Results 11 to 20 of about 4,790 (193)

An Overview on Sparse Recovery-based STAP

Leida xuebao, 2014
This paper gives a brief review on the Sparse-Recovery (SR)-based Space-Time Adaptive Processing (STAP) technique. First, the motivation for introducing sparse recovery into STAP is presented.
Ma Ze-qiang, Wang Xi-qin, Liu Yi-min, Meng Hua-dong   +3 more
doaj   +2 more sources

Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array

Sensors, 2018
In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed.
Jianfeng Li, Zheng Li, Xiaofei Zhang
doaj   +2 more sources

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

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   +2 more sources

A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects

Remote Sensing, 2022
Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots.
Cheng Liu, Tong Wang, Kun Liu, Xinying Zhang   +3 more
doaj   +2 more sources

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

Sensors, 2021
In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector
Kun Liu, Tong Wang, Jianxin Wu, Jinming Chen   +3 more
doaj   +2 more sources

Meshless Search SR-STAP for Airborne Radar Based on Meta-Heuristic Algorithms

Sensors, 2023
The sparse recovery (SR) space-time adaptive processing (STAP) method has excellent clutter suppression performance under the condition of limited observation samples.
Yunfei Hou, Yingnan Zhang, Wenzhu Gui, Di Wang, Wei Dong   +4 more
doaj   +2 more sources

Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method

Leida xuebao, 2021
When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter.
Ning CUI, Kun XING, Keqing DUAN, Zhongjun YU   +3 more
doaj   +2 more sources

DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression

Remote Sensing, 2022
With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar.
Bo Zou, Xin Wang, Weike Feng, Hangui Zhu, Fuyu Lu   +4 more
doaj   +2 more sources

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   +2 more sources

A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar

Sensors, 2022
Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient
Cheng Liu, Tong Wang, Shuguang Zhang, Bing Ren   +3 more
doaj   +2 more sources