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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Series: | International Journal of Antennas and Propagation |
Online Access: | http://dx.doi.org/10.1155/2018/2054271 |
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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 |
_version_ |
1716793641098280960 |