Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations

• Published in: IEEE transactions on signal processing Vol. 69; pp. 15 - 27
• Main Authors: Song, Weihao, Wang, Zidong, Wang, Jianan, Alsaadi, Fuad E., Shan, Jiayuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Energy distribution; Energy harvesting; Energy harvesting sensor; Energy levels; Energy measurement; Filtration; Moving targets; multi-sensor systems; Nonlinear systems; nonlinear/non-Gaussian systems; particle filtering; Particle measurements; randomly occurring sensor saturations; Remote sensors; Sensor phenomena and characterization; Sensor systems; Sensors; Signal processing algorithms; Tracking problem
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2020.3042951

In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions. The energy harvesting sensor transmits its measurement output to the remote filter only when the current energy level is sufficient, where the transmission probability of the measurement is recursively calculated by using the probability distribution of the sensor energy level. The effects of the ROSSs and the possible measurement losses induced by insufficient energies are fully considered in the design of filtering scheme, and an explicit expression of the likelihood function is derived. Finally, the numerical simulation examples (including a benchmark example for nonlinear filtering and the applications in moving target tracking problem) are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.