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Range Resolution Improvement of Eyesafe Ladar Testbed (ELT) Measurements Using Sparse Signal Deconvolution

机译:使用稀疏信号反卷积提高Eyesafe Ladar测试平台(ELT)测量的距离分辨率

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The Eyesafe Ladar Test-bed (ELT) is a experimental ladar system with the capability of digitizing return laser pulse waveforms at 2 GHz. These waveforms can then be exploited o-line in the laboratory to develop signal processing techniques for noise reduction, range resolution improvement, and range discrimination between two surfaces of similar range interrogated by a single laser pulse. This paper presents the results of experiments with new deconvolution algorithms with the hoped for gains of improving the range discrimination of the ladar system. The sparsity of ladar returns is exploited to solve the deconvolution problem in two steps. The rst step is to estimate a point target response using a database of measured calibration data. This basic target response is used to construct a dictionary of target responses with dierent delays/ranges. Using this dictionary ladar returns from a wide variety of surface congurations can be synthesized by taking linear combinations. A sparse linear combination matches the physical reality that ladar returns consist of the overlapping of only a few pulses. The dictionary construction process is a pre-processing step that is performed only once. The deconvolution step is performed by minimizing the error between the measured ladar return and the dictionary model while constraining the coecient vector to be sparse. Other constraints such as the non-negativity of the coecients are also applied. The results of the proposed technique are presented in the paper and are shown to compare favorably with previously investigated deconvolution techniques.
机译:Eyesafe激光测试台(ELT)是一种实验性激光系统,能够数字化2 GHz的返回激光脉冲波形。然后可以在实验室中通过o线利用这些波形来开发信号处理技术,以降低噪声,改善距离分辨率并在单个激光脉冲询问的相似范围的两个表面之间进行距离辨别。本文介绍了使用新的反卷积算法进行实验的结果,希望能改善雷达系统的距离分辨力。利用稀疏的雷达收益分两步来解决反卷积问题。第一步是使用测得的校准数据数据库估算点目标响应。该基本目标响应用于构建具有不同延迟/范围的目标响应的字典。使用该字典,可以通过采用线性组合来合成来自各种表面构造的激光雷达回波。稀疏的线性组合符合物理现实,即雷达返回仅包含几个脉冲的重叠。词典构建过程是仅执行一次的预处理步骤。去卷积步骤是通过最小化测得的雷达回波和字典模型之间的误差,同时限制余数向量来进行的。其他约束,例如系数的非负性也适用。所提出的技术的结果在论文中提出,并显示出与先前研究的反卷积技术相比具有优势。

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