激光雷达出现硬件故障时，会使回波数据的质量变差。目前，对由硬件故障造成的错误回波还缺乏比较有效的识别方法。对中国科学院安徽光学精密机械研究所自主研发的大气颗粒物监测激光雷达有硬件故障出现时的回波数据进行分析，根据硬件故障对雷达的回波波形、强度等回波信号信息的影响，采用模糊逻辑算法对大气颗粒物雷达的硬件故障数据进行识别检验。同时，为了降低对无故障数据的误判，分析被误判数据的回波特征，比较硬件故障数据和被误判数据在 300 m～500 m高度上对应的消光系数和信噪比均值，通过设置信噪比阈值来降低误判率。实验结果表明：应用此方法对外场运行的大气颗粒物监测激光雷达硬件故障数据进行识别，识别率为 94.6%，而误判率仅为 1.5%，证明该算法对硬件故障数据的识别有很好的效果。

The hardware fault of the LiDAR will make the quality of the echo data worse. However, there is still a lack of effective identification methods for the error data caused by the hardware failure. Analysis of echo characteristics of atmospheric particulate matter monitoring when LiDAR has hardware failure, according to the echo signal infor-mation of the echo shape and intensity of the LiDAR, the fuzzy logic algorithm is used to identify the fault data. The hardware fault data of the atmospheric particulate LiDAR is identified and tested. At the same time, in order to re-duce the false positive rate of data without hardware failures, the mean values of extinction coefficient and sig-nal-to-noise ratio (SNR) at the height of 300 meters to 500 meters were compared between the data of hardware failures and the data was misjudged, reduced the false positive rate by setting the signal to noise ratio threshold. The experimental results show that this method is used to identify the hardware fault data of the LiDAR monitoring of the external field, the recognition rate is 94.6%, and the false positive rate is only 1.5%. This method has a good recog-nition effect on hardware fault data.