Multi-GMTI fusion for Doppler blind zone suppression using PHD fusion

For ground moving target indication (GMTI) sensor tracking, the existence of the Doppler blind zone (DBZ) seriously deteriorates tracking performance. In order to minimize the adverse effects of the DBZ factor, this paper puts forward the idea of using sensor fusion technique to suppress the DBZ masking problem. First, we derive the probability hypothesis density (PHD) fusion under the generalized covariance intersection (GCI) framework and its Gaussian mixture (GM) implementation for fusing local PHDs from the local trackers. However, we find that there is the problem of cardinality underestimation (CUE) in the original PHD fusion, which is exacerbated when targets are masked by the DBZ. After analyzing this problem in detail, we propose an improved PHD fusion algorithm through operations such as scale coefficient correction, GM component partition, and fused label correction. Finally, the feasibility and effectiveness of the proposed fusion are verified through numerical examples, and it is proved that it alleviates the CUE problem and is significantly better than local trackers.