieves punctate multi-target detection and multi-user communication in two-dimensional range-angle planes, enormously facilitating the LPI capability.

This paper deals with the dual function waveform design problem for a knowledge-aided integrated radar and jamming (IRAJ) system. Supposing the IRAJ system has access to an information database obtained from a reconnaissance system for the threatening radar’s transmit waveform knowledge, the signal-to-jamming and noise ratio (SJNR) at the output of the threatening radar filter is considered as a figure of merit to reduce its detection probability, which represents the performance of blanket jamming. Besides, along with energy and peak-to-average ratio (PAR) constraints to comply with the hardware realization, let us minimize the weighted peak sidelobe level (WPSL) or maximize the signal-to-interference and noise ratio (SINR) of the IRAJ system for the detection performance according to the delay structure of reflected echoes. To tackle the resulting nonconvex multi-objective optimization problems, iterative fractional programming algorithms (IFPA) leveraging cyclic algorithmnew (CAN) and alternating direction method of multipliers (ADMM) are proposed, respectively. Finally, simulation results are provided to demonstrate the competition between radar and jamming functions within the proposed Pareto optimization framework and validate the effectiveness of the conceived algorithms.

How to efficiently schedule the transmit beams and power resources in colocated multiple-input-multiple-output (C-MIMO) radar network to achieve better multi-target tracking (MTT) accuracy is a critical research issue. However, in electronic countermeasure environments, excessive resource allocation may lead to the interception of transmit pulses by hostile receivers. As the number of intercepted pulses increases, the transmit signals of C-MIMO radar nodes would be identified and subject to precise suppression jamming, resulting in a sharp degradation in MTT performance. To overcome this shortcoming, we propose a low probability of identification (LPID)-based composed resource allocation (CRA) scheme for MTT in C-MIMO radar network. Closed-form expressions for the predicted conditional Cramér-Rao lower bound (PC-CRLB) and the radar signal identification probability under suppression jamming are derived. Based on the tactical significance map (TSM) function, we compute the comprehensive threat level for each target in real time. We then employ the sum of products between PC-CRLB values and threat levels, and the worst-case identification probability as the quantitative metrics for MTT performance and LPID performance, respectively. The LPID-CRA is developed to jointly balance these two metrics by dynamically allocating beam-pairing and power resources within each tracking frame. To address the resulting non-convex non-linear problem, we perform an equivalent transformation of the optimization model and develop an efficient five-stage solution method that incorporates the Zoutendijk methods of feasible directions (ZMFD) and the min-cost flow algorithm. Simulation results demonstrate that the proposed LPID-CRA strategy significantly enhances overall system performance.

This article investigates the problem of joint transmit and receive beamforming for a colocated multiple-input multiple-output radar network with multiple targets and signal-dependent interferences. At first, a distributed beamforming design scheme is developed using noncooperative game theory. The basis of this scheme is to minimize the maximum transmit power of antennas for each radar while adhering to the constraint of signal-to-interference-plus-noise ratio for the targets. By transforming the formulated optimization problem into a convex problem, the Lagrangian duality theory is utilized to decompose the original problem into two sub-problems. Then, a subgradient projection method and an equivalent receive beamforming optimization method are developed to tackle the two sub-problems, yielding the optimal transmit beamformers for the radars. After obtaining the transmit and receive beamformers iteratively, the game relationship among radars is then formulated as a power control game, and the paper proves the existence and uniqueness of the Nash equilibrium. Furthermore, a centralized beamforming design problem is explored for comparative analysis, where the beamformers of all radars are jointly optimized to suppress power fluctuations among transmit antennas. Simulation results are provided to validate the effectiveness of the proposed schemes.

In this study, a low probability of intercept (LPI)- based robust node selection and power allocation (RNS-PA) scheme is proposed for multiple targets tracking (MTT) within a colocated multiple-input-multiple-output (C-MIMO) radar network. The core of the RNS-PA scheme is to adaptively coordinate the radar-to-target assignment and transmit power to improve the LPI performance of the radar network, subject to the MTT accuracy requirements and system resource budgets. Unlike the existing resource-aware design algorithms that prioritize total resource consumption as the optimization objective (commonly resulting in decreased resource utilization and suboptimal solutions), we employ the closed-form expression for the worst-case probability of the C-MIMO radar network being intercepted by hostile targets as the optimization criterion. It is shown that the RNS-PA scheme is formulated as a non-linear and non-convex optimization problem. By evaluating whether all targets can satisfy the predefined tracking accuracy demands, the original problem is transformed into two subproblems: (1) accelerating the reduction of MTT error under a given interception probability threshold (when the original problem is infeasible), and (2) minimizing the worst-case interception probability subject to the MTT accuracy threshold constraints (when the original problem is feasible). We verify that the interception probability constraint forms a convex set and then propose a fast, comprehensive solution method to tackle the optimization problem efficiently. Simulation results verify the robustness and effectiveness of the proposed RNS-PA scheme.