{'fraction_non_alphanumeric': 0.01963350785340314, 'fraction_numerical': 0.005235602094240838, 'mean_word_length': 5.12, '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 new state augmentation for maneuvering targets detection

In this paper, an innovation model is presented to transform the maneuvering target tracking problems to the standard Bayesian model, therefore a standard Kalman filter can be applied to them. The modeling is based on mixed Bayesian-fisher uncertainties and a special augmentation in state space. In this model, target position and velocity are conventional states and the acceleration is treated as an additive input term, which has been augmented in the corresponding state equation. The results have been compared with the work of Wang, TC et al., (1993). The simulation results show a high performance of the proposed innovation model and effectiveness of this scheme in tracking maneuvering targets.