卡尔曼算法在目标跟踪、风向预测、非均匀性矫正领域都有着广泛应用。提出了用卡尔曼算法进行线列相机扫描灰度值的预测。根据线列探测器的实时扫描, 得出了灰度直方图的变化, 并将这一过程归一化到卡尔曼公式中; 用递推法快速测出了下一时间灰度的统计分布, 间接预测了积分时间, 最终达到了红外线列探测器动态范围自适应控制的目的。采用多幅8 bit图像为不同场景提供仿真环境, 最终快速稳定地预测出了下一阶段的灰度分布统计图。结果为红外线列相机扫描提供了智能化解决方案。

Kalman algorithm is widely used in the fields of target tracking, wind direction prediction and nonuniformity correction. The prediction of scanning gray value of a linear array camera by Kalman algorithm is proposed. According to thereal-time scanning of the linear array detector, the change in the gray level histogram is derived and normalized into Kalman’s formula. The statistical distribution of next time gray level is measured quickly by a recursive method. The integration time is predicted indirectly. Finally, the adaptive control of the dynamic range of the linear infrared detector is achieved. Several 8 bit images are used to provide simulation environment for different scenes. Then, the gray level distribution statistics in neat stage is predicted quickly and stably. The result provides an intelligent solution for the scanning of the linear array infrared camera.