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We propose a novel consensus notion, called "partial consensus", fordistributed GM-PHD (Gaussian mixture probability hypothesis density) fusionbased on a peer-to-peer (P2P) sensor network, in which only highly-weightedposterior Gaussian components (GCs) are disseminated in the P2P communicationfor fusion while the insignificant GCs are not involved. The partial consensusdoes not only enjoy high efficiency in both network communication and localfusion computation, but also significantly reduces the affect of potentialfalse data (clutter) to the filter, leading to increased signal-to-noise ratioat local sensors. Two "conservative" mixture reduction schemes are advocatedfor fusing the shared GCs in a fully distributed manner. One is given bypairwise averaging GCs between sensors based on Hungarian assignment and theother is merging close GCs based a new GM merging scheme. The proposedapproaches have a close connection to the conservative fusion approaches knownas covariance union and arithmetic mean density. In parallel, average consensusis sought on the cardinality distribution (namely the GM weight sum) amongsensors. Simulations for tracking either a single target or multiple targetsthat simultaneously appear are presented based on a sensor network where eachsensor operates a GM-PHD filter, in order to compare our approaches with thebenchmark generalized covariance intersection approach. The results demonstratethat the partial, arithmetic average, consensus outperforms the complete,geometric average, consensus.

Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion