Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. The conventional Gaussian decomposition (GD) cannot deal with system waveforms (SWs) with non-Gaussian shapes, whereas the recently proposed B-spline-based decomposition method holds an assumption of similarity transformation. We present a multi-Gaussian decomposition (MGD) algorithm that employs a Gaussian mixture model (GMM) to represent the SW. Compared with the GD algorithm, the MGD algorithm exploits the characteristic of the SW using the GMM and hence can fit the received waveforms better than the GD algorithm. In contrast to the B-spline-based method, the proposed algorithm holds a more reasonable assumption that a received waveform is a convolution result of the SW with the target response, which accords with the ranging principle better. The MGD algorithm was validated using the experimental waveforms with negative tails collected by our self-designed LiDAR system. The GD algorithm and the B-spline-based decomposition were also introduced and studied for comparison. The experimental results show that the GMM with six components fit the SW with an acceptable residual. The experimental results also show that the MGD algorithm produced better decomposition results than the other two algorithms in terms of range retrieval, whereas the B-spline-based decomposition showed the best performance with regards to the root-mean-square-error for waveform fitting.

中文翻译：

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高斯混合模型分解负尾LiDAR波形

波形分解技术通常用于从光检测和测距（LiDAR）波形中提取目标的属性。常规的高斯分解（GD）无法处理具有非高斯形状的系统波形（SW），而最近提出的基于B样条的分解方法则假定了相似变换。我们提出了一种采用高斯混合模型（GMM）表示软件的多高斯分解（MGD）算法。与GD算法相比，MGD算法利用GMM利用了SW的特性，因此与GD算法相比，可以更好地拟合接收到的波形。与基于B样条的方法相比，该算法更合理地假设接收到的波形是SW与目标响应的卷积结果，更好地符合测距原理。使用我们自行设计的LiDAR系统收集的带有负尾部的实验波形对MGD算法进行了验证。还介绍了GD算法和基于B样条的分解，并进行了比较研究。实验结果表明，具有六种成分的GMM可以将SW保留为可接受的残差。实验结果还表明，在距离检索方面，MGD算法比其他两种算法产生更好的分解结果，而基于B样条的分解在波形拟合的均方根误差方面表现出最佳性能。还介绍了GD算法和基于B样条的分解，并进行了比较研究。实验结果表明，具有六种成分的GMM可以将SW保留为可接受的残差。实验结果还表明，在距离检索方面，MGD算法比其他两种算法产生更好的分解结果，而基于B样条的分解在波形拟合的均方根误差方面表现出最佳性能。还介绍了GD算法和基于B样条的分解，并进行了比较研究。实验结果表明，具有六种成分的GMM可以将SW保留为可接受的残差。实验结果还表明，在距离检索方面，MGD算法比其他两种算法产生更好的分解结果，而基于B样条的分解在波形拟合的均方根误差方面表现出最佳性能。