{'fraction_non_alphanumeric': 0.018949181739879414, 'fraction_numerical': 0.0, 'mean_word_length': 5.52808988764045, '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}}
Analysis of order statistic filters for robust detection

In detection systems (e.g. radar) , the effect of interference patterns (clutter) from the environment are partially unknown and/or varying in terms of their statistical properties. In such instances, the performance of the optimal detector deteriorates significantly, and a nonparametric or constant false alarm rate (CFAR) detector which is designed to be insensitive to changes in the underlying density functions of the clutter is needed. Order Statistic (OS) filters have been shown to perform effectively in detection systems when the observations are independent and identically distributed. When the assumptions about the observations are violated or the underlying distribution of the clutter is altered, only certain ranks of the OS filter show robust performance. This study analyzes the performance of OS filters in the framework of nonparametric detection for cases in which the observations do not contain equivalent statistical information or observations are not independent. Through computer simulation, the extent of robustness that can be obtained from different ranks is illustrated.