Results 281 to 290 of about 70,734 (340)

A Target-Oriented Bayesian Compressive Sensing Imaging Method With Region-Adaptive Extractor for mmW Automotive Radar

IEEE Transactions on Geoscience and Remote Sensing, 2023
Millimeter-wave (mmW) automotive radar imaging technology has shown significant potential in autopilot assistance systems. The automotive radar with limited aperture can achieve high-resolution images by synthetic aperture technology.
Yanqin Xu, Xiaoling Zhang, Shunjun Wei, Jun Shi, Tianjiao Zeng, Tianwen Zhang   +5 more
semanticscholar   +1 more source

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2023
Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement ...
Peiping Li, Yu Wang
semanticscholar   +1 more source

Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

International Radar Conference, 2023
The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed.
Ignacio Roldan, Lucas L. Lamberti, F. Fioranelli, A. Yarovoy   +3 more
semanticscholar   +1 more source

Image Reconstruction for Low-Oversampled Staggered SAR Based on Bayesian Compressive Sensing

IEEE International Geoscience and Remote Sensing Symposium, 2023
Staggered synthetic aperture radar (SAR) is an innovative concept of high-resolution and wide-swath systems, it combines SCan-On-REceive (SCORE) with continuous variation of the pulse repetition interval (PRI) to deal with the blind ranges over wide ...
Wenjiao Chen, Li Zhang, Jiwen Geng, Hongling Liu   +3 more
semanticscholar   +1 more source

Combination Complex-Valued Bayesian Compressive Sensing Method for Sparsity Constrained Deconvolution Beamforming

IEEE Transactions on Instrumentation and Measurement, 2022
Several deconvolution methods have been proposed to reduce the mainlobe width and sidelobe intensity of conventional beamforming results without increasing the array aperture; however, most of them cannot perform well in the face of coherent targets ...
Fei Wang, Xiang Tian, Xuesong Liu, Boxuan Gu, Fan Zhou, Yao-wu Chen   +5 more
semanticscholar   +1 more source

Augmented Bayesian Compressive Sensing

2015 Data Compression Conference, 2015
The simultaneous sparse approximation problem is concerned with recovering a set of multichannel signals that share a common support pattern using incomplete or compressive measurements. Multichannel modifications of greedy algorithms like orthogonal matching pursuit (OMP), as well as convex mixed-norm extensions of the Lasso, have typically been ...
David Wipf, Jeong-Min Yun, Qing Ling
openaire   +1 more source

Hopping Time Estimation of Frequency-Hopping Signals Based on HMM-Enhanced Bayesian Compressive Sensing With Missing Observations

IEEE Communications Letters, 2022
The hopping time reflects the time-varying characteristics of frequency-hopping (FH) signals, which are essential parameters for the spectrum estimation of FH signals.
Hongbin Wang, Bangning Zhang, Heng Wang, Binbin Wu, D. Guo   +4 more
semanticscholar   +1 more source

A Bayesian Compressive Sensing-Based Planar Array Diagnosis Approach From Near-Field Measurements

IEEE Antennas and Wireless Propagation Letters, 2021
Array diagnosis is an important tool for detecting and correcting array antenna failures. In this letter, a high-precision planar array diagnosis method based on the Bayesian compressive sensing (BCS) theory is proposed.
Zhenwei Lin, Yao-wu Chen, Xuesong Liu, Rongxin Jiang, Binjian Shen   +4 more
semanticscholar   +1 more source

Multi-Task Bayesian compressive sensing exploiting signal structures

Signal Processing, 2021
Conventional Bayesian compressive sensing (CS) is considered for signals that are sparse in some domains, and only sparse prior is adopted to guarantee the exact inverse recovery.
Jiahao Liu, Qisong Wu, M. Amin
semanticscholar   +1 more source

Adaptive Beamforming Design of Planar Arrays Based on Bayesian Compressive Sensing

IEEE Sensors Journal, 2021
An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded.
Zhenwei Lin, Yao-wu Chen, Xuesong Liu, Rongxin Jiang, Binjian Shen   +4 more
semanticscholar   +1 more source