{'fraction_non_alphanumeric': 0.014326647564469915, 'fraction_numerical': 0.0, 'mean_word_length': 5.131578947368421, '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 labeled random finite set spawning model

Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage.