A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model.

Xin Tian, Guoliang Wei, Jianhua Wang, Dianchen Zhang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An Indoor Robust Localization Algorithm Based on Data Association Technique.Sensors (Basel, Switzerland) · 2020
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xin TianDepartment of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 171560040@st.usst.edu.cn.

Guoliang WeiCollege of Science, University of Shanghai for Science and Technology, Shanghai 200093, China. guoliang.wei@usst.edu.cn.

Jianhua WangDepartment of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 171560042@st.usst.edu.cn.

Dianchen ZhangDepartment of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 172560460@st.usst.edu.cn.

Funding

National Natural Science Foundation of China 61873169Program for Capability Construction of Shanghai Provincial Universities 15550502500

6 · The paper itself

Abstract

In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.

Indexed as

EKFlocalizationMLENLOSprobability model

Identifiers

PMID31614979

DOI10.3390/s19204438

PMCPMC6833029

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.