A Suboptimal Optimizing Strategy for Velocity Vector Estimation in Single-Observer Passive Localization.

Shuyi Gu, Zhenghua Luo, Yingjun Chu, Yanghui Xu, Junxiong Guo

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Shuyi GuSchool of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.ORCID 0000-0002-2591-885X

Zhenghua LuoSchool of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.

Yingjun ChuThe Fifth Institute of Telecommunication Science and Technology, Chengdu 610036, China.

Yanghui XuChengdu Jinjiang Electronic System Engineering Company, Ltd., Chengdu 610051, China.

Junxiong GuoSchool of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.ORCID 0000-0002-0071-1086

Funding

National Natural Science Foundation of China 62201096Science and Technology Plan of Sichuan Province 2023YFS0426

6 · The paper itself

Abstract

In a single-observer passive localization system, the velocity and position of the target are estimated simultaneously. However, this can lead to correlated errors and distortion of the estimated value, making independent estimation of the speed and position necessary. In this study, we introduce a novel optimization strategy, suboptimal estimation, for independently estimating the velocity vector in single-observer passive localization. The suboptimal estimation strategy converts the estimation of the velocity vector into a search for the global optimal solution by dynamically weighting multiple optimization criteria from the starting point in the solution space. Simulation verification is conducted using uniform motion and constant acceleration models. The results demonstrate that the proposed method converges faster with higher accuracy and strong robustness.

Indexed as

AccelerationAlgorithmsComputer SimulationMotiondirection of arrival (DOA)Kalman filtering (KF)optimizationsimulated annealing (SA)single-observer passive localizationtime of arrival (TOA)

Identifiers

PMID37447787

DOI10.3390/s23135940

PMCPMC10346755

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.