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A SINS‐aided two‐step fast acquisition method for GNSS signal based on compressive sensing
Author(s) -
Cheng Junbing,
Li Dengao
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5369
Subject(s) - gnss applications , computer science , inertial navigation system , compressed sensing , satellite system , satellite navigation , real time computing , signal (programming language) , electronic engineering , global positioning system , algorithm , engineering , inertial frame of reference , telecommunications , physics , quantum mechanics , programming language
Summary The Global navigation satellite system (GNSS) multi‐mode compatible and multi‐frequency information not only improves information redundancy but also eliminates errors such as ionospheric delays to improve positioning performance. However, multi‐mode and multi‐frequency observation information increases the hardware overhead and computational complexity, especially the signal acquisition. In this contribution, according to the strong complementarity between GNSS and Strapdown inertial navigation system (SINS) and the sparsity of the GNSS signal spreading code in the autocorrelation domain, a SINS‐aided fast acquisition method for GNSS signals based on Compressive sensing (CS) is proposed. First, the inertia information of SINS narrows the Doppler frequency search range; then, the CS is applied to measure compressively the GNSS signal in two steps, ie, the first step is to narrow down the code phase search range, and the second step is to determine the code phase of the half chip accuracy. Finally, the proposed method and the other two typical acquisition methods are compared by the detection probability and the Mean acquisition time (MAT). Theoretical analysis and experimental results show that the proposed method can not only reduce the number of correlators and computational complexity but also improve the detection probability and reduce the MAT.