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.
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.
In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed.
How to utilize the limited power budget to accurately track more targets plays a critical role for the radar system in air defense applications, especially in the weapon guidance application. In this paper, we propose a robust power allocation (RPA) strategy in the collocated multiple-input and multiple-output (C-MIMO) radar system for multiple target guidance (MTG) under blanket jamming. The optimization model is established with the aim of increasing the number of effective tracking targets (ETTs) and improving the overall tracking accuracy among those targets. Since the mutual information (MI) quantifies the parameter estimation performance and can be predicted, the MI under blanket jamming is derived and utilized as the optimization criterion. We then propose a two-step optimization algorithm based on benefit–cost ratio (BCR) to solve the non-convex problem. Finally, numerical results are provided to demonstrate the effectiveness of the proposed algorithm.
How to utilize limited system resources budget to maximize the effectiveness potential of the entire system through optimal allocation has always been a hot issue in radar resource management. This paper establishes a hybrid distributed phased array multiple-input and multiple-output (PA-MIMO) radar system model. It combines coherent processing gain and spatial diversity gain to synergistically improve the target detection performance of the radar system. For the hybrid distributed PA-MIMO radar system, we derive a likelihood ratio (LRT) detector based on the Neyman-Pearson (NP) criterion. The coherent processing gain and spatial diversity gain are jointly optimized by implementing subarray-level and array element-level optimal configuration at both transceiver and transmitter ends. Moreover, a quantum particle swarm optimization-based stochastic rounding (SR-QPSO) solution algorithm is proposed for the integer planning-based configuration model. And the optimal array element configuration strategy is guaranteed to be obtained with fewer iterations and realize the joint optimization between subarray and array levels. Finally, numerical simulations are carried out using three typical optimization problems to demonstrate the effectiveness of the optimal configuration of the hybrid distributed PA-MIMO radar system.
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