GPS Sparse Multipath Signal Estimation Based on Compressive Sensing | Zendy

Guodong He | Zendy; Maozhong Song | Zendy; Shanshan Zhang | Zendy; Huiping Qin | Zendy; Xiaojuan Xie | Zendy

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GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

Author(s) -

Guodong He,

Maozhong Song,

Shanshan Zhang,

Huiping Qin,

Xiaojuan Xie

Publication year - 2021

Publication title -

wireless communications and mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.42

H-Index - 64

eISSN - 1530-8677

pISSN - 1530-8669

DOI - 10.1155/2021/5583429

Subject(s) - multipath propagation , computer science , compressed sensing , algorithm , signal reconstruction , global positioning system , norm (philosophy) , signal (programming language) , gps signals , signal processing , assisted gps , telecommunications , channel (broadcasting) , radar , political science , law , programming language

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

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