Multiple-input Multiple-output Radar and Sparse Array Synthetic Impulse and Aperture Radar

2006 ◽  
Author(s):  
Chen Duofang ◽  
Chen Baixiao ◽  
Zhang Shouhong
Keyword(s):  
Multiple Input Multiple Output ◽  
Sparse Array ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aperture Radar

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 Investigation on Beamspace Multiple-Input Multiple-Output Synthetic Aperture Radar Data Imaging

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Hongbo Mo ◽  
Wei Xu ◽  
Zhimin Zeng
Keyword(s):  
Synthetic Aperture Radar ◽  
Multiple Input Multiple Output ◽  
Synthetic Aperture ◽  
Raw Data ◽  
Burst Mode ◽  
Multiple Input ◽  
Null Steering ◽  
Imaging Approach ◽  
Input Multiple Output ◽  
Aperture Radar

The multiple-input multiple-output (MIMO) technique can improve the high-resolution wide-swath imaging capacity of synthetic aperture radar (SAR) systems. Beamspace MIMO-SAR utilizes multiple subpulses transmitted with different time delays by different transmit beams to obtain more spatial diversities based on the relationship between the time delay and the elevation angle in the side-looking radar imaging geometry. This paper presents a beamspace MIMO-SAR imaging approach, which takes advantage of real time digital beamforming (DBF) with null steering in elevation and azimuth multichannel raw data reconstruction. Echoes corresponding to different subpulses in the same subswath are separated by DBF with null steering onboard, while echoes received and stored by different azimuth channels are reconstructed by multiple Doppler reconstruction filters on the ground. Afterwards, the resulting MIMO-SAR raw data could be equivalent to the raw data of the single-channel burst mode, and classical burst mode imaging algorithms could be adopted to obtain final focused SAR images. Simulation results validate the proposed imaging approach.

scholarly journals A PE-MUSIC Algorithm for Sparse Array in MIMO Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yucai Pang ◽  
Song Liu ◽  
Yun He
Keyword(s):  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Music Algorithm ◽  
Sparse Arrays ◽  
Sparse Array ◽  
Direction Of Departure ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Array Aperture ◽  
The Cost

Larger array aperture is provided by sparse arrays than uniform ones, which can improve the angle estimation resolution and reduce the cost of system evidently. However, manifold ambiguity is introduced due to the array sparsity. In this paper, a Power Estimation Multiple-Signal Classification (PE-MUSIC) algorithm is proposed to solve the manifold ambiguity of arbitrary sparse arrays for uncorrelated sources in Multiple-Input Multiple-Output (MIMO) radar. First, the paired direction of departure (DOD) and direction of arrival (DOA) are obtained for all targets by MUSIC algorithm, including the true and spurious ones; then, the well-known Davidon–Fletcher–Powell (DFP) algorithm is applied to estimate all targets’ power values, among which the value of a spurious target trends to zero. Therefore, the ambiguity of sparse array in MIMO radar can be cleared. Simulation results verify the effectiveness and feasibility of the method.

scholarly journals Phaseless Radar Coincidence Imaging with a MIMO SAR Platform

2019 ◽  
Vol 11 (5) ◽  
pp. 533 ◽  
Author(s):  
Aaron Diebold ◽  
Mohammadreza Imani ◽  
David Smith
Keyword(s):  
Synthetic Aperture Radar ◽  
Imaging Technique ◽  
Multiple Input Multiple Output ◽  
Synthetic Aperture ◽  
Ghost Imaging ◽  
Microwave Frequencies ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Coincidence Imaging ◽  
Aperture Radar

The correlation-based synthetic aperture radar imaging technique, termed radar coincidence imaging, is extended to a fully multistatic multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) configuration. Within this framework, we explore two distinct processing schemes: incoherent processing of intensity data, obtained using asynchronous receivers and inspired by optical ghost imaging works, and coherent processing with synchronized array elements. Improvement in resolution and image quality is demonstrated in both cases using numerical simulations that model an airborne MIMO SAR system at microwave frequencies. Finally, we explore methods for reducing measurement times and computational loads through compressive and gradient image reconstruction using phaseless data.

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