基于运动学的机载红外被动测距算法研究

Research on Kinematic Model Based Passive Ranging Algorithm for Infrared Searching and Tracking Systems

建立了基于运动学模型的机载红外被动测距算法。在算法中,根据目标的方位和俯仰角度信息,利用状态预测滤波的方法来测量目标的距离,并结合卡尔曼滤波技术降低测距的误差。仿真分析表明:当机动时间达到20s的时候测距误差基本能控制在10%左右,随着机动时间的增加,误差变化率趋于稳定。该算法适用于机载红外搜索/跟踪系统对空中目标的被动测距。

A passive ranging algorithm based on kinematic model for vehicle-mounted infrared imaging system is proposed. According to the pose and altitude of target, distance to the target could be initially obtained by the state forecast, and refined by state filtering in Kalman filter. Simulation Experiments show that the error can be controlled within 10% when maneuvering time reached 20 s. With the increase of mobile time, error rate remained stable. The algorithm could be applied to airborne infrared searching and tracking systems for passive ranging to aerial targets.