{'fraction_non_alphanumeric': 0.02819548872180451, 'fraction_numerical': 0.0, 'mean_word_length': 5.614906832298137, '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}}
A new framework for direction-of-arrival estimation

A new approach for spatial direction-of-arrival (DOA) estimation is developed based on the minimum mean-square error (MMSE) framework. Unlike many traditional DOA estimators, the MMSE approach, denoted as Re-Iterative Super-Resolution (RISR), does not employ spatial sample covariance information which may significantly degrade DOA estimation if spatially-separated sources are temporally correlated. Instead, RISR is a recursive algorithm that relies on a structured signal covariance matrix comprised of the set of possible spatial steering vectors each weighted by an associated power estimate from the previous iteration. Furthermore, RISR can naturally accommodate prior information on spatially colored noise, does not require knowledge of the number of sources, and can also exploit multiple time samples in a non-coherent manner to improve performance. For low to moderate time sample support, RISR is demonstrated to provide super-resolution performance superior to MUSIC and spatially-smoothed MUSIC.