To enhance air defense efficiency in a Distributed Multiple Input Multiple Output (D-MIMO) radar sensor network (RSN) for detecting low-altitude targets, this paper introduces a cooperative game theoretic power allocation (CGTPA) strategy within power budget constraints. The CGTPA mechanism involves precise power management to effectively mitigate variations and fading in the received signal caused by multipath effects. Thereby, it can capitalize on the abundant scattering characteristics within a multipath environment to achieve enhanced target detectability. The paper introduces the concept of multipath distance difference (MDD) by incorporating errors from space-based external information, and subsequently derives the Neyman-Pearson based detection probability and signal-to-interference-plus-noise ratio (SINR). Further, SINR is utilized as the metric for RSN target detection performance, and an optimization model is established based on the Max-Min criterion. Considering the non-convex characteristic of the optimization model, cooperative game theory provides a vivid and flexible framework for collaborative detection among intelligent antennas in the RSN. The existence and uniqueness of the core are analyzed and reasoned. Furthermore, the Shapley values, derived based on the fairness principle, are utilized to address the cooperative game power allocation problem. Weighted graph techniques are employed to simplify computational complexity, resulting in a power allocation scheme that adheres to Pareto optimality and fairness principles. Numerical results validate our theoretical analysis and the effective performance of the proposed algorithm, which approaches optimal RA method with significant instantaneity.

Radar network configuration and power allocation are of great importance in military applications, where the entire surveillance area needs to be searched under resource budget constraints. To pursue the joint antenna placement and power allocation (JAPPA) optimization, this paper develops a JAPPA strategy to improve target detection performance in a widely distributed multiple-input and multiple-output (MIMO) radar network. First, the three variables of the problem are incorporated into the Neyman–Pearson (NP) detector by using the antenna placement optimization and the Lagrange power allocation method. Further, an improved iterative greedy dropping heuristic method based on a two-stage local search is proposed to solve the NP-hard issues of high-dimensional non-linear integer programming. Then, the sum of the weighted logarithmic likelihood ratio test (LRT) function is constructed as optimization criteria for the JAPPA approach. Numerical simulations and the theoretical analysis confirm the superiority of the proposed algorithm in terms of achieving effective overall detection performance.

The realization of anti-interference technologies via beamforming for applications in frequency-diverse arrays and multiple-input and multiple-output (FDA-MIMO) radar is a field that is undergoing intensive research due to its two-dimensional range-angle-dependent beampattern characteristics. To solve the missing covariance matrix problem and improve the anti-interference capability of FDA-MIMO radar, we present a two-stage based intelligent anti- interference scheme for FDA-MIMO radar under the nonideal condition. The scheme consists of two parts: signal covariance matrix missing data recovery and intelligent beamforming vector estimation. A dual-channel generation adversarial network (DC-GAN) structure is proposed to effectively recover both real and imaginary parts of data from a covariance matrix. Based on the recovered covariance matrix, the beamforming vectors are accurately estimated by constructing a one-dimension convolution network (1D-CNN). Meanwhile, a multiple target process scheme combined with the 1D-CNN is introduced to deal with multi-target situation. In the numerical simulation part, the simulation results reveal that the DC-GAN network can effectively recover the missing data from the covariance matrix, and the lower the missing data rate, the better the data recovery performance. In addition, in the simulation of beamforming vector estimation, the effects of two different input modes on network training performance are evaluated, and the performance differences between a fully connected neural network (FCNN) and 1D-CNN are analyzed and compared. The numerical simulation results verify the effectiveness of the proposed FDA-MIMO radar anti-interference scheme under different number of interference signal scenarios, and improve the interference suppression capability of FDA-MIMO radar.

Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

In the domain of vehicular engineering, multipath detection plays a critical role in the accurate assessment of the environment. This paper introduces a Shapley value-based power resource allocation (SVPRA) method developed for low-grazing angle detection in distributed multiple-input multiple-output (D-MIMO) radar sensor networks (RSN). The SVPRA leverages multipath echoes for improved detection performance through coordinated power resource allocation (PRA). A systematic multipath scattering model with uncertainty identifies four independent spatial paths, highlighting the impact of multipath effects (ME) fluctuations on detection. The PRA is then formulated as a non-convex Max-min optimization problem, incorporating the signal-to-interference plus noise ratio (SINR) indicator and modeling cognitive collaboration detection as a cooperative game. The application of the PRA rule has been mathematically proven to consistently enhance the detection performance. To address convergence challenges in multi-target scenarios, a fine-tuning method is introduced, providing a provisional iteration outcome, succeeded by a greedy selection process to achieve a feasible solution. Simulation results validate that the SVPRA method effectively enhances detection performance while reducing the system's timeliness load.