差分吸收激光雷达回波信号统计模型的研究

差分吸收激光雷达回波信号统计模型是研究差分吸收激光雷达系统的基础,但是现有的统计模型中均没有考虑大气湍流导致的激光闪烁对回波信号的影响。针对已有统计模型的缺陷和应用需要,综合考虑了大气湍流导致的激光闪烁、目标反射斑纹、接收噪声以及发射功率波动对回波信号统计特性的影响,提出了一种改进的长程差分吸收激光雷达回波信号统计模型,分析了该模型的统计特性。仿真结果表明大气湍流导致的激光闪烁对回波信号统计特性的影响十分显著,同时也验证了该统计模型比已有统计模型更精确、更符合实际。     

Novel algorithm for simultaneously detecting multiple vapor materials with multiple-wavelength differential absorption lidar

Differential absorption lidar (DIAL) has been successfully used to detect vapor material, however limited to detect single vapor using two closely spaced wavelengths. The progress in multiple-wavelength lasers motivates the need for detection and estimation algorithms that have the capability for simultaneous detection of multiple materials. In this paper, a simple and accurate algorithm is presented for simultaneously detecting and estimating multiple vapor materials with multiple-wavelength DIAL, which based on the maximum likelihood estimation (MLE) methodology. The performance of the algorithm is evaluated by simulation experiments, the results show that this algorithm can separately identify and quantify vapor material in mixtures and perform quite well.     

Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering

Lidar is an effective tool for remotely monitoring target or object, but the lidar signal is often affected by various noises or interferences. Therefore, detecting the weak signals buried in noises is a fundamental and important problem in the lidar systems. In this paper, an effective noise reduction method combining wavelet improved threshold with wavelet domain spatial filtration is presented to denoise pulse lidar signal and is investigated by detecting the simulating pulse lidar signals in noise. The simulation results show that this method can effectively identify the edge of signal and detect the weak lidar signal buried in noises.