{'fraction_non_alphanumeric': 0.015772870662460567, 'fraction_numerical': 0.002365930599369085, 'mean_word_length': 5.609375, '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}}
Passive Sensor Fusion and Tracking in Underwater Surveillance with the GLMB model

Passive sensors pose challenges in the localisation and tracking of targets due to the inherent ambiguity in the source range-specifically, accurate 2-D localisation using only a single sensor is not guaranteed. On the other hand, the underwater domain exhibits certain characteristics that induce some structure to the problem that help reduce the range ambiguity and lead to a favourable 2-D localisation and tracking performance. This work incorporates target motion analysis (TMA) into tracking algorithms by specifying target birth process parameters and demonstrates that the output exhibits contraction in the range uncertainty. In addition, we propose a likelihood model that incorporates cepstrum processing detections that exploit multi-path reflections from the sea surface and the sea bottom to reduce the range ambiguity. We use these models in the generalised labelled multi-Bernoulli (GLMB) multi-object model and the associated tracking filter with sequential Monte Carlo techniques. This model explicitly incorporates track labels in Bayesian recursions yielding coherent trajectory estimates consisting of the 2-D location estimates when used with passive detections.