Discrimination of Exo-atmospheric Targets Based on Optimization of Probabilistic Neural Network and IR Multispectral Fusion

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 PNNs 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.