Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter

Abstract

This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP) with the information of the target aspect angle, and target classification is implemented through the data correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-target JTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTC method into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter with Bayesian theory. To further tackle the complex integrals’ calculation involved in targets state and class estimation, we finally provide the sequential Monte Carlo (SMC) implementation of the proposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC method is validated by simulation results under the application scenario of maritime ship surveillance.

HDT network: A high‐resolution range profile multi‐target detection and tracking method based on neural network

“…The data-association-based method involves explicit associations. For example, the joint probabilistic data association (JPDA) algorithm weights all the observations by association probabilities, and the MHT algorithm propagates association hypothesis [13,14]. With the rapid development of deep learnings, CNN and Long Short-Term Memory (LSTM) have been used in object tracking [15][16][17][18].…”