摘要
针对二维激光告警系统在极端环境下成像质量差的问题,设计了一种基于卡尔曼算法的自适应控制系统。采用两个InGaAs焦平面阵列探测器分别对来袭激光的角度进行精测和粗测,对卡尔曼算法进行推导优化和仿真验证,同时,基于改进后的卡尔曼算法,通过FPGA实现两个探测器的自适应积分控制,达到系统输出自适应控制的目的。结果显示,精测的卡尔曼预测平均误差为0.56%,粗测的卡尔曼预测平均误差为0.78%,在532 nm的模拟光源照射下,自适应控制系统在极端光强下减少了欠曝和过曝的干扰,在不同光强下光斑大小稳定在55个像素左右。实验表明,自适应控制系统提高了激光告警系统在不同光强下的成像稳定性,对激光告警系统在恶劣环境下工作具有重要意义。
Aiming at the problem of poor imaging quality of two-dimensional laser warning system in extreme environment,an adaptive control system based on Kalman algorithm is designed.Two InGaAs focal plane array detectors are used to measure the Angle of the incoming laser,respectively,and the Kalman algorithm is derived and optimized and verified by simulation.At the same time,based on the improved Kalman algorithm,the adaptive integral control of the two detectors is realized by FPGA to achieve the adaptive control of the system output.The results show that the average error of Kalman prediction for coarse measurement is 0.78%,and that for fine measurement is 0.56%.Under the illumination of 532 nm simulated light source,the adaptive control system reduces the interference of under-exposure and over-exposure under extreme light intensity,and the spot size is stable at about 55 pixels under different light intensity.The experiments show that the adaptive control system can improve the imaging stability of the laser warning system under different light intensity,which is of great significance for the laser warning system to work in harsh environment.
作者
张卓奇
张瑞
牛家麒
薛鹏
李孟委
王志斌
ZHANG Zhuoqi;ZHANG Rui;NIU Jiaqi;XUE Peng;LI Mengwei;WANG Zhibin(School of Instruments and Electronics,North University of China,Taiyuan 030051,China;Shanxi Province Optoelectronic Information and Instrumentation Engineering Technology Research Center,Taiyuan 030051,China;Academy for Advanced Interdisciplinary Research,North University of China,Taiyuan 030051,China)
出处
《激光杂志》
CAS
北大核心
2024年第5期62-68,共7页
Laser Journal
基金
国家自然科学基金(No.62105302)
山西省基础研究计划(No.20210302124269)。
关键词
二维激光告警
卡尔曼算法
FPGA
光电探测
two-dimensional laser warning
kalman algorithm
FPGA
photoelectric detection