{'fraction_non_alphanumeric': 0.02626641651031895, 'fraction_numerical': 0.0009380863039399625, 'mean_word_length': 5.627329192546584, '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}}
M-Estimation-Based Robust Kalman Filter Algorithm for Three-Dimensional AoA Target Tracking

An attractive issue in numerous applications is to track targets in three-dimensional (3-D) space with angle of arrival (AoA) measurements through nonlinear filters. The tracking performance degradation caused by outlier prompts a variety of robust filters. In this paper, an M-estimation-based robust bias compensation Kalman filter algorithm (MR-BCKF) is proposed. This algorithm recasts the AoA measurement equation to a linear form by pseudo-linearization, and then incorporates the M-estimation criterion into pseudo linear Kalman filter to enhance robustness, followed by the bias compensation to improve tracking accuracy. An improved three-segment weight function based on Mahalanobis distance is established to handle outliers for each element, which does not require the noise characteristics. Simulation demonstrates that MR-BCKF has enhanced robustness against outliers at different levels and achieves more accurate tracking compared with other robust filters.