A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network
AbstractConventional 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
Share & Cite This Article
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.
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(5):1482.Chicago/Turabian Style
Wang, Zhen; Zhuang, Yuan; Yang, Jun; Zhang, Hengfeng; Dong, Wei; Wang, Min; Hua, Luchi; Liu, Bo; Shi, Longxing. 2018. "A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network." Sensors 18, no. 5: 1482.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.