Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication
Abstract The Shannon‐Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it wil...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-02-01
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| Series: | IET Signal Processing |
| Online Access: | https://doi.org/10.1049/sil2.12075 |
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| _version_ | 1832546734234927104 |
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| author | Zengkai Shi Yongshun Zhang Zhaoyong Qian Xiaoshuang Sun Xiangtai Ma |
| author_facet | Zengkai Shi Yongshun Zhang Zhaoyong Qian Xiaoshuang Sun Xiangtai Ma |
| author_sort | Zengkai Shi |
| collection | DOAJ |
| description | Abstract The Shannon‐Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon‐Nyquist sampling theorem‐based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods. |
| format | Article |
| id | doaj-art-6202009986ea43788fc83a175b7a54e4 |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-6202009986ea43788fc83a175b7a54e42025-02-03T06:47:17ZengWileyIET Signal Processing1751-96751751-96832022-02-01161142510.1049/sil2.12075Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communicationZengkai Shi0Yongshun Zhang1Zhaoyong Qian2Xiaoshuang Sun3Xiangtai Ma4Department of Space Information Space Engineering University Beijing ChinaUnit 32039 of PLA Beijing ChinaDepartment of Space Information Space Engineering University Beijing ChinaDepartment of Space Information Space Engineering University Beijing ChinaDepartment of Space Information Space Engineering University Beijing ChinaAbstract The Shannon‐Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon‐Nyquist sampling theorem‐based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.https://doi.org/10.1049/sil2.12075 |
| spellingShingle | Zengkai Shi Yongshun Zhang Zhaoyong Qian Xiaoshuang Sun Xiangtai Ma Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication IET Signal Processing |
| title | Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| title_full | Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| title_fullStr | Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| title_full_unstemmed | Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| title_short | Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| title_sort | compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication |
| url | https://doi.org/10.1049/sil2.12075 |
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