| An adaptive time delay estimati on algorithm based onquadratic weighting of the frequency domain |
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| 作者姓名： | CHENHuawei ZHAOJunwei GUOYecai CAIZongyi XUXuezhong |
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| 作者单位： | [1]InstituteofAcousticEngineering,NorthwesternPolytechnicalUniversityXi‘an710072 [2]NorthwesternInstituteofNucleusTechnologyXi’an710024,NorthwesternPolytechnicalUniversityXi‘an710072 |
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| 基金项目： | This work was supported by the National Natural Science Foundation of China and the Doctoral
Foundation of Northwestern Polytechnical University. |
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| 摘 要： | Time delay estimation (TDE) plays an important role in many engineering appli-cations. A new time delay estimation configuration, the quadratic weighting of the frequency domain adaptive TDE model, is put forward. The quadratic weighting of the frequency domainSCOT (Smoothed Coherence Transform) and ML (Maximum Likelihood) adaptive TDE algo-rithms are presented, respectively. The variance of the quadratic weighting of the frequency domain SCOT algorithm is derived. Then the proposed algorithms are applied in the TDE of helicopter passive acoustic location. The simulation results are presented which verify that the proposed algorithm has better performance in the low signal to noise ratio.
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| 关 键 词： | 时间延迟估计 TDE模型 二次加权 频率分析 快速傅里叶变换 算法 噪声 平滑 信号估计 |
An adaptive time delay estimation algorithm based on quadratic weighting of the frequency domain |
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| Authors: | CHEN Huawei ZHAO Junwei GUO Yecai |
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| Abstract: | Time delay estimation (TDE) plays an important role in many engineering applications. A new time delay estimation configuration, the quadratic weighting of the frequency domain adaptive TDE model, is put forward. The quadratic weighting of the frequency domain SCOT (Smoothed Coherence Transform) and ML (Maximum Likelihood) adaptive TDE algorithms are presented, respectively. The variance of the quadratic weighting of the frequency domain SCOT algorithm is derived. Then the proposed algorithms are applied in the TDE of helicopter passive acoustic location. The simulation results are presented which verify that the proposed algorithm has better performance in the low signal to noise ratio. |
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| Keywords: | 43 60 |
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