Discrimination of Exo-atmospheric Targets Based on Optimization of Probabilistic Neural Network and IR Multispectral Fusion
Zhang Guo-liang① Yang Chun-ling① Wang Jian-lai②
①(School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China) ②(China Academy of Launch Vehicle Technology, Beijing 100076, China)
Abstract：A Probabilistic Neural Network (PNN) based on Particle Swarm Optimization (PSO) is proposed for ballistic target recognition due to its difficulty in this paper. The fusion of multispectral infrared data is achieved through the use of this method. Firstly, the temperature and emissivity-area of targets are extracted by using a novel multi-colorimetric technology, then the parameter of the PNN is optimized with Gaussian PSO (GPSO), and finally the four typical ballistic targets are classified via the optimized PNN. The method fuses the multi-spectral and multiple dynamic features, hence allowing this algorithm to be quite robust. In addition, the method fully exploits the PNN’s capability for its higher stability and fault-tolerance mechanism. The simulation experiments present multi-spectral infrared radiation intensity sequence of four ballistic targets, and the results show that the proposed method based on the PNN is able to recognize the multiple ballistic targets.
张国亮, 杨春玲, 王暕来. 基于优化概率神经网络和红外多光谱融合的大气层外空间弹道目标识别[J]. 电子与信息学报, 2014, 36(4): 896-902.
Zhang Guo-Liang, Yang Chun-Ling, Wang Jian-Lai. Discrimination of Exo-atmospheric Targets Based on Optimization of Probabilistic Neural Network and IR Multispectral Fusion. , 2014, 36(4): 896-902.
2014, Vol. 36 
