Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

Maozhong Song,Wang Zheng,Xinwen Shu,Shanshan Zhang,Peng Song,Guodong He

doi:10.1155/2020/9654120

Abstract

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

Highlights

Global navigation satellite system (GLONASS), which was developed by the Soviet Union first, is maintained and developed by Russia now. e purpose of GLONASS is to provide unlimited number of air, marine, and any other type of users with all-weather three-dimensional positioning, velocity measuring, and timing anywhere in the world or near-earth space [1]
Numerical simulations illustrate that the method can recover the global position system (GPS) sparse spike signal effectively. e application of compressive sensing in global navigation satellite system (GNSS) is limited to GPS with single measurement vector (SMV), and until now, the application in GLONASS with measurement vectors (MMVs) is not reported
A sparse dictionary corresponding to the satellite is designed according to the satellite PR ranging code and intermediate frequency. e satellite signal has been measured by the same random matrix first, which is compressed largely. en, a MMV reconstruction algorithm is used for the acquisition of the satellite. e new acquisition method is mainly composed of two parts: sparse dictionary and reconstruction algorithm

Summary

Introduction

Global navigation satellite system (GLONASS), which was developed by the Soviet Union first, is maintained and developed by Russia now. e purpose of GLONASS is to provide unlimited number of air, marine, and any other type of users with all-weather three-dimensional positioning, velocity measuring, and timing anywhere in the world or near-earth space [1]. E aim of acquisition is to estimate the coarse spread spectrum code phase and Doppler frequency of the satellite in the field of view [5]. E acquisition is a two-dimensional searching process, which needs to search all the likely spread spectrum code phase and Doppler frequency at the same time. E serial acquisition method is a common method, which needs to reconstruct all possible code phase and Doppler frequency and correlate with the satellite signal. Several CS-based methods have been improved for global navigation satellite system (GNSS) signal acquisition. A new GLONASS signal acquisition method based on CS and MMV is proposed.

Compressive Sensing and Multiple Measurement Vectors

GLONASS Signal and Acquisition

GLONASS Signal Acquisition Based on CS and MMV

Conclusions