自适应噪声阈值估计联合AICC的全波形激光雷达波形分解方法  

Full-Waveform LiDAR Decomposition Method Using AICC Integrated Adaptive Noise Threshold Estimation

作　　者：程赛 周梅 姚强强 王金虎 周春城 腾格尔 李传荣 Cheng Sai;Zhou Mei;Yao Qiangqiang;Wang Jinhu;Zhou Chuncheng;Teng Geer;Li Chuanrong(Key Laboratory of Quantitative Remote Sensing Information Technology,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]中国科学院空天信息创新研究院中国科学院定量遥感信息技术重点实验室,北京100094

出　　处：《光学学报》2022年第12期271-279,共9页Acta Optica Sinica

基　　金：国家自然科学基金(61771456)。

摘　　要：传统基于阈值法滤波去噪并结合AIC(Akaike information criterion)的期望最大(EM)分解方法,难以完全有效地消除噪声影响,而在面向小样本目标数据时,AIC的适应性较差。针对该问题,提出了一种改进的EM波形分解方法,采用自适应噪声阈值估计一次性消除背景噪声与随机噪声;针对存在的小样本、弱回波目标数据,使用AICC(Akaike information criterion,corrected)辅助完成EM波形分解。基于多组实测数据对本文方法进行了波形分解,验证了本文方法的有效性和准确性。The conventional expectation maximum(EM)decomposition method combining threshold-based denoising and AIC(Akaike information criterion)is ineffective for eliminating the noisy completely.Moreover,the AIC is less flexible for a small sample target data.To solve this problem,an improved EM waveform decomposition method is proposed,which uses adaptive noise threshold estimation to eliminate background noise and random noise at one time.For the small samples and weak echo target data,the waveform decomposition is performed using EM algorithm collaborated with AICC(Akaike information criterion,corrected).The effectiveness and accuracy of the proposed method are validated based on several sets of measured data.

关 键 词：遥感 全波形激光雷达 波形分解 去噪 期望最大分解 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]