Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Etzlinger, Bernhard; Meyer, Florian; Hlawatsch, Franz; Springer, Andreas; Wymeersch, Henk

doi:10.1109/tsp.2017.2691665

Cited by 62 publications

(49 citation statements)

References 45 publications

(126 reference statements)

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“…For simplicity, we assume time synchronization between the PAs and the mobile agent. However, the BP-SLAM algorithm can be extended to nonsynchronized PA-agent links along the lines of [13] (based on the fact that the relevant geometric information is also contained in the time differences of the MPCs [14], [48]) or to joint SLAM and synchronization along the lines of [49]. Furthermore, we assume that the probabilities with which the preliminary signal analysis stage (producing measurements) detects features in the radio signals are known; however, an adaptive extension to unknown and time-varying detection probabilities can be obtained along the lines of [33], [43], [50].…”

Section: B Contributions and Organization Of The Papermentioning

confidence: 99%

A Belief Propagation Algorithm for Multipath-Based SLAM

Leitinger

Meyer

Hlawatsch

et al. 2019

IEEE Trans. Wireless Commun.

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175

174

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We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

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Section: B Contributions and Organization Of The Papermentioning

confidence: 99%

A Belief Propagation Algorithm for Multipath-Based SLAM

Leitinger

Meyer

Hlawatsch

et al. 2019

IEEE Trans. Wireless Commun.

Self Cite

175

174

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“…Accordingly, the synergy between relative localization and clock synchronization is obvious. Various methods for joint localization and synchronization (JLAS) in WSN using TOA measurements have been proposed [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multiagent Systems

Shi

Cui

Zhao

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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The spatiotemporal information plays crucial roles in a multi-agent system (MAS). However, for a highly dynamic and dense MAS in unknown environments, estimating its spatiotemporal states is a difficult problem. In this paper, we present BLAS: a wireless broadcast relative localization and clock synchronization system to address these challenges. Our BLAS system exploits a broadcast architecture, under which a MAS is categorized into parent agents that broadcast wireless packets and child agents that are passive receivers, to reduce the number of required packets among agents for relative localization and clock synchronization. We first propose an asynchronous broadcasting and passively receiving (ABPR) protocol. The protocol schedules the broadcast of parent agents using a distributed time division multiple access (D-TDMA) scheme and delivers interagent information used for joint relative localization and clock synchronization. We then present distributed state estimation approaches in parent and child agents that utilize the broadcast inter-agent information for joint estimation of spatiotemporal states. The simulations and real-world experiments based on ultra-wideband (UWB) illustrate that our proposed BLAS cannot only enable accurate, high-frequency and real-time estimation of relative position and clock parameters but also support theoretically an unlimited number of agents.

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“…In addition, we find in the literature works which presented a factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). () In fact, authors in Meyer et al proposed a particle‐based belief propagation algorithm for distributed CoSLAS in decentralized sensor networks. However, this proposed technique still suffers from important root mean square error (RMSE) and very high complexity since it uses a high number of particles .…”

Section: Introduction and Related Workmentioning

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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Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cited by 62 publications

References 45 publications

A Belief Propagation Algorithm for Multipath-Based SLAM

A Belief Propagation Algorithm for Multipath-Based SLAM

BLAS: Broadcast Relative Localization and Clock Synchronization for Dynamic Dense Multiagent Systems

Cooperative localization and tracking in wireless sensor networks

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