A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs-Reference-Cited by-同舟云学术

A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs

Published:2023-04 Issue:4 Volume:10 Page:1077-1079
ISSN:2329-9266
Container-title:IEEE/CAA Journal of Automatica Sinica
language:
Short-container-title:IEEE/CAA J. Autom. Sinica

Author:

Zheng Linyao¹,Liu Meiqin¹,Zhang Senlin²,Lan Jian¹

Affiliation:

1. Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University,Xi'an,China,710049

2. College of Electrical Engineering, Zhejiang University,Hangzhou,China,310027

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization

Link

http://xplorestaging.ieee.org/ielx7/6570654/10085745/10085958.pdf?arnumber=10085958

Reference11 articles.

1. Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

2. Survey of maneuvering target tracking. Part I. Dynamic models;li;IEEE Trans Aerospace and Electronic Syst,2003

3. Joint tracking and classification based on recursive joint decision and estimation using multi-sensor data;cao;Proc 17th Intern Conf Information Fusion,0

4. A Multi-Node Cooperative Bearing-Only Target Passive Tracking Algorithm via UWSNs

5. Adaptive sensor scheduling for target tracking in underwater wireless sensor networks

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