scholarly journals Compressive sensing-based super-resolution DOA estimation for mechanical scanning radar

2019 ◽  
Vol 2019 (21) ◽  
pp. 7800-7804
Author(s):  
Fanglei Cheng ◽  
Hongyu Wang ◽  
Yang Li
Keyword(s):  
Compressive Sensing ◽  
Super Resolution ◽  
Doa Estimation ◽  
Mechanical Scanning

scholarly journals A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hamid Ali Mirza ◽  
Laeeq Aslam ◽  
Muhammad Asif Zahoor Raja ◽  
Naveed Ishtiaq Chaudhary ◽  
Ijaz Mansoor Qureshi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Fitness Function ◽  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Direction Of Arrival Estimation ◽  
Grid Refinement ◽  
Computing Paradigm ◽  
The Difference

In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

Experimental Study of Super-Resolution Using a Compressive Sensing Architecture

2015 ◽  
Author(s):  
J. C. Flake ◽  
Gary Euliss ◽  
John B. Greer ◽  
Stephanie Shubert ◽  
Glenn Easley ◽  
...  
Keyword(s):  
Experimental Study ◽  
Compressive Sensing ◽  
Super Resolution

The Cotton Aphids Counting System with Super Resolution and Compressive Sensing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Min Tian ◽  
Pei Lu ◽  
Xiaoyong Liu ◽  
Xi Lu
Keyword(s):  
Compressive Sensing ◽  
Super Resolution ◽  
Counting System

SISSI Project: A Feasibility Study for a Super Resolved Compressive Sensing Multispectral Imager in the Medium Infrared

2021 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Cinzia Lastri ◽  
Gabriele Amato ◽  
Massimo Baldi ◽  
Tiziano Bianchi ◽  
Maria Fabrizia Buongiorno ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressive Sensing ◽  
Feasibility Study ◽  
Spatial Resolution ◽  
Spatial Light Modulator ◽  
Super Resolution ◽  
Earth Observation ◽  
Light Modulator ◽  
The Core ◽  
Multispectral Imager

This paper describes the activities related to a feasibility study for an Earth observation optical payload, operating in the medium infrared, based on super-resolution and compressive sensing techniques. The presented activities are running in the framework of the ASI project SISSI, aiming to improve ground spatial resolution and mitigate saturation/blooming effects. The core of the payload is a spatial light modulator (SLM): a bidimensional array of micromirrors electronically actuated. Thanks to compressive sensing approach, the proposed payload eliminates the compression board, saving mass, memory and energy consumption.

scholarly journals Two-Step Root-MUSIC for Direction of Arrival Estimation without EVD/SVD Computation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Feng-Gang Yan ◽  
Shuai Liu ◽  
Jun Wang ◽  
Ming Jin
Keyword(s):  
Super Resolution ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Low Complexity ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Noise Subspace ◽  
Using Data ◽  
Eigen Decomposition ◽  
Value Decomposition

Most popular techniques for super-resolution direction of arrival (DOA) estimation rely on an eigen-decomposition (EVD) or a singular value decomposition (SVD) computation to determine the signal/noise subspace, which is computationally expensive for real-time applications. A two-step root multiple signal classification (TS-root-MUSIC) algorithm is proposed to avoid the complex EVD/SVD computation using a uniform linear array (ULA) based on a mild assumption that the number of signals is less than half that of sensors. The ULA is divided into two subarrays, and three noise-free cross-correlation matrices are constructed using data collected by the two subarrays. A low-complexity linear operation is derived to obtain a rough noise subspace for a first-step DOA estimate. The performance is further enhanced in the second step by using the first-step result to renew the previous estimated noise subspace with a slightly increased complexity. The new technique can provide close root mean square error (RMSE) performance to root-MUSIC with reduced computational burden, which are verified by numerical simulations.

Temporal super resolution of ultrasound images using compressive sensing

2019 ◽  
Vol 52 ◽  
pp. 53-68 ◽  
Author(s):  
Mina Hosseinpour ◽  
Hamid Behnam ◽  
Maryam Shojaeifard
Keyword(s):  
Compressive Sensing ◽  
Super Resolution ◽  
Ultrasound Images

Rapid real-time generation of super-resolution hyperspectral images through compressive sensing and GPU

2016 ◽  
Vol 37 (18) ◽  
pp. 4201-4224 ◽  
Author(s):  
Marwa Moustafa ◽  
Hala M. Ebeid ◽  
Ashraf Helmy ◽  
Taymoor M. Nazmy ◽  
Mohamed F. Tolba
Keyword(s):  
Compressive Sensing ◽  
Real Time ◽  
Super Resolution ◽  
Hyperspectral Images
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