Evidence map›Paper›PMID 42740065›Full record

ArticleSensors (Basel, Switzerland)2026

Coherent Signal DOA Estimation and 3D Point Cloud Imaging Based on a Straight-Curved Hybrid L-Shaped Conformal Array.

Bowen Bie, Yang Chen, Huiwen Chen, Ning Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

4 authors.

Bowen BieSchool of Information Mechanics and Sensing Engineering, Xidian University, Xi'an 710071, China.
Yang ChenSchool of Information Mechanics and Sensing Engineering, Xidian University, Xi'an 710071, China.
Huiwen ChenHangzhou Institute of Technology, Xidian University, Xi'an 710071, China.
Ning LiFaculty of Infor-X, School of Information Mechanics and Sensing Engineering, Xidian University, Xi'an 710071, China.ORCID 0000-0003-3389-981X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-speed airborne platforms impose stringent aerodynamic constraints that restrict traditional planar antenna designs. Concurrently, multi-component echoes from extended targets and complex propagation environments induce strong signal coherence, severely degrading spatial angle estimation. To address these dual challenges, this paper proposes a straight-curved hybrid L-shaped asymmetric conformal array hardware topology tailored for cylindrical radomes. Building upon this, a millimeter-wave radar 3D point cloud imaging framework is developed for coherent targets. An orthogonal virtual manifold transformation is first devised to effectively compensate for the non-linear phase distortion induced by the conformal topology. Subsequently, a cascaded Forward-Backward Spatial Smoothing (FBSS) and Root-MUSIC framework is used for efficient signal decoherence. To resolve angle mismatches, a global cost function based on the cross-covariance Frobenius norm is formulated, which pairs independent angles and significantly suppresses spatial ghost targets. Systematic evaluations using 3D computer vision metrics demonstrate that the proposed method achieves accurate geometric restoration of aircraft targets with coherent signals. In the representative simulation, the method obtains a median point-wise localization error of 0.3986 m, a Chamfer Distance (CD) of 0.9695 m

Indexed as

2D DOA estimation3D point cloud imagingL-shaped conformal arraymillimeter-wave radarvirtual array transformation

Identifiers

PMID42740065
PMCPMC13568084

What OpenQuestion holds

Textmetadata
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.