Wall clutter mitigation based on eigen-analysis in through-the-wall radar imaging
This paper presents an effective approach for mitigating the wall EM returns in through-the-wall radar imaging. The wall returns tend to obscure indoor targets, rendering target detection and classification difficult, if not impossible. The proposed approach recognizes the relative strength of the front wall returns compared to behind-the-wall targets, and uses eigen-structure methods to identify, and then remove the wall subspace that is typically associated with the dominant eigenvalues. The paper provides analyses of wall and target subspace characteristics, dwelling on the underlying property that the wall and target subspaces are, in most cases, spanned by complex sinusoidal components. It is shown that both the wall and the target subspaces can be of multiple dimensions. The paper demonstrates, using simulated and real data, the effectiveness of the proposed approach and compares its performance to that of background subtraction.
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