In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks.

In this article, a game-theoretic joint coalition formation and power allocation (JCFPA) strategy is investigated for multitarget tracking (MTT) in a distributed radar network. The main objective of the presented strategy is to minimize the total transmit power consumption and enhance the target tracking accuracy concurrently, while adhering to predefined requirements on the MTT performance and system illumination resource budgets, thus improving the low probability of intercept performance. To achieve this, a utility function is developed to evaluate the coalition structure, transmit power consumption, and tracking accuracy. Then, by formulating the cooperative interactions among radars as a coalition game, we establish an optimization model to optimize the coalition structure and power allocation for the distributed radar network. The existence of the Nash equilibrium solution for the game is proven mathematically. To address the optimization model, an iterative three-step algorithm is developed based on the sequential quadratic programming. Numerical results reveal that the presented JCFPA strategy obtains superior system performance compared to other benchmarks.

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm.