Results 21 to 30 of about 47,616 (177)
IET Radar, Sonar & Navigation, 2021 Sparse recovery (SR) based space‐time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples.
Zhongyue Li, Tong Wang, Yuyu Su
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Maximal Dependence Capturing as a Principle of Sensory Processing
Frontiers in Computational Neuroscience, 2022 Sensory inputs conveying information about the environment are often noisy and incomplete, yet the brain can achieve remarkable consistency in recognizing objects.
Rishabh Raj +4 more
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A Dynamical System With Fixed Convergence Time for Sparse Recovery
IEEE Access, 2019 The sparse recovery (SR) algorithm, under the premise that signals are sparse, can be divided into two categories. One is a digital discrete method implemented via lots of iterative computations and the other is a continuous method implemented via analog
Junying Ren +4 more
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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 +6 more
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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
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Radiometric Constraints on the Timing, Tempo, and Effects of Large Igneous Province Emplacement
Geophysical Monograph Series, Page 27-82., 2021 Exploring the links between Large Igneous Provinces and dramatic environmental impact
An emerging consensus suggests that Large Igneous Provinces (LIPs) and Silicic LIPs (SLIPs) are a significant driver of dramatic global environmental and biological changes, including mass extinctions.
Jennifer Kasbohm +2 more
wiley +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 +3 more
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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 +6 more
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Remote SensingThe 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
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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 +3 more
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