Research On The Tracking And Fusing Technologies Between ESM And Early-Warning Radar

Nowadays, with the development of technology, the circumstance of modern war has changed a lot. The former methods were not able to meet the actual combat's need, especially the emergences of Network war and the development of information fusion. The circumstance of war is becoming more and more complicated, and there are more and more new weapons coming forth, too. All above bring the system of data fusion a big challenge. For these reasons, in this paper we mainly do some researches on track initiation,track association and data fusion with ESM and Early-warning Radar.Firstly, this paper describes a special location scene in engineering usage. A suitable mathematical model is put forwards using the signal's time of arrival and direction of arrival, and the target states are also estimated by solving nonlinear equation groups. Simulation results show that location by both TOA and DOA has the highest accuracy.Secondly, the shortcomings of Hough transform-based track initiation algorithms have been analyzed. Combining the time information and the heuristic rules with Hough transform, this paper presents Hough transform-based an ant colony optimization. Simulation results show that the algorithm which is used in this paper is easy for implementation, and the ratio of correct track initiation is also high.Thirdly, to solve the problem of the track association between ESM and Early-Warring Radar, for the sake of practical engineering, we propose a new track association algorithm based on fuzzy comprehensive analysis. The Segmenting Track Identifier tracking method has been used to fit the target measure Curves, and then there will be more measurements available for track association judgment. Finally, the algorithm is tested by the numerical data. The good result show that the foreground of this algorithm's engineering application is great.Finally, aiming at the inequality of Measure space's dimension between ESM and Early-Warring radar, we make a comparison between Bayes filter based extend dimension filter and equivalent sensor. Fusion tracking method based on extend dimension filter is simple and flexible, but with the number of sensors increasing, the calculated amount of the algorithms increases sharply. So it is not suitable for engineering application. By comparison, the fusion tracking method based on equivalent sensor performs well in both time cost and location precision.