CAJ | 학술논문

针对大气层外空间弹道目标难识别的问题，该文利用红外多光谱数据融合的思想，提出一种基于粒子群优化概率神经网络(PNN)的大气层外空间弹道目标识别方法。该方法首先通过一种新的多色测温方法提取出弹道目标的温度变化率和有效辐射面积两类动态特征，然后利用高斯粒子群优化(GPSO)方法对PNN的平滑因子进行优化，最后利用优化的PNN完成4类典型空间目标的识别。该方法融合了多光谱信息并提取出了多个动态特征，具有较强的鲁棒性。另外，该方法充分利用了概率神经网络的较高的稳定性和样本容错能力。仿真实验给出了4类典型空间弹道目标的多光谱红外辐射强度序列数据，并进行了目标识别研究。仿真测试结果表明，提出的优化PNN网络对多个弹道目标具有良好的识别能力。
침대대기층외공간탄도목표난식별적문제，해문이용홍외다광보수거융합적사상，제출일충기우입자군우화개솔신경망락(PNN)적대기층외공간탄도목표식별방법。해방법수선통과일충신적다색측온방법제취출탄도목표적온도변화솔화유효복사면적량류동태특정，연후이용고사입자군우화(GPSO)방법대PNN적평활인자진행우화，최후이용우화적PNN완성4류전형공간목표적식별。해방법융합료다광보신식병제취출료다개동태특정，구유교강적로봉성。령외，해방법충분이용료개솔신경망락적교고적은정성화양본용착능력。방진실험급출료4류전형공간탄도목표적다광보홍외복사강도서렬수거，병진행료목표식별연구。방진측시결과표명，제출적우화PNN망락대다개탄도목표구유량호적식별능력。
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.