{'fraction_non_alphanumeric': 0.017959183673469388, 'fraction_numerical': 0.0, 'mean_word_length': 5.811111111111111, '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 Modified Particle Filter Algorithm for Wireless Capsule Endoscope Location Tracking

Tracking a capsule endoscope location is one of promising application offered by implant body area networks (BANs). In this paper, we pay attention to a particle filter algorithm with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem, which assumes a nonlinear transition model on the capsule endoscope location. However, the original particle filter requires to calculate the particle weight according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated by received wireless signal. Therefore, for the purpose of applying the particle filter to the capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrates that the proposed tracking methods with the modified particle filter can improve the performance as compared with not only the conventional maximum likelihood (ML) localization but also the original particle filter-based location tracking.