In this paper, we address the problem of blind denoising for laser detection and ranging equipment (LiDAR) based on estimating noise level from LiDAR pulse echo. We first provide rigorous statistical analysis on the eigenvalue distributions of a sample covariance matrix. Then we propose an interval-bounded estimator for noise variance in high dimensional setting. To this end, an effective blind denoising filtering method for LiDAR is devised based on the adaptive estimation noise level. The estimation performance of our method has been guaranteed both theoretically and empirically. The analysis and experiment results have demonstrated that the proposed algorithm can reliably infer true noise levels, and outperforms the relevant existing methods.

对激光雷达距离探测中的回波信号建模, 提出一种窗宽自适应形心修正算法, 根据窗宽与饱和度的关系建立窗宽自适应模型以获取形心, 并采用中位数修正, 实现高精度饱和波形时刻提取.利用Matlab进行仿真分析, 结果表明当信噪比为10 dB时, 窗宽自适应形心修正算法精度为0.3 ns, 相比于传统形心算法提高92%, 可有效解决形心漂移问题.利用板级系统实测波形验证算法, 并针对实测波形的微小畸变修正该算法, 结果表明在饱和波形下该算法时间精度可达0.5 ns, 可实现7.5 cm的测距精度, 有效增大测距动态范围, 降低系统复杂度.