改进的可变加权卡尔曼激光雷达滤波算法

Improved variable weighted Kalman filter algorithm for lidar denoising

针对大气激光雷达探测信号容易受到背景光的干扰,远端探测信号信噪比下降剧烈的问题,根据激光雷达回波信号具有长序列的特点,提出了激光雷达探测信号的改进可变加权卡尔曼滤波算法。该算法在可变加权系数中增加一个常数项,使得改进后的算法可在不同时刻,对长时间序列的激光雷达探测信号提供可变的加权系数。该算法克服了传统卡尔曼算法中滤波增益恒定的问题,增强了新探测信号在最优估计的修正作用,对于长、短时间序列信号均具有很好的滤波效果。本算法经大气激光雷达在不同天气实测信号的验证,与其他3种卡尔曼滤波算法相比,在无云天大气气溶胶消光系数的反演误差,分别降低了53%、25%和3%,回波信号信噪比分别提高了5.5、4.4和3.4 dB。在有云天大气气溶胶消光系数的反演误差,分别降低了57%、26%和4%,回波信号的信噪比分别提高了4.9、3.7和2.5 dB。该算法不但提高了大气气溶胶光学特性的反演精度,而且为气溶胶微物理参数精细探测和激光雷达在气象领域的应用提供了一种有效手段。

In order to solve the problem that atmospheric lidar detection is easily interfered by noise and the signal-to-noise ratio(SNR)of distant signals drops rapidly,according to the long sequence characteristics of the lidar detection,an improved variable weighted Kalman filter method for lidar detection is proposed.A constant term is added to the variable weighted coefficient in the algorithm.Therefore,the changing weighted coefficient can be provided for the long sequence measurements values at different time in the improved variable weighted Kalman filter algorithm.The correction effect of the new measurement is enhanced and the influence of the old measurement on the optimal estimation is reduced in this algorithm.The algorithm is verified by the actual atmospheric lidar measurements in different weathers.Compared with the other three Kalman filtering algorithms,under cloudy weather,the SNR of lidar detections are improved by nearly 4.9,3.7 and 2.5 dB,respectively.The inversion error of aerosol extinction coefficient is reduced by 57%,26%and 4%respectively.In cloudless day,the signal to noise ratio of lidar echo signals are improved by nearly 5.5,4.4 and 3.4 dB respectively.The inversion error of aerosol extinction coefficient is reduced by 53%,25%and 3%respectively.The inversion accuracy of atmospheric aerosol optical properties are improved using this algorithm.An effective method for fine detection of aerosol microphysical parameters and practical application of lidar is provided.