Multiple‐input–multiple‐output radar super‐resolution three‐dimensional imaging based on a dimension‐reduction compressive sensing

2016 ◽  
Vol 10 (4) ◽  
pp. 757-764 ◽  
Author(s):  
Xiaowei Hu ◽  
Ningning Tong ◽  
Yongshun Zhang ◽  
Guoping Hu ◽  
Xingyu He
Keyword(s):  
Compressive Sensing ◽  
Dimension Reduction ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Super Resolution ◽  
Three Dimensional Imaging ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals Watchstrap-Embedded Four-Element Multiple-Input–Multiple-Output Antenna Design for a Smartwatch in 5.2–5.8 GHz Wireless Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Chia-Hao Wu ◽  
Jwo-Shiun Sun ◽  
Bo-Shiun Lu
Keyword(s):  
Ground Plane ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Antenna Design ◽  
Mimo Antenna ◽  
Wireless Applications ◽  
Plastic Materials ◽  
Monopole Antennas ◽  
Multiple Input ◽  
Input Multiple Output

This paper presents a compact four-element multiple-input–multiple-output (MIMO) antenna design operating within the WiFi 802.11 ac bands (5.2–5.84 GHz) for a smartwatch. The antenna is fabricated using a polyamide substrate and embedded into the strap of a smartwatch model; the strap is created using three-dimensional etching of plastic materials. The four-element MIMO antenna is formed by four monopole antennas, has a simple structure, and is connected to the system ground plane of the smartwatch. Due to the stub and notched block between two antennas and the slit in the system ground, the four-element MIMO antenna exhibits favorable isolation. Moreover, the envelope correlation coefficient of the antennas is considerably lower than 0.005 in the operating band. The measured −6 dB impedance bandwidths of the four elements of the antenna (Ant1–Ant4) with the human wrist encompass the WiFi 802.11 ac range of 5.2–5.84 GHz; moreover, an isolation of more than 20 dB is achieved. The measured antenna efficiency with and without a phantom hand are 45%–55% and 93%–97%, respectively.

scholarly journals A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4839
Author(s):  
Kong ◽  
Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Covariance Matrix ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Estimation Accuracy ◽  
Sample Covariance ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aperture Radar

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

scholarly journals A Novel MIMO–SAR Solution Based on Azimuth Phase Coding Waveforms and Digital Beamforming

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3374 ◽  
Author(s):  
Fang Zhou ◽  
Jiaqiu Ai ◽  
Zhangyu Dong ◽  
Jiajia Zhang ◽  
Mengdao Xing
Keyword(s):  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Spatial Diversity ◽  
Reconstruction Method ◽  
Digital Beamforming ◽  
Phase Coding ◽  
Imaging Result ◽  
Multiple Input ◽  
Input Multiple Output ◽  
The Time Domain

In multiple-input multiple-output synthetic aperture radar (MIMO–SAR) signal processing, a reliable separation of multiple transmitted waveforms is one of the most important and challenging issues, for the unseparated signal will degrade the performance of most MIMO–SAR applications. As a solution to this problem, a novel APC–MIMO–SAR system is proposed based on the azimuth phase coding (APC) technique to transmit multiple waveforms simultaneously. Although the echo aliasing occurs in the time domain and Doppler domain, the echoes can be separated well without performance degradation by implementing the azimuth digital beamforming (DBF) technique, comparing to the performance of the orthogonal waveforms. The proposed MIMO–SAR solution based on the APC waveforms indicates the feasibility and the spatial diversity of the MIMO–SAR system. It forms a longer baseline in elevation, which gives the potential to expand the application of MIMO–SAR in elevation, such as improving the performance of multibaseline InSAR and three-dimensional SAR imaging. Simulated results on both a point target and distributed targets validate the effectiveness of the echo separation and reconstruction method with the azimuth DBF. The feasibility and advantage of the proposed MIMO–SAR solution based on the APC waveforms are demonstrated by comparing with the imaging result of the up- and down-chirp waveforms.

scholarly journals Compressed Sensing in On-Grid MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Michael F. Minner
Keyword(s):  
Compressive Sensing ◽  
Regularization Method ◽  
Sensor Array ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Sparsity Constraints ◽  
Recovery Algorithms

The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar. Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task. The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler. Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation. We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets. Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on thel1-squared Nonnegative Regularization method.

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