2013Unpublished venueRequires access

Localization of mobile equipment in radio environments with no line-of-sight path

Jie Chen, Feng Jiang, A. Lee Swindlehurst, José A. López-Salcedo

Open publisher page 1 citations

Abstract

In recent years, radio positioning has received increasing attention and found many applications in various areas. However, the existence of non-line-of-sight (NLOS) paths introduces considerable positioning errors. In this paper, we propose a two-step approach in order to deal with pure NLOS scenarios based on a simple assumption regarding the propagation environment. A nonlinear least squares (NLS) method is proposed for the initial estimation, followed by a Kalman filter-based method to track subsequent movements. Compared with previous studies, fewer measurements are required to be made. Simulation results are provided to show the performance of both methods.

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What this paper is about

In recent years, radio positioning has received increasing attention and found many applications in various areas. However, the existence of non-line-of-sight (NLOS) paths introduces considerable positioning errors. In this paper, we propose a two-step approach in order to deal with pure NLOS scenarios based on a simple assumption regarding the propagation environment. A nonlinear least squares (NLS) method is proposed for the initial estimation, followed by a Kalman filter-based method to track subsequent movements. Compared with previous studies, fewer measurements are required to be made. Simulation results are provided to show the performance of both methods.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In recent years, radio positioning has received increasing attention and found many applications in various areas. However, the existence of non-line-of-sight (NLOS) paths introduces considerable positioning errors. In this paper, we propose a two-step approach in order to deal with pure NLOS scenarios based on a simple assumption regarding the propagation environment. A nonlinear least squares (NLS) method is proposed for the initial estimation, followed by a Kalman filter-based method to track subsequent movements. Compared with previous studies, fewer measurements are required to be made. Simulation results are provided to show the performance of both methods.

Key concepts: Non-line-of-sight propagation, Kalman filter, Line-of-sight, Computer science, Sight, Path loss, Path (computing), Extended Kalman filter

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