Track-to-Track Association Based on Structural Similarity in the Presence of Sensor Biases

Hongyan Zhu,Suying Han

doi:10.1155/2014/294657

Abstract

The paper addresses the problem of track-to-track association in the presence of sensor biases. In some challenging scenarios, it may be infeasible to implement bias estimation and compensation in time due to the computational intractability or weak observability about sensor biases. In this paper, we introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets. Although the absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar. As a result, instead of using the absolute kinematic states only, we employee the structural similarity to define the association cost. When there are missed detections, the structural similarity between local tracks is evaluated by solving another 2D assignment subproblem. Simulation results demonstrated the power of the proposed approach.

Highlights

The potential advantages of fusing information from disparate sensor systems to achieve better surveillance have been recognized
We introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets
Track-to-track association [1, 2] is a crucial step in the distributed estimation fusion system, which seeks to determine the correspondence between local tracks from different sensors

Summary

Introduction

The potential advantages of fusing information from disparate sensor systems to achieve better surveillance have been recognized. When given the azimuth bias of 3 degrees, the position deviation could reach up to 10 kilometers for a target located 200 km from the sensor In this case, the similarity measure based on absolute coordinates is unbelievable anymore. The absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar. Instead of using the absolute kinematic states only, we employ the structural similarity to measure the distance of two local tracks from different sensors. We develop a structural similarity-based approach to deal with the problem of track association in the presence of sensor biases. Instead of using the absolute kinematic states only, the structural similarity between local tracks is adopted to measure the association cost and is evaluated by solving another 2D assignment subproblem.

Problem Formulation

Track Association Based on Structural Similarity

Association Cost of Local Nodes Based on the Structural

Simulation Results

Conclusions