Non linear state estimation using particle filter based on backtracking search optimization

Bhateja, Abhimanyu; Walia, Gurjit Singh; Kapoor, Rajiv

doi:10.1109/ccaa.2016.7813742

Cited by 1 publication

(1 citation statement)

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“…To this end, messages that are being transmitted for localization or communication can be reused. As an example, backtracking particle filters have been proposed that are able to correct past location predictions of mobile tags based on more recent location estimates [4,42]. Such backtracking algorithms [97], that aim to minimize the error of range estimates considering recent and past locations, could be modified to also correct the most likely position of the anchor nodes.…”

Section: Multiple Evaluation Metricsmentioning

confidence: 99%

Self-calibration and Collaborative Localization for UWB Positioning Systems

et al. 2021

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Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

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Section: Multiple Evaluation Metricsmentioning

confidence: 99%