Results 21 to 30 of about 4,790 (193)

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   +2 more sources

Fourier-Sparsity Integrated Method for Complex Target ISAR Imagery

Sensors, 2015
In existing sparsity-driven inverse synthetic aperture radar (ISAR) imaging framework a sparse recovery (SR) algorithm is usually applied to azimuth compression to achieve high resolution in the cross-range direction.
Xunzhang Gao, Zhen Liu, Haowen Chen, Xiang Li   +3 more
doaj   +2 more sources

Industrial Data Denoising via Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine

Energies, 2022
The operation data of a tunnel boring machine (TBM) reflects its geological conditions and working status, which can provide critical references and essential information for TBM designers and operators.
Yitang Wang, Yong Pang, Wei Sun, Xueguan Song   +3 more
doaj   +2 more sources

An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

Remote Sensing, 2022
In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples.
Weichen Cui, Tong Wang, Degen Wang, Cheng Liu   +3 more
doaj   +2 more sources

A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar

Remote Sensing, 2023
Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance ...
Bing Ren, Tong Wang
doaj   +2 more sources

Sparse Representations-Based Super-Resolution of Key-Frames Extracted from Frames-Sequences Generated by a Visual Sensor Network

Sensors, 2014
Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications.
Muhammad Sajjad, Irfan Mehmood, Sung Wook Baik   +2 more
doaj   +2 more sources

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   +2 more sources

A novel sparse recovery space‐time adaptive processing algorithm using the log‐sum penalty to approximate the ℓ0 − norm penalty

IET Radar, Sonar & Navigation
Applying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter ...
Kun Liu, Tong Wang
doaj   +2 more sources

A Fast IAA–Based SR–STAP Method for Airborne Radar

Remote Sensing
Space–time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. When working in the heterogeneous environment, the number of training samples that satisfy independent and identically
Shuguang Zhang, Tong Wang, Cheng Liu, Bing Ren   +3 more
doaj   +2 more sources

Back in the US-SR: unlimited sampling and sparse super-resolution with Its hardware validation [PDF]

, 2022
The Unlimited Sensing Framework (USF) is a digital acquisition protocol that allows for sampling and reconstruction of high dynamic range signals. By acquiring modulo samples, the USF circumvents the clipping or saturation problem that is a fundamental ...
Bhandari, Ayush
core   +1 more source