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

Automated summary

In this paper, a low complexity distributed approach is proposed to address the multitarget detection/tracking problem in the presence of noisy and missing data. The approach includes a distributed flooding scheme for measurements exchanging among sensors and a sampling-based clustering approach for target detection/tracking. The main advantage of the proposed approach is that it does not require any priori information and all the information required is the measurement set from multiple sensors. A comparison of the proposed approach with existing distributed clustering approaches and cutting edge distributed multi-Bernoulli filters confirms its effectiveness and reliability.