# The Labeled Square Root Cubature Information GM-PHD Approach for Multi Extended Targets Tracking

## TL;DR

This paper introduces a new method for tracking extended targets using a labeled square root cubature information GM-PHD approach to handle nonlinear motion and improve tracking accuracy.

## Contribution

The novel approach combines labeled GM-PHD with SRCIF and label-based trajectory construction for better tracking of extended targets.

## Key findings

- The proposed method improves tracking performance for extended targets with nonlinear motion.
- Label-based trajectory construction enables accurate state and trajectory estimation.
- Simulation results confirm the effectiveness of the new approach.

## Abstract

For modern radars with high resolutions, an extended target may generate more than one observations. The conventional point target-based tracking method can hardly be applied in such scenarios. Recently, the ET-GM-PHD approach has been presented for tracking these extended targets. The performance of such an approach has been influenced by the following disadvantages. First, it has been formulated under the linear Gaussian assumptions. When targets move with nonlinear models, the tracking performance may be rapidly decreased. Second, it neglects the time associations of the estimated states at different time steps, which makes it very challenging to manage targets for the radar systems. In this paper, we present a labeled ET-GM-PHD approach based on the square root cubature information filter (SRCIF) to solve such problems. To be more specific, we, first, utilize the SCRIF for predicting and updating the GM components of the ET-GM-PHD approach. For decreasing the computational cost, a candidate observation extracting method has been put forward in the GM component updating step. Thus, the ET-GM-PHD approach can be adopted to track extended targets with nonlinear motions. Second, a label-based trajectory constructing method has been proposed. By assigning the GM components with different labels before the GM component predicting step, we can obtain the estimated states with different labels. On this basis, the associations between the estimated states and trajectories can be modeled based on these labels. Thus, we can obtain the states and trajectories of multi extended targets simultaneously. The simulation results prove the effectiveness of our approach.