Evidence map›Paper›PMID 40573466›Full record

ArticleSensors (Basel, Switzerland)2025

High-Resolution Localization Using Distributed MIMO FMCW Radars.

Huijea Park, Seungsu Chung, Jaehyun Park, Yang Huang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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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.

Huijea ParkDivision of Smart Robot Convergence and Application Engineering, Department of Electronic Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Seungsu ChungDivision of Smart Robot Convergence and Application Engineering, Department of Electronic Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Jaehyun ParkDivision of Smart Robot Convergence and Application Engineering, Department of Electronic Engineering, Pukyong National University, Busan 48513, Republic of Korea.
Yang HuangCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.ORCID 0000-0002-6685-221X

Funding

National Natural Science Foundation of China 62211540396National Research Foundation of Korea RS-2023-00241706
6 · The paper itself

Abstract

Due to its fast processing time and robustness against harsh environmental conditions, the frequency modulated continuous waveform (FMCW) multiple-input multiple-output (MIMO) radar is widely used for target localization. For high-accuracy localization, the two-dimensional multiple signal classification (2D MUSIC) algorithm can be applied to signals received by a single FMCW MIMO radar, achieving high-resolution positioning performance. To further enhance estimation accuracy, received signals or MUSIC spectra from multiple FMCW MIMO radars are often collected at a data fusion center and processed coherently. However, this approach increases data communication overhead and implementation complexity. To address these challenges, we propose an efficient high-resolution target localization algorithm. In the proposed method, the target position estimates from multiple FMCW MIMO radars are collected and combined using a weighted averaging approach to determine the target's position within a unified coordinate system at the data fusion center. We first analyze the achievable resolution in the unified coordinate system, considering the impact of local parameter estimation errors. Based on this analysis, weights are assigned according to the achievable resolution within the unified coordinate framework. Notably, due to the typically limited number of antennas in FMCW MIMO radars, the azimuth angle resolution tends to be relatively lower than the range resolution. As a result, the achievable resolution in the unified coordinate system depends on the placement of each FMCW MIMO radar. The performance of the proposed scheme is validated using both synthetic simulation data and experimentally measured data, demonstrating its effectiveness in real-world scenarios.

Indexed as

2D MUSIC algorithmcoordinate transformationdistributed radarsFMCW MIMO radarjoint range and angle estimation

Identifiers

PMID40573466
PMCPMC12196919

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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.