光子计数激光雷达的时间相关卡尔曼深度估计

在高背景噪声和低积分时间的激光雷达远距离成像场景中,针对传统方法得到的深度图像目标被噪声淹没和深度估计偏差较大的问题,提出了一种基于信号光子时间相关性和自适应卡尔曼滤波器的深度信息估计方法。首先,提取在时间上具有聚集特征的光子计数形成集合;然后,分析了影响信号光子在时间上分布的因素并使用静态高斯线性模型来描述该集合;最后将集合中的所有光子飞行时间乱序,输入改进的自适应卡尔曼滤波器,从而迭代估计深度值。在信号噪声比为1的室内,积分时间分别为10 ms和1 ms时,本文方法相对传统的最大似然方法在均方根误差指标上提升了40%和38%。在信噪比约为0.135的室外2 km目标成像实验中,在信号光子数分别为100、33和17的情况下,本文方法成像效果都优于传统最大似然估计方法和时间相关光子快速去噪方法,得到的深度图像都更清晰,噪声更低。在高噪声和短积分时间下,本文方法可以被运用于激光雷达远距离成像的深度信息估计和图像恢复中。

Time-correlated Kalman Depth Estimation of Photon-counting Lidar

In the lidar long-distance imaging scene with high background noise and low integration time,in view of the problem of the depth image target obtained by traditional methods being submerged by noise and the large deviation of depth estimation,a method based on signal photon time correlation and adaptive Kalman filter depth information estimation method is proposed.The photon counts with aggregation feature in time will be extracted to form a set firstly;then the factors that affect the temporal distribution of signal photons will be analyzed and Gaussian linear model will be used to described the photon set;finally,the time-of-flight of all photons in the set will be scrambled and input into the improved adaptive Kalman filter to iteratively estimate the depth value.In the room with signal to noise ratio of 1,compared with the traditional maximum likelihood method,this method improves the root mean square error by 40%and 38%when the integration time is 10 ms and 1ms respectively.In the outdoor 2 km target imaging experiment with signal to noise ratio of about 0.135,when the signal photon numbers are 100,33 and 17 respectively,the depth image of this method is clearer and the noise is lower than the traditional maximum likelihood method and fast denoising algorithm with the temporal correlation of photons.It is verified that the method can be applied to the depth information estimation and image restoration of lidar remote imaging under high noise and short integration time.