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

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...

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Vydané v:IEEE transactions on signal processing Ročník 69; s. 15 - 27
Hlavní autori: Song, Weihao, Wang, Zidong, Wang, Jianan, Alsaadi, Fuad E., Shan, Jiayuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.3042951