Jamming Resilient Tracking Using POMDP-Based Detection of Hidden Targets

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.