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A Fast Acquisition Algorithm Based on Division of GNSS Signals

Published online by Cambridge University Press:  02 February 2018

Qingxi Zeng*
Affiliation:
(Department of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, P. R. China)
Wenqi Qiu
Affiliation:
(Department of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, P. R. China)
Pengna Zhang
Affiliation:
(Department of Vehicle Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, P. R. China)
Xuefen Zhu
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Jiangsu, P.R. China)
Ling Pei
Affiliation:
(Shanghai Key Laboratory of Navigation and Location Based service, Shanghai Jiao tong University, Shanghai, P. R. China)
*
(E-mail: jslyzqx@163.com)

Abstract

The acquisition of signals is a precondition for tracking and solution calculation in software-based Global Navigation Satellite System (GNSS) receivers. The Parallel Code phase Acquisition (PCA) algorithm can simultaneously obtain the correlation results at every sampling point. However, if the number of sampling points that needs processing is large, this method will lead to a heavy computational load. Thus, we improve the process of the PCA algorithm and propose a novel algorithm that divides the signals into K (K is a constant) parts to achieve correlation and obtains the correlation results with a fusion algorithm. This algorithm can simultaneously obtain the correlation results for sampling points at an interval of K points. If the K value is selected appropriately, the computational load can be decreased by about 50%. Also, the Receiver Operating Characteristic (ROC) curves show that under a certain probability of false alarm, the detection probability of the proposed algorithms is 5% lower than that of the PCA algorithm. Therefore, the proposed algorithm can speed up the acquisition process with a slight decrease in detection probability.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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