Manoeuvre Target Tracking in Wireless Sensor Networks Using Convolutional Bi-Directional Long Short-Term Memory Neural Networks and Extended Kalman Filtering

Autor:	Duo Peng, Kun Xie, Mingshuo Liu
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	mobile target tracking convolutional long short-term neural networks extended Kalman filtering wireless sensor networks received signal strength Chemical technology TP1-1185
Zdroj:	Sensors, Vol 24, Iss 13, p 4261 (2024)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s24134261
Popis:	Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.
Databáze:	Directory of Open Access Journals
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