子空间跟踪在时间更新中的优势

The 10th IEEE International Symposium on Signal Processing and Information Technology Pub Date : 2010-12-15 DOI:10.1109/ISSPIT.2010.5711805

Matthias Lechtenberg, J. Götze

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引用次数: 4

摘要

在进行参数估计时，ESPRIT是一种常用的算法。作为输入，ESPRIT需要信号子空间的特征向量。这些可以通过特征值分解或子空间跟踪算法生成。在本文中，我们证明了子空间跟踪不仅在计算复杂度上优越，而且在精度上优越，这是由于它的时间更新特性。我们还将详细说明这种情况，并说明延迟是一种副作用。

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On the advantages of subspace tracking for temporal updating

When doing parameter estimation, ESPRIT is an often used algorithm. As input, ESPRIT needs the eigenvectors of the signal subspace. These can be generated by an eigenvalue decomposition or by subspace tracking algorithms. In this paper, we demonstrate, that subspace tracking is superior not only regarding computational complexity but also regarding accuracy, which is due to its temporal updating character. We will also detail the circumstances for which this holds and that delay is a side-effect.

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The 10th IEEE International Symposium on Signal Processing and Information Technology

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