Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning
Journal article, 2019

Cooperative localization is a promising solution to the vehicular high-accuracy localization problem. Despite its high potential, exhaustive measurement and information exchange between all adjacent vehicles are expensive and impractical for applications with limited resources. Greedy policies or hand-engineering heuristics may not be able to meet the requirement of complicated use cases. In this paper, we formulate a scheduling problem to improve the localization accuracy (measured through the Cramér-Rao lower bound) of every vehicle up to a given threshold using the minimum number of measurements. The problem is cast as a partially observable Markov decision process and solved using decentralized scheduling algorithms with deep reinforcement learning, which allow vehicles to optimize the scheduling (i.e., the instants to execute measurement and information exchange with each adjacent vehicle) in a distributed manner without a central controlling unit. Simulation results show that the proposed algorithms have a significant advantage over random and greedy policies in terms of both required numbers of measurements to localize all nodes and achievable localization precision with limited numbers of measurements.

deep reinforcement learning

policy gradient

Machine-learning for vehicular localization

deep Q-learning

cooperative localization

Author

Bile Peng

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

G. Seco-Granados

Universitat Autonoma de Barcelona (UAB)

Erik M Steinmetz

RISE Research Institutes of Sweden

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Markus Fröhle

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

IEEE Transactions on Vehicular Technology

0018-9545 (ISSN)

Vol. 68 5 4295-4305 8701533

Subject Categories

Control Engineering

Signal Processing

Computer Science

DOI

10.1109/TVT.2019.2913695

More information

Latest update

7/12/2019