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2022 | OriginalPaper | Chapter

Acquisition Algorithm Optimization Based on Amount of Computation and Performance Joint

Authors : Pengcheng Zhang, Xinming Huang, Jingyuan Li, Zengjun Liu, Gang Ou

Published in: China Satellite Navigation Conference (CSNC 2022) Proceedings

Publisher: Springer Nature Singapore

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Abstract

Signal acquisition is the prerequisite for the receiver’s work. The current optimization of the signal acquisition algorithm is mainly focused on the improvement of the acquisition performance, and the optimization of the signal acquisition calculation amount is relatively ignored. Aiming at the computational optimization problem of the navigation signal acquisition algorithm, this paper establishes the equivalent detection capability factor under the unit computational complexity, determines the optimal search interval, and establishes the computational model of the acquisition process. The three common signal acquisition search algorithms are analyzed in detail, and the joint optimization of signal acquisition performance and computational complexity is established. In this paper, a joint optimization function of signal acquisition performance and computational complexity is established, the optimal pre-detection integration time is determined, and the acquisition algorithm is optimized. For the parallel acquisition algorithm in the frequency domain, the acquisition performance can be optimized by only about 1.87 dB, and nearly 60% of the acquisition calculation amount can be optimized, sacrificing part of the acquisition performance in exchange for a smaller computational complexity. The acquisition algorithm optimization method in this paper can alleviate the shortage of computing resources caused by the rapid increase in the number of signals during the development of low-orbit systems, and can also be applied to low-power receivers to reduce the resources consumed by acquisition.

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Metadata
Title
Acquisition Algorithm Optimization Based on Amount of Computation and Performance Joint
Authors
Pengcheng Zhang
Xinming Huang
Jingyuan Li
Zengjun Liu
Gang Ou
Copyright Year
2022
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-2580-1_54