Anti‐bias track‐to‐track association algorithm based on distance detection. Issue 2 (1st February 2017)

• Record Type: Journal Article
• Authors: Qi, Lin; Dong, Kai; Liu, Yu; Liu, Jun; Jian, Tao; He, You
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 2(2017)
• Page Start: 269
• Page End: 276
• Publication Date: 2017-02-01
• Subjects: sensor fusion -- aircraft -- Gaussian processes -- target tracking; antibias track‐to‐track association algorithm -- distance detection -- multisensor real‐time track‐to‐track association problem -- aircraft platforms -- Gaussian random vectors -- target densities -- random errors -- sensor biases -- Monte Carlo simulations; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0139
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : To address multi‐sensor real‐time track‐to‐track association problem of aircraft platforms in a complex environment, where sensor biases are time‐varied, targets are distributed closely and different sensors report different targets, an anti‐bias track‐to‐track association algorithm based on distance detection is proposed according to the statistical characteristics of Gaussian random vectors. First, a distance vector between homologous tracks is derived and its feature is analysed; second, the rough association based on minimum average and the refined association based on χ 2 distribution are illustrated according to the features of distance vectors. Adaptability experiments are established based on three factors including different target densities, random errors, and sensor biases. Monte Carlo simulations demonstrate the proposed algorithm has significant improvements of association accuracy and complex condition adaptability compared with the classical reference topology feature algorithm.