Multi-Radar Bias Estimation Without a Priori Association

• Published in: IEEE access Vol. 6; pp. 44616 - 44625
• Main Authors: Zhang, Tao, Li, Hai, Yang, Lei, Liu, Weijian, Wu, Renbiao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bias; Estimation; Gaussian distribution; Maximum likelihood estimators; Multi-sensor multi-target tracking; Multiple target tracking; Noise measurement; probability hypothesis density filter; Radar measurements; Radar networks; radar systematic bias estimation; Radar tracking; Statistical analysis; Systematics; Target tracking; Tracking systems
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2862926

A solution for multi-radar bias estimation without a priori association is provided for a decentralized multi-radar tracking system. We describe the systematic bias of radar with random finite sets by a pseudo-measurement of bias, which is derived at the measurement level to decouple the bias estimation and target tracking. The Gaussian mixture probability hypothesis density filter is applied for estimating the systematic bias recursively in multi-target tracking scene without a priori association. The numerical results show that the proposed method provides similar performance to the maximum likelihood estimator with the perfect known association and improved performance to the maximum likelihood estimator combined with probabilistic data association.