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

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.

GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain

“…Chang et al [19] combined CS with subspace to enhance the acquisition performance of GNSS signal when there are a lot of interferences. He et al [20] designed a sparse dictionary based on the features of the GLONASS navigation signal and modified the greedy reconstruction algorithm to achieve fast acquisition. However, the above algorithms are based on the conventional frequency search method with a fixed bandwidth, which results in low acquisition probabilities under high dynamic conditions.…”

Coherent Target Direction‐of‐Arrival Estimation for Coprime Arrays: From Spatial Smoothing Perspective

“…[11]. In response to these aspects, corresponding theoretical and applied research has been carried out at home and abroad, which has enabled the rapid development of DOA estimation theory [12][13][14].…”