Work

Adaptive Multi-Radar Anti-Bias Track Association Algorithm Based on Reference Topology Features

Year: 2025

Type: article

Abstract: Accurate track association is essential for multi-radar fusion, since incorrect associations may result in significant errors in integrated information. To address the track association problem in multi-radar systems, particularly the challenges posed by offset bias, this paper proposes an adaptive multi-radar anti-bias track association algorithm based on reference topological features (RETs) that achieves accurate association despite offset bias and radar missed detections. The multi-radar adaptive RET algorithm employs the Optimal Sub-Pattern Assignment (OSPA) metric, which is corrected for offset bias, to measure the distance among RETs, thus generating an association cost matrix. The obtained distances among RETs follow a chi-squared distribution, thereby replacing the manually adjusted association threshold with an adaptive association threshold, enhancing robustness against offset bias and measurement noise. Subsequently, the multi-dimensional association cost matrix is filtered using threshold filtering to reduce erroneous associations caused by radar missed detections. Finally, the Lagrangian relaxation algorithm is applied to assign the association cost matrix and determine the final track association. The simulation results demonstrate that the multi-radar adaptive RET algorithm achieves accurate association results and exhibits considerable adaptability to radar offset bias and random noise errors.

Source: Remote Sensing

DOI: 10.3390/rs17111876

Authors: Shaoming Wei, Xuan Zhou, Jun Wang, Rui Pang, Xueqing Li, Qiang Liu

Institution: Beihang University

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China