A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network
Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize the GPS acquisition correlation envelope in this a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/5/1482 |
| _version_ | 1798038958309900288 |
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| author | Zhen Wang Yuan Zhuang Jun Yang Hengfeng Zhang Wei Dong Min Wang Luchi Hua Bo Liu Longxing Shi |
| author_facet | Zhen Wang Yuan Zhuang Jun Yang Hengfeng Zhang Wei Dong Min Wang Luchi Hua Bo Liu Longxing Shi |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize the GPS acquisition correlation envelope in this article. The proposed method is a double dwell acquisition in which a short integration is adopted in the first dwell and a long integration is applied in the second one. To reduce the search space for parameters, BCNN detects the possible envelope which contains the auto-correlation peak in the first dwell to compress the initial search space to 1/1023. Although there is a long integration in the second dwell, the acquisition computation overhead is still low due to the compressed search space. Comprehensively, the total computation overhead of the proposed method is only 1/5 of conventional ones. Experiments show that the proposed double dwell/correlation envelope identification (DD/CEI) neural network achieves 2 dB improvement when compared with the MAX/TC under the same specification. |
| first_indexed | 2024-04-11T21:47:22Z |
| format | Article |
| id | doaj.art-0bf603fd34244e8486ca633126335540 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:47:22Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0bf603fd34244e8486ca6331263355402022-12-22T04:01:23ZengMDPI AGSensors1424-82202018-05-01185148210.3390/s18051482s18051482A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural NetworkZhen Wang0Yuan Zhuang1Jun Yang2Hengfeng Zhang3Wei Dong4Min Wang5Luchi Hua6Bo Liu7Longxing Shi8National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaNational ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, ChinaConventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize the GPS acquisition correlation envelope in this article. The proposed method is a double dwell acquisition in which a short integration is adopted in the first dwell and a long integration is applied in the second one. To reduce the search space for parameters, BCNN detects the possible envelope which contains the auto-correlation peak in the first dwell to compress the initial search space to 1/1023. Although there is a long integration in the second dwell, the acquisition computation overhead is still low due to the compressed search space. Comprehensively, the total computation overhead of the proposed method is only 1/5 of conventional ones. Experiments show that the proposed double dwell/correlation envelope identification (DD/CEI) neural network achieves 2 dB improvement when compared with the MAX/TC under the same specification.http://www.mdpi.com/1424-8220/18/5/1482GPS acquisitionbinarized convolution neural networkhigh sensitivitydouble dwell |
| spellingShingle | Zhen Wang Yuan Zhuang Jun Yang Hengfeng Zhang Wei Dong Min Wang Luchi Hua Bo Liu Longxing Shi A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network Sensors GPS acquisition binarized convolution neural network high sensitivity double dwell |
| title | A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network |
| title_full | A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network |
| title_fullStr | A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network |
| title_full_unstemmed | A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network |
| title_short | A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network |
| title_sort | double dwell high sensitivity gps acquisition scheme using binarized convolution neural network |
| topic | GPS acquisition binarized convolution neural network high sensitivity double dwell |
| url | http://www.mdpi.com/1424-8220/18/5/1482 |
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