Results 21 to 30 of about 4,767 (172)

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

Remote Sensing, 2022
Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment.
Chenxi Zhang, Huiliang Zhao, Wenchao Chen, Bo Chen, Penghui Wang, Changrui Jia, Hongwei Liu   +6 more
doaj   +1 more source

A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar

Journal of Applied Mathematics, 2014
We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years.
Tao Chen, Huanxin Wu, Lutao Liu
doaj   +1 more source

A Grid-Less Total Variation Minimization-Based Space-Time Adaptive Processing for Airborne Radar

IEEE Access, 2020
Sparse recovery (SR) based space-time adaptive processing (STAP) has attracted much attention due to its small requirement of snapshots in the estimation of the clutter plus noise covariance matrix (CNCM).
Yuyu Su, Tong Wang, Fuyu Tao, Zhongyue Li   +3 more
doaj   +1 more source

Activities and participation after primary total hip arthroplasty; posterolateral versus direct anterior approach in 860 patients

Acta Orthopaedica, 2022
Background and purpose: In the past decade, a shift occurred in surgical total hip arthroplasty (THA) approaches to the posterolateral (PLA) and direct anterior approach (DAA).
Daisy A J M Latijnhouwers, Niels Laas, Suzan H M Verdegaal, Rob G H H Nelissen, Thea P M Vliet Vlieland, Herman H Kaptijn, Maaike G J Gademan   +6 more
doaj   +1 more source

An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework

Remote Sensing
The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression.
Kun Liu, Tong Wang, Weijun Huang
doaj   +1 more source

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

EURASIP Journal on Advances in Signal Processing, 2018
Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM).
Wei Wang, Lin Zou, Xuegang Wang, Yang Yang   +3 more
doaj   +1 more source

A Review of Radar Signal Processing Based on Sparse Recovery

Leida xuebao
With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN, Yaojun WU, Lining DUAN, Gang XU, Min XUE, Zhixing LIU, Mengdao XING   +6 more
doaj   +1 more source

Linear Regression Based Clutter Reconstruction for STAP

IEEE Access, 2018
Space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in a strong clutter background for airborne phased array radar systems.
Wei Zhang, Zishu He, Huiyong Li
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Fast and Gridless Sparse Recovery STAP Method Based on Nonconvex Relaxation of Atomic Norm

Leida xuebao
Sparse Recovery-based Space-Time Adaptive Processing (SR-STAP) methods offer significant advantages in nonhomogeneous clutter environments owing to their minimal requirement for training samples.
Linfeng CUI, Min WU, Chengpeng HAO, Jia LIU   +3 more
doaj   +1 more source