Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm
Minimum Variance Distortionless Response (MVDR) is basically a unity gain adaptive beamformer which is suffered from performance degradation due to the presence of interference and noise. Also, MVDR is sensitive to errors such as the steering vector errors, and the nulling level. MVDR combined with...
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| Format: | Article |
| Language: | English |
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Informatics Publishing Limited
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/16866/7/fkee-2016-ayib-Evaluating%20the%20Impact%20of%20SNOIs.pdf |
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| author | Shahab, Suhail Najm Ayib Rosdi, Zainun Izzeldin, I. Mohd Alabdraba, Waleed M. Sh. Nurul Hazlina, Noordin |
| author_facet | Shahab, Suhail Najm Ayib Rosdi, Zainun Izzeldin, I. Mohd Alabdraba, Waleed M. Sh. Nurul Hazlina, Noordin |
| author_sort | Shahab, Suhail Najm |
| collection | UMP |
| description | Minimum Variance Distortionless Response (MVDR) is basically a unity gain adaptive beamformer which is suffered from performance degradation due to the presence of interference and noise. Also, MVDR is sensitive to errors such as the steering vector errors, and the nulling level. MVDR combined with a Linear Antenna Array (LAA) is used to acquire desired signals and suppress the interference and noise. This paper examines the impact of the number of interference sources and the mainlobe accuracy by using Signal to Interference plus Noise Ratio (SINR) and array beampattern as two different Figure-of-Merits to measure the performance of the MVDR beamformer with a fixed number of array elements (L). The findings of this study indicate that the MVDR successfully form a nulls to L-1 nonlook signal with average SINR of 49.31 dB. Also, the MVDR provides accurate mainlobe with a small change to the real user direction when the SNOIs are bigger than array elements. The proposed method was found to perform better than some existing techniques. Based on this analysis, the beampattern not heavily relies on the number of unwanted source. Moreover, the SINR strongly depends on the number of SNOIs and the nulling level. |
| first_indexed | 2024-03-06T12:13:24Z |
| format | Article |
| id | UMPir16866 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:13:24Z |
| publishDate | 2016 |
| publisher | Informatics Publishing Limited |
| record_format | dspace |
| spelling | UMPir168662018-04-12T07:08:10Z http://umpir.ump.edu.my/id/eprint/16866/ Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm Shahab, Suhail Najm Ayib Rosdi, Zainun Izzeldin, I. Mohd Alabdraba, Waleed M. Sh. Nurul Hazlina, Noordin TK Electrical engineering. Electronics Nuclear engineering Minimum Variance Distortionless Response (MVDR) is basically a unity gain adaptive beamformer which is suffered from performance degradation due to the presence of interference and noise. Also, MVDR is sensitive to errors such as the steering vector errors, and the nulling level. MVDR combined with a Linear Antenna Array (LAA) is used to acquire desired signals and suppress the interference and noise. This paper examines the impact of the number of interference sources and the mainlobe accuracy by using Signal to Interference plus Noise Ratio (SINR) and array beampattern as two different Figure-of-Merits to measure the performance of the MVDR beamformer with a fixed number of array elements (L). The findings of this study indicate that the MVDR successfully form a nulls to L-1 nonlook signal with average SINR of 49.31 dB. Also, the MVDR provides accurate mainlobe with a small change to the real user direction when the SNOIs are bigger than array elements. The proposed method was found to perform better than some existing techniques. Based on this analysis, the beampattern not heavily relies on the number of unwanted source. Moreover, the SINR strongly depends on the number of SNOIs and the nulling level. Informatics Publishing Limited 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16866/7/fkee-2016-ayib-Evaluating%20the%20Impact%20of%20SNOIs.pdf Shahab, Suhail Najm and Ayib Rosdi, Zainun and Izzeldin, I. Mohd and Alabdraba, Waleed M. Sh. and Nurul Hazlina, Noordin (2016) Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm. Indian Journal of Science and Technology, 9 (30). pp. 1-9. ISSN 0974-6846 (Print); 0974-5645 (Online). (Published) http://52.172.159.94/index.php/indjst/article/view/99275 DOI: 10.17485/ijst/2016/v9i30/99275 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Shahab, Suhail Najm Ayib Rosdi, Zainun Izzeldin, I. Mohd Alabdraba, Waleed M. Sh. Nurul Hazlina, Noordin Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title | Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title_full | Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title_fullStr | Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title_full_unstemmed | Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title_short | Evaluating the Impact of SNOIs on SINR and Beampattern of MVDR Adaptive Beamforming Algorithm |
| title_sort | evaluating the impact of snois on sinr and beampattern of mvdr adaptive beamforming algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/16866/7/fkee-2016-ayib-Evaluating%20the%20Impact%20of%20SNOIs.pdf |
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