Cooperative localization based on an evolved variational message passing algorithm

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

doi:10.23919/softcom.2017.8115521

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…() Despite the improvement of the positioning accuracy, on one hand, B‐VMP and Tri‐VMP algorithms were not able to omit the ambiguities in different scenarios; on the other hand, they could not eliminate the alternation of the position estimate for one or more mobile nodes between two different positions through the iterations. To overcome these drawbacks, we proposed an enhanced version of the VMP algorithm, E‐VMP algorithm, which significantly reduces the impact of this alternation . In fact, the E‐VMP algorithm eliminates the original cause of the mentioned alternation and proposes a solution to bypass it.…”

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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Section: Cooperative Localization Techniquesmentioning

confidence: 99%