NEW TRACK-TO-TRACK CORRELATION ALGORITHMS BASED ON BITHRESHOLD IN A DISTRIBUTED MULTISENSOR INFORMATION FUSION SYSTEM

Abstract

Track-to-Track correlation (or association) is an ongoing area of interest in the field of distributed multisensory information fusion. In order to perform accurately identifying tracks with common origin and get fast convergence, this study presents independent and dependent Bi-threshold Track Correlation Algorithms (called BTCAs), which are described in detail and the track correlation mass and multivalency processing methods are discussed as well. Then, Based on BTCAs, two modified Bi-threshold Track Correlation Algorithms with average Test Statistic (called BTCA-TSs) are proposed. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer’s and Bar-Shalom’s algorithms. The simulation results show that the performance of these algorithms proposed in this study is much better than that of the classical methods under the environments of dense targets, interfering, noise and track cross and so on.