A variational message passing algorithm for cooperative localization in wireless networks

Hedhly, Kaouther; Laaraiedh, Mohamed; Abdelkefi, Fatma; Siala, Mohamed

doi:10.1109/softcom.2016.7772114

Cited by 7 publications

(1 citation statement)

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“…The literature includes three different versions of VMP algorithm: the unimodal, the bimodal, and the trimodal VMP algorithms. () The principle aim of these versions is to approximate a complex or intractable pdf by a simpler pdf. () Given the set

scriptX

of unknown positions and the set

scriptD

of distance measurements, the posterior pdf

p false(scriptX false| scriptD false)

can be approximated by a simpler pdf function denoted

q false(scriptX false)

that minimizes the Kullback‐Leibler divergence (KL‐divergence) function …”

Section: Cooperative Localization Techniquesmentioning

confidence: 99%

Cooperative localization and tracking in wireless sensor networks

Hehdly

Laaraiedh

Abdelkefi

et al. 2018

Int J Communication

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Summary Cooperative localization has attracted great attention in recent years. However, in some scenarios, localization precision is challenging and does not meet the application requirements. In this paper, Kalman and Particle filters (KF and PF) are considered for cooperative localization scenarios purpose. We propose to apply these techniques to cooperative localization approaches that we investigated in previous papers: Evolved Variational Message Passing algorithm (E‐VMP) and Cooperative Robust Geometric Positioning Algorithm (C‐RGPA). The main added value of distributed tracking filters is to guarantee dynamic versions of these two algorithms. The proposed techniques are evaluated and compared by means of real heterogeneous measurements carried out using ZigBee and OFDM devices and where location‐dependent parameters such as RSSI and RTD are exploited. Experiments and realistic simulations reveal that the proposed techniques exhibit better localization accuracy for very low complexity and cost. Moreover, the comparative study shows that distributed particle filter (DPF) provides better performance than KF in terms of positioning accuracy and root‐mean square error.

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