{'fraction_non_alphanumeric': 0.023421588594704685, 'fraction_numerical': 0.0050916496945010185, 'mean_word_length': 5.779310344827586, '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}}
A N-dimensional assignment algorithm to solve multitarget tracking

This paper deals with combinatorial optimization in multitarget multisensor tracking. The cornerstone in any multitarget and/or multisensor tracking problem is the data-association problem. The approach retained in this paper deals with the combinatorial complexity; it amounts to solve a multi-dimensional assignment problem. Although this problem is known to be NP-hard, the Lagrangean relaxation provides bounds on the optimal solution by solving successive 2-dimensional assignment problems. Inherited from commonly used methods in operational research, the N-dimensional assignment problem first applied to multisensor tracking by Pattipati et al. (1992) is revisited. Particularly, issues of dummy measurements to model missed detection and false-alarms are carefully studied. General conditions required to formulate the multitarget multisensor tracking as a multi-dimensional assignment are also discussed.