This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.

中文翻译：

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使用基于POMDP的隐藏目标检测进行干扰弹性跟踪

本文考虑了在存在欺骗干扰机的情况下跟踪多个运动目标飞行器的抗干扰优化问题。由于雷达无法将实际目标车辆与大量欺骗性车辆分开，因此我们将现有的非抗干扰跟踪模型推广为基于抗干扰的部分可观察的基于马尔可夫决策过程（基于POMDP）的游戏跟踪建立一个新的抗干扰贝叶斯跟踪器进行建模。拟议的跟踪器能够分离隐藏的真实目标车辆并建立其精确轨迹，但是有限的雷达资源将降低准确性。为了有效地利用有限的资源来保证抗干扰性能，这项工作推导了针对资源管理策略的抗干扰性能梯度，可以用渐近消失的偏差来估算 利用梯度估计，可以找到具有可容忍复杂性的最佳抗干扰资源管理策略。收敛性分析表明，该算法收敛到概率为1的Nash均衡解。数值结果表明，该算法在存在干扰信号的情况下能够获得准确的目标轨迹。