{'fraction_non_alphanumeric': 0.018849206349206348, 'fraction_numerical': 0.0, 'mean_word_length': 5.386075949367089, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detecting target under non-flat surface with holographic radar

The ability to penetrate medium and provide high-resolution image makes holographic radar popular among researchers in recent years. However, the strong clutter caused by the non-flat surface, which is unavoidable in practice, may obscure the image of target. This paper presents a Principal Component Analysis (PCA) based method to remove the surface clutter and enhance the imaging result. The symmetric non-flat surface and the consequent clutter are analyzed at first to reveal the low intrinsic dimensionality of the surface clutter signal in the recorded data matrix. Then the PCA is applied to project the recorded data into different subspaces and separate target response form clutter. With clutter suppressed, the target response is focused with wavenumber domain Filtered Backprojection (FBP) to obtain clear imaging result. Numerical simulations and laboratory experiments are conducted and the results validate the proposed method.