AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System

In this paper, we propose a solution to find the angle of arrival (AOA), delay, and the complex propagation factor for the monostatic multiple-input multiple-output (MIMO) radar system. In contrast to conventional iterative computationally demanding estimation schemes, we propose a closed form solut...

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Main Authors: Saleh O. Al-Jazzar, Sami Aldalahmeh
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2018/2054271
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spelling doaj-ce1235fa89a74c11a2041a28958d4d072020-11-24T20:54:41ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58691687-58772018-01-01201810.1155/2018/20542712054271AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar SystemSaleh O. Al-Jazzar0Sami Aldalahmeh1Al-Zaytoonah University of Jordan, Amman, JordanAl-Zaytoonah University of Jordan, Amman, JordanIn this paper, we propose a solution to find the angle of arrival (AOA), delay, and the complex propagation factor for the monostatic multiple-input multiple-output (MIMO) radar system. In contrast to conventional iterative computationally demanding estimation schemes, we propose a closed form solution for most of the previous parameters. The solution is based on forming an approximate correlation matrix of the received signals at the MIMO radar receiver end. Then, an eigenvalue decomposition (EVD) is performed on the formed approximate correlation matrix. The AOAs of the received signals are deduced from the corresponding eigenvectors. Then, the delays are estimated from the received signal matrix properties. This is followed by forming structured matrices which will be used to find the complex propagation factors. These estimates can be used as initializations for other MIMO radar methods, such as the maximum likelihood algorithm. Simulation results show significantly low root mean square error (RMSE) for AOAs and complex propagation factors. On the other hand, our proposed method achieves zero RMSE in estimating the delays for relatively low signal-to-noise ratios (SNRs).http://dx.doi.org/10.1155/2018/2054271
collection DOAJ
language English
format Article
sources DOAJ
author Saleh O. Al-Jazzar
Sami Aldalahmeh
spellingShingle Saleh O. Al-Jazzar
Sami Aldalahmeh
AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
International Journal of Antennas and Propagation
author_facet Saleh O. Al-Jazzar
Sami Aldalahmeh
author_sort Saleh O. Al-Jazzar
title AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
title_short AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
title_full AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
title_fullStr AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
title_full_unstemmed AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System
title_sort aoa, delay, and complex propagation factor estimation for the monostatic mimo radar system
publisher Hindawi Limited
series International Journal of Antennas and Propagation
issn 1687-5869
1687-5877
publishDate 2018-01-01
description In this paper, we propose a solution to find the angle of arrival (AOA), delay, and the complex propagation factor for the monostatic multiple-input multiple-output (MIMO) radar system. In contrast to conventional iterative computationally demanding estimation schemes, we propose a closed form solution for most of the previous parameters. The solution is based on forming an approximate correlation matrix of the received signals at the MIMO radar receiver end. Then, an eigenvalue decomposition (EVD) is performed on the formed approximate correlation matrix. The AOAs of the received signals are deduced from the corresponding eigenvectors. Then, the delays are estimated from the received signal matrix properties. This is followed by forming structured matrices which will be used to find the complex propagation factors. These estimates can be used as initializations for other MIMO radar methods, such as the maximum likelihood algorithm. Simulation results show significantly low root mean square error (RMSE) for AOAs and complex propagation factors. On the other hand, our proposed method achieves zero RMSE in estimating the delays for relatively low signal-to-noise ratios (SNRs).
url http://dx.doi.org/10.1155/2018/2054271
work_keys_str_mv AT salehoaljazzar aoadelayandcomplexpropagationfactorestimationforthemonostaticmimoradarsystem
AT samialdalahmeh aoadelayandcomplexpropagationfactorestimationforthemonostaticmimoradarsystem
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