An Improved Data Correlation Algorithm for Multi-passive-sensor Tracking System

Lijing Zhang,Li Zhou,Yuxiao Song

doi:10.19026/rjaset.7.504

Abstract

For improving the performance of the optimal assignment problem of data correlation of multi-passive- sensor system, an improved optimal assignment algorithm based on multi-source information fusion is put forward. The new algorithm takes advantage of the optimal solution and a certain number of near-optimal solutions of the traditional optimal assignment problem to construct a set of effective multi-tuple of measurement and constructs correlation probability fusing multi-source information between above effective multi-tuple of measurement and target track by using combination rule of D-S evidence theory. The result of simulation experiments shows that, compared with the traditional optimal assignment algorithm, the new algorithm not only improves the accuracy of multi-target tracking in different degrees but also saves a lot of time. So it is an effective data correlation algorithm for multi-passive-sensor system.

Highlights

When the dimension of the optimal assignment problem is bigger than or equal to 3, the technical complexity of solving the optimal assignment model increases exponentially with the increasing of the dimension of the assignment model (Han et al, 2010; Pattipati et al, 1992)
Technol., 7(10): 2106-2111, 2014 lead to the fact that two or more multi-tuple of measurement corresponding to one track are assigned to different target tracks, so when the dimension of the optimal assignment problem is not high, this algorithm will not achieve the goal of eliminating the interferences of false location points effectively and decreasing the model error of the optimal assignment problem
When target interval is 1000 m and bearing measurement error is pi/60, Root Mean Square Error (RMSE) curves and target tracking curves of the traditional multi-dimensional (SD) assignment algorithm and the improved optimal assignment algorithm based on multi-source information fusion (FMFSD) are as follows

Summary

INTRODUCTION

When the dimension of the optimal assignment problem is bigger than or equal to 3, the technical complexity of solving the optimal assignment model increases exponentially with the increasing of the dimension of the assignment model (Han et al, 2010; Pattipati et al, 1992). The model error of the traditional optimal assignment problem constructed by using negative log likelihood ratio about measurement division is small It is more suitable for solving data correlation problem in dense target and clutter scenario. Chummun et al (2001) proposes a clustering algorithm which can avoid a large number of calculations from a great number of false location points This algorithm is not suitable for solving data correlation problem in dense target and clutter scenario. Technol., 7(10): 2106-2111, 2014 lead to the fact that two or more multi-tuple of measurement corresponding to one track are assigned to different target tracks, so when the dimension of the optimal assignment problem is not high, this algorithm will not achieve the goal of eliminating the interferences of false location points effectively and decreasing the model error of the optimal assignment problem.

IMPROVED MEASUREMENT DATA CORRELATION ALGORITHM

Real Track SD FM FSD

CONCLUSION