{'fraction_non_alphanumeric': 0.014198782961460446, 'fraction_numerical': 0.0, 'mean_word_length': 5.760273972602739, '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}}
Sequential Fusion Estimation for RSS-Based Mobile Robots Localization With Event-Driven WSNs

This paper is concerned with the sequential fusion estimation for mobile sensor node localizations with received signal strength measurements in mobile wireless sensor networks (MWSNs). The modeling errors induced by the communication uncertainties are considered and the process noise covariance is assumed to follow a uniform distribution. A sequential fusion estimation method based on a novel square root cubature Kalman filter is presented, where the process noise covariance is generated randomly. Moreover, a lower bound of the distribution is given to improve the stability and performance of the estimator. An E-puck robot-based MWSN experiment platform is designed, and both simulations and experiments show that the proposed sequential fusion estimation method help simplify the determination of the process noise covariance while maintaining a satisfactory estimation performance.