A New Target-correlation Algorithm for Heterogeneous Sensors Based on Neural Network Classification
Meng Cang-zhen①② Yuan Ding-bo① Xu Jia③ Peng Shi-bao① Wang Xiao-jun②
①(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China) ②(Air Force Early Warning Academy of PLA, Wuhan 430019, China) ③(School of Information and Electronic, Beijing Institute of Technology, Beijing 100081, China)
Abstract：In the data fusion system composed of radar and infrared sensor installed in high speed of dynamic platform, the system error estimation and target correlation are dependent and are difficult very much. To solve the problem, a new target correlation algorithm based on pattern classification is proposed in the article according to the property of system errors variation. The approach realizes pattern classification by BP neural network. It needn’t estimate the system error and compensate it, and has a tolerance to system error. The experiment shows that the average correct probability for target-correlation in the data fusion between the above two kind of sensors is more than 86%.
