2017/04/06 by Hassan Naseri, Visa Koivunen, Naseri, Hassan +1 · 1 citation
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Machine Learning (stat.ML) #Underwater Vehicles and Communication Systems
paper · pdf · doi:10.48550/arxiv.1704.01918
openalex publication_date 2017/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A reliable, accurate, and affordable positioning service is highly required\nin wireless networks. In this paper, the novel Message Passing Hybrid\nLocalization (MPHL) algorithm is proposed to solve the problem of cooperative\ndistributed localization using distance and direction estimates. This hybrid\napproach combines two sensing modalities to reduce the uncertainty in\nlocalizing the network nodes. A statistical model is formulated for the\nproblem, and approximate minimum mean square error (MMSE) estimates of the node\nlocations are computed. The proposed MPHL is a distributed algorithm based on\nbelief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It\nimproves the identifiability of the localization problem and reduces its\nsensitivity to the anchor node geometry, compared to distance-only or\ndirection-only localization techniques. For example, the unknown location of a\nnode can be found if it has only a single neighbor; and a whole network can be\nlocalized using only a single anchor node. Numerical results are presented\nshowing that the average localization error is significantly reduced in almost\nevery simulation scenario, about 50% in most cases, compared to the competing\nalgorithms.\n