Multi-sensor joint target detection, tracking and classification via Bernoulli filter

TL;DR

This paper introduces centralized and distributed Bernoulli filter-based methods for joint detection, tracking, and classification of targets using multi-sensor data, effectively handling target existence, class, and mode uncertainties.

Contribution

It proposes novel centralized and distributed Bernoulli filters for joint detection, tracking, and classification in multi-sensor fusion scenarios, modeling targets with extended Bernoulli RFS.

Findings

Effective multi-sensor fusion for JDTC demonstrated in simulations

Novel centralized and distributed Bernoulli filters developed

Improved target detection, classification, and tracking accuracy

Abstract

This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli \textit{random finite set} (RFS) uniquely characterized by existence, classification, class-conditioned mode and class\&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel \textit{centralized} and \textit{distributed} JDTC \textit{Bernoulli filters} (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.