{'fraction_non_alphanumeric': 0.01878453038674033, 'fraction_numerical': 0.0, 'mean_word_length': 5.291666666666667, '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}}
An Improved Multi-sensor Orderly PHD Convolution Particle Filtering Algorithm

An improved multi-sensor orderly PHD convolution particle filtering algorithm is proposed to handle multi-sensor multi-target tracking problem under the condition of asynchronous sampling. Firstly, the sampling points in a fusion cycle are mapped to the timeline of the fusion center based on the framework of SMC-PHD. State equations within two adjacent intervals are fused to model the state of the sampling interval based on the sequence of measured value, and the particle weights are updated by convolution particle filter to avoid calculating likelihood function in multi-dimensional measurement space. Finally the state values of targets are extracted by CLEAN technology. Simulation results show CKUPC-PHD has higher robustness and stability in the context of multi-sensor multi-target tracking as compared to SMC-PHD.