# Joint Target Detection, Tracking and Classification with Forward-Backward PHD Smoothing

## TL;DR

This paper introduces a generalized forward-backward PHD smoothing method that incorporates target class features, significantly improving joint detection, tracking, and classification performance in dense clutter environments.

## Contribution

It extends the PHD smoothing framework to include mode features like class, enhancing target state estimation and classification capabilities.

## Key findings

- Outperforms existing algorithms in target state estimation.
- Reduces OSPA distance by up to 40%.
- Improves accuracy in dense clutter environments.

## Abstract

Forward-backward Probability Hypothesis Density (PHD) smoothing is an efficient way for target tracking in dense clutter environment. Although the target class has been widely viewed as useful information to enhance the target tracking, there is no existing work in literature which incorporates the feature information into PHD smoothing. In this paper, we generalized the PHD smoothing by extending the general mode, which includes kinematic mode, class mode or their combinations etc., to forward-backward PHD filter. Through a top-down method, the general mode augmented forward-backward PHD smoothing is derived. The evaluation results show that our approach out-performs the state-of-art joint detection, tracking and classification algorithm in target state estimation, number estimation and classification. The reduction of OSPA distance is up to 40%.