A Markov Chain Monte Carlo Alternating Minimization Algorithm for Asynchronous Relay Network Localization

作者：Rui Liyang; Ho K C^*

来源：IEEE Wireless Communications Letters, 2017, 6(2): 278-281.

DOI：10.1109/LWC.2017.2672671

• Localization
• relay network
• TOA

This letter proposes an algorithm to locate an object by an asynchronous relay network using time of arrival (TOA) measurements. It applies the alternating minimization approach that iterates between measurement association and TOA localization. It solves the highly complex association problem between measurements and relays efficiently using the Markov Chain Monte Carlo method. Simulations show that the proposed method has high accuracy for measurement association and yields a localization accuracy near the Cramer Rao lower bound before the measurement noise becomes large.

• 出版日期2017-4