Improved NN-JPDAF for Joint Multiple Target Tracking and Feature Extraction

TL;DR

This paper introduces an improved NN-JPDAF algorithm that jointly tracks multiple targets and extracts features from sparse, rapidly changing Fourier signals, even with corrupted measurements, using convex optimization techniques.

Contribution

It develops a novel joint tracking and feature extraction method leveraging atomic norm and l1-norm regularization, solved efficiently via ADMM, for dense target environments.

Findings

Enhanced tracking accuracy demonstrated in simulations.

Effective feature extraction from corrupted, sparse signals.

Robust performance in dense radar target scenarios.

Abstract

Feature aided tracking can often yield improved tracking performance over the standard multiple target tracking (MTT) algorithms with only kinematic measurements. However, in many applications, the feature signal of the targets consists of sparse Fourier-domain signals. It changes quickly and nonlinearly in the time domain, and the feature measurements are corrupted by missed detections and mis-associations. These two factors make it hard to extract the feature information to be used in MTT. In this paper, we develop a feature-aided nearest neighbour joint probabilistic data association filter (NN-JPDAF) for joint MTT and feature extraction in dense target environments. To estimate the rapidly varying feature signal from incomplete and corrupted measurements, we use the atomic norm constraint to formulate the sparsity of feature signal and use the $\ell_1$-norm to formulate the sparsity of the corruption induced by mis-associations. Based on the sparse representation, the feature signal are estimated by solving a semidefinite program (SDP) which is convex. We also provide an iterative method for solving this SDP via the alternating direction method of multipliers (ADMM) where each iteration involves closed-form computation. With the estimated feature signal, re-filtering is performed to estimate the kinematic states of the targets, where the association makes use of both kinematic and feature information. Simulation results are presented to illustrate the performance of the proposed algorithm in a radar application.