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摘要:
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。
Abstract: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 information 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 reduce the false positive rate of data without hardware failures, the mean values of extinction coefficient and signal-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 recognition effect on hardware fault data.
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Key words:
- LiDAR /
- fuzzy logic /
- membership function /
- signal to noise ratio /
- extinction coefficient
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Overview: Particle LiDAR is a high-precision instrument with laser as the emitter. It continuously monitors the temporal and spatial evolution and characteristics of aerosol, boundary layer, cloud height and multi-layer cloud structure, thus obtaining the three-dimensional structure of atmospheric aerosol distribution with detailed changes, which has strong ability and high degree of automation. The particulate matter LiDAR is fully covered in the area to achieve high-temporal resolution air pollution monitoring, combined with the application of informational big data to achieve pollution source tracking, early warning, forecasting functions, etc., to provide more timely and effective decision support for environmental pollution prevention and control. However, when the hardware of the radar's transmitting unit, receiving unit, etc. fails, there will often be abnormal echo data generated, which will directly affect the subsequent inversion results and have a great influence on the accuracy of the above applications. As a long-term, high-intensity, continuous operation high-precision equipment, atmospheric particulate matter monitoring LiDAR affected by factors such as working environment and accessory quality, and hardware failure is difficult to avoid.
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 information of the echo shape and intensity of the radar, the fuzzy logic algorithm is used to identify the fault data. The hardware fault data of the atmospheric particulate radar is identified and tested. At the same time, in order to reduce the false positive rate of data without hardware failures, the mean values of extinction coefficient and signal-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, reducing 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 recognition effect on hardware fault data.
The method adopted in this paper can also realize the real-time monitoring of the LiDAR operating state and achieve real-time warning of the LiDAR running state, which provides a reference for us to find faults in time and ensures the normal operation of the equipment.
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图 1 无硬件故障数据的Z/Z′和r概率统计分布图。(a) Z/Z′；(b) r
Figure 1. Statistical distribution of Z/Z′ and r probabilities without hardware failure data. (a) Z/Z′; (b) r
图 2 有硬件故障数据的Z/Z′和r概率统计分布图。(a) Z/Z′；(b) r
Figure 2. Statistical distribution of Z/Z′ and r probabilities with hardware failure data. (a) Z/Z′; (b) r
图 3 Z/Z′和r的阈值公式。(a) Z/Z′；(b) r
Figure 3. Z/Z′ and r corresponding membership function. (a) Z/Z′; (b) r
图 4 误判数据的回波波形图。(a)云；(b)霾；(c)沙尘
Figure 4. Echo waveform of misjudged data. (a) Cloud; (b) Haze; (c) Dust
图 5 300 m~500 m处信噪比和消光系数均值的聚类分析图
Figure 5. Cluster analysis of the mean value of SNR and extinction coefficient from 300 meters to 500 meters
表 1 大气颗粒物监测激光雷达硬件故障数据识别表
Table 1. Identification of hardware failure data of atmospheric particulates monitor LiDAR
数据类型 判别为故障数据 判别为无故障数据 故障数据(1470) 1390(NTN) 80(NFP) 无故障数据(7226) 1228(NFN) 5998(NTP) 
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表 2 误判校正后颗粒物监测激光雷达硬件故障数据识别表
Table 2. Identification of the hardware failure data of atmospheric particulates monitor LiDAR after misjudgment correction
数据类型 判别为故障数据 判别为无故障数据 故障数据(1470) 1390(NTN) 80(NFP) 无故障数据(7226) 109(NFN) 7117(NTP)
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表 3 颗粒物激光雷达硬件故障数据的识别表
Table 3. Identification of the hardware failure data of particle LiDAR
数据类型 判别为故障数据 判别为无故障数据 故障数据(185) 173(NTN) 12(NFP) 无故障数据(489) 11(NFN) 478(NTP)
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