A Low Complexity Distributed Multitarget Detection and Tracking Algorithm

In this paper, we propose a low complexity distributed approach to address the multitarget detection/tracking problem in presence of noisy and missing data. The proposed consists two components: flooding scheme for measurements exchanging among sensors sampling-based clustering target from aggregated measurements. main advantage over prevailing Markov-Bayes-based filters is that it does not require any priori information all required measurement set multiple sensors. A comparison with available approaches cutting edge multi-Bernoulli are modeled appropriate parameters confirms effectiveness reliability approach.