The Research On Target Data Fusion Methods Of Radar Network System

With the development of information technology, the theme of future war will be the information war, and will gradually become the focus of future war. While as a main detection equipment of target information, radar detection equipment is also facing unprecedented challenges. The detecting data of single radar is hard to meet the needs of defensive formation, also hard to realize"four counteractions". Therefore, the only way to establish ourselves in an unassailable position in the future severe information war under the abominable electromagnetic condition is to network the present radar devices based on multi-channel radar and advanced computer technology. Network is the necessary requirement for the air target detection system, also is one of the inevitable trend of the development of radar technology in the future. However, how to use the advantages of fusion algorithm to take full advantage of the system to obtain objective information, data fusion technology has become a key foothold.This paper mainly studies the multi-sensor data fusion methods in the radar network system, so that the information provided by Multi-sensors can be rational and effective complementary. The main research contents are as follows:1,It analyzes the reasons for deviation detection accuracy in the radar network system, realizes the geographic network encoding of the target information and studies the objective data fusion algorithm based on gray theory. Thus, solve multi-sensor target data association ambiguity problem in the radar network system. The simulation results show that the algorithm is good overall performance.2,It analyses the advantages and disadvantages of the radar and infrared sensors for the detection of low-altitude target. And on this basis, this paper gives feedback based on the weighted radar and infrared sensors, a network of local fusion algorithm and its simulation results. This method will transmit the two types of partial estimates of sensor detection to the fusion center. And the fusion center will mix the right track to have the local track. And then it will feedback the results to the radar and infrared sensors tracking filter as their predicted state and its covariance. The results show that the algorithm improves the estimation accuracy of the global integration to achieve better data fusion.3,After studying the single-sensor interactive multiple model (IMM) algorithm and multi-sensor interactive multiple model algorithm, and meanwhile considering the measured variance which will result in filtering accuracy declined is related to transmission error, calculation error, environmental error, human disturbance and other factors, this paper presents a measurement-based variance adaptive interactive multi-model multi-sensor fusion tracking algorithm and meanwhile makes the simulation test for this algorithm. Experimental results show that the algorithm has excellent feasibility and efficiency and put forward to the future problems.