Multiple Active False Target Suppression Based on Distributed GM-PHD Filter
Yongfeng Zhu, Kai Da, Ye Yang, Qiang Fu · 2021 International Conference on Control, Automation and Information Sciences (ICCAIS) · 2021
Current research on radar anti-jamming by multi-static fusion is mainly based on centralized radar networks. A data-level fusion method based on distributed radar network to counter active false targets is proposed in this paper. Firstly, the random-finite-set-based probability hypothesis density (PHD) filter is adopted for each radar to track multiple real and false targets independently. Then, the generalized covariance intersection (GCI) method with zero-forcing property is employed in multi-sensor data fusion to suppress non-collaborative false targets. Furthermore, after introducing passive radars which are less likely to be jammed, the consensus algorithm is utilized to share the true information obtained by passive radars to the entire network for the purpose of suppressing collaborative false targets. The effectiveness of the proposed approach is validated by simulations.