On generalized covariance intersection for distributed PHD filtering and a simple but better alternative

Some concerns are raised on the prevailing generalized covariance intersection (GCI) based Gaussian mixture probability hypothesis density (GM-PHD) fusion for distributed multiple target tracking under cluttered environments, which is both communicative and computation expensive, and generates a large amount of Gaussian components (GCs) of little physical significance. The problems become more serious when targets are closely distributed and/or when clutter is heavy. To avoid these problems and to save communication and computation, we advocate to only share the sufficiently strong-weighted GCs between neighboring sensors. The shared significant GCs are simply merged based on their spatial proximity, which resembles a type of multisensor signal superposition and will enhance the signal-noise-ratio (SNR) since strong GCs are more likely to be a 'target signal' than a weak one, thereby facilitating less likely false alarms and a more accurate estimation. In parallel to the conservative GC sharing and merging, a standard averaging consensus is also sought on the cardinality distribution (a.k.a. the probability distribution of the target number) among sensors. Simulations have been provided to demonstrate the superiority and reliability of our approach with comparison to the benchmark GCI approach.