基于光谱探测的战场目标快速识别

基于光谱探测的目标识别技术,因其能够对抗伪装目标、抗干扰性强等优势,已经逐步应用于军事领域,为战场目标识别提供了一个新思路。该方法的重点在于光谱识别的准确性、数据处理技术等等,目前对于该方法的研究、改进以及创新多基于上述重点进行的。现代战争的复杂程度越来越高、可预见性越来越低,所以对于战场机会的把握成为了能否打赢现代战争的关键,因此对于应用于战场的目标识别方法的实时性要求将越来越苛刻。故重点集中在基于光谱识别的目标识别方法的实时性,目的是为了实现快速的目标识别,使得该方法能够适应现代战争的作战特点,增强其实用性。首先对该研究的必要性进行分析;然后以偏振干涉成像光谱仪为例,说明该工作的改进之处并对提高目标的实时性进行原理分析;最后对下一步的工作重点以及应用领域进行分析,简述发展前景。

Research on Fast Target Recognition Method Based on Spectrum Detection in Battlefield

In the modern battlefield, in order to improve the combat capability of the precision guided weapons, realizing effective recognition and precision strikes for targets in battlefield, in this paper, a new method of fast target recognition based on spectral fingerprint analysis is proposed, which combines the characteristics of precision guided weapon seeker characteristics with the operational characteristics. Spectral detection technique is used to obtain the spectral fingerprints of the suspected target areas, and then the target characteristics are analyzed through the spectral fingerprints. Due to the time limit of the precision guided weapon in the actual combat, the process speed of target identification and processing must have good real-time performance. Based on this, we simplified the traditional spectrum detection processing program, and only on the range of the part was concerned with the spectral analysis, so that the amount of data was greatly reduced; When analyzing the spectrum information, the characteristic wavelength of the equipment was defined according to the classification of the weapon and equipment, and the characteristic information of the target was used to characterize a class of targets, which can help to improve the speed of target recognition. In this paper, firstly the influence of target recognition methods were expounded, which showed the necessity of this study; then the fast identification method based on spectrum detection was derived, which showed the feasibility of this study; at last, the proposed method of fast target recognition based on spectrum detection was verified by a series of experiments, which showed the superiority of this study.