Target tracking for a correlated multi-sensor system with constant time delay

Abstract

We propose a new tracking system, comprising a binocular vision sensor and a laser range finder, which is able to deliver higher precision and real-time performance for spatial multi-sensor tracking applications. In this sort of system, the problem of multi-sensor estimated correlation normally brings about unknown cross-covariance. In the proposed system, a covariance intersection–Kalman fusion algorithm with diagonal matrix weighting is used to solve this problem and to improve fusion accuracy. However, the observed constant time delay caused by the poor performance of the two-dimensional rotary table used in this study still remains. To overcome this problem, we propose the use of a direct-update algorithm that turns the time-delay information into real-time data. Simulation and experimental results showed that the real-time performance of the time-delay information was enhanced, while the mean squared errors produced using the final information fusion algorithm were reduced to about 1.5, signifying an improvement in target tracking accuracy. The experimental data and reference values produced should assist the further study of spatial target tracking and have a positive impact on the development of subsequent engineering applications for this system.

Keywords

Target tracking information fusion estimated correlation observed constant time delay fusion covariance intersection

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