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Open AccessArticle

A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network

National ASIC System Engineering Research Center, Southeast University, 2 Sipailou, Nanjing 210096, China
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Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1482; https://doi.org/10.3390/s18051482
Received: 18 February 2018 / Revised: 22 April 2018 / Accepted: 25 April 2018 / Published: 9 May 2018
(This article belongs to the Collection Positioning and Navigation)
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. View Full-Text
Keywords: GPS acquisition; binarized convolution neural network; high sensitivity; double dwell GPS acquisition; binarized convolution neural network; high sensitivity; double dwell
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MDPI and ACS Style

Wang, Z.; Zhuang, Y.; Yang, J.; Zhang, H.; Dong, W.; Wang, M.; Hua, L.; Liu, B.; Shi, L. A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network. Sensors 2018, 18, 1482.

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