已知波形信号的到达时间和多普勒频移的子空间估计

V. Latyshev
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引用次数: 2

摘要

基于子空间的技术用于估计已知波形信号的到达时间和多普勒频移。找到所需子空间的工具是对接收到的数据进行特殊的正交分解。它允许在分解的少量第一项中集中关于所需参数的费雪信息。这种方法提供了一个具有足够统计量的低维向量。它导致计算效率高的贝叶斯估计。此外,它还扩大了有效的机器学习估计的信噪比范围。最后,在矩阵对的基本广义特征向量上得到独立的时间到达估计和多普勒频移估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subspace-based estimation of time of arrival and Doppler shift for a signal of known waveform
The subspace-based technique is used for the estimation of the time of arrival and Doppler shift of a signal of the known waveform. The tool to find required subspaces is a special orthogonal decomposition of received data. It allows concentrate Fisher information about desired parameter in a small number of the first terms of the decomposition. This approach offers a low-dimensional vector of sufficient statistics. It leads to computationally efficient Bayes estimation. Besides, it results in expanding of the SNR range for effective ML-estimating. At last, we can obtain independent time arrival and Doppler shift estimations on the base generalized eigenvectors of the matrix pair.
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