Sparsity-Aware Noise Subspace Fitting for DOA Estimation.

Chundi Zheng, Huihui Chen, Aiguo Wang

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

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

1 citing paper in PubMed.

DOA Estimation Method Based on Improved Deep Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022
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

3 authors.

Chundi ZhengSchool of Electronic Information Engineering, Foshan University, Foshan 528231, Guangdong, China.

Huihui ChenSchool of Electronic Information Engineering, Foshan University, Foshan 528231, Guangdong, China.

Aiguo WangSchool of Electronic Information Engineering, Foshan University, Foshan 528231, Guangdong, China.

Funding

Foshan Self-found S&T Innovation Plan 1920001001001Major Natural Science Research of the Anhui Higher Education Institution of China KJ2019ZD44National Natural Science Foundation of China 61401496National Natural Science Foundation of China 61902068National Natural Science Foundation of China 61972092

6 · The paper itself

Abstract

We propose a sparsity-aware noise subspace fitting (SANSF) algorithm for direction-of-arrival (DOA) estimation using an array of sensors. The proposed SANSF algorithm is developed from the optimally weighted noise subspace fitting criterion. Our formulation leads to a convex linearly constrained quadratic programming (LCQP) problem that enjoys global convergence without the need of accurate initialization and can be easily solved by existing LCQP solvers. Combining the weighted quadratic objective function, the ℓ 1 norm, and the non-negative constraints, the proposed SANSF algorithm can enhance the sparsity of the solution. Numerical results based on simulations, using real measured ultrasonic, and radar data, show that, compared to existing sparsity-aware methods, the proposed SANSF can provide enhanced resolution with a lower computational burden.

Indexed as

array signal processingdirection-of-arrival (DOA) estimationlinearly constrained quadratic programming (LCQP)sparse recoverysubspace fitting

Identifiers

PMID31877776

DOI10.3390/s20010081

PMCPMC6982932

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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.