{'fraction_non_alphanumeric': 0.012113055181695828, 'fraction_numerical': 0.0, 'mean_word_length': 5.413793103448276, '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 State Multiple-Hypothesis Tracking

In tracking algorithms where measurements from various sensors are combined the track state representation is usually dependent on the type of sensor information that is received. When a multi-hypothesis tracking algorithm is used the probabilities of the different hypotheses containing tracks in different representations need to be re-evaluated when track state representations are changed. For the particular case of trilateration a method is presented to adapt the state representation as more information becomes available. A discussion is given on how to re-evaluate the probabilities of the hypotheses leading to a method for the trilateration case. This is illustrated by a simple example.