Abstract
Generalized Covariance Intersection (GCI) is state-of-the-art distributed fusion method, however, its fusion accuracy is affected by low detection probability targets. The presence of the inherent Doppler blind zone (DBZ) of the airborne radar seriously affects the detection probability of the targets as well as the performance of distributed multi-target tracking (DMTT). The traditional GCI fusion tends to remain only the targets that detected by all nodes. For tracking the targets hidden in the DBZ, based on multi-model (MM) Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter, an efficient fusion strategy is proposed. Numerical experiment clearly shows the effectiveness of the proposed fusion algorithm.
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