{'fraction_non_alphanumeric': 0.021929824561403508, 'fraction_numerical': 0.0017543859649122807, 'mean_word_length': 5.482954545454546, '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}}
Adaptive Kalman Filtering for Hybrid ToA/AoA Navigation System

This paper is devoted to adaptive Kalman filtering approach for hybrid ToA/AoA 2D-positioning. The proposed algorithm is easily generalized to 3D positioning case. This work includes four parts: introduction, algorithm, modelling results and conclusion. In the introductory part of paper some arguments in favour of relevance of ongoing studies are advanced. In the second section of this work the proposed algorithm of adaptive Kalman filter including state transition and observation (measurement) models is described in detail. In the third part of present paper the modelling results which confirm accuracy characteristics of proposed adaptive filter were presented. The modelling aims at obtaining estimates of three types of filter of different orders and adaptive filter combining all of them for the measurement under condition of three different types of changing user’s coordinates in time – constant (static position), linear (constant velocity) and quadratic (constant acceleration). In the concluding part of this work directions of future research are presented.