| 赵 虎,张海伦,郭嘉琦,谢青青,毛建东,饶志敏.改进的可变加权卡尔曼激光雷达滤波算法[J].电子测量与仪器学报,2022,36(5):188-195 |
| 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 |
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| DOI： |
| 中文关键词: 卡尔曼滤波 激光雷达 大气遥感 消光系数 |
| 英文关键词:Kalman filter lidar atmospheric remote sensing extinction coefficient |
| 基金项目:国家自然科学基金（61865001）、宁夏自然科学基金（2018AAC03103）、北方民族大学创新项目基金（YCX20110）项目资助 |
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| Author | Institution |
| Zhao Hu | 1. College of Electrical and Information Engineering, North Minzu University,2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
| Zhang Hailun | 1. College of Electrical and Information Engineering, North Minzu University,2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
| Guo Jiaqi | 1. College of Electrical and Information Engineering, North Minzu University,2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
| Xie Qingqing | 1. College of Electrical and Information Engineering, North Minzu University,2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
| Mao Jiandong | 2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
| Rao Zhimin | 1. College of Electrical and Information Engineering, North Minzu University,2. Key Laboratory of Atmospheric Environment Remote Sensing in Ningxia Autonomous Region |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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