CSSR: a 2For1 Compressive Sensing Software Receiver with Combined Correlation For GPS-CA and Galileo-OS Signals

Albu-Rghaif, Ali and Lami, Ihsan (2015) CSSR: a 2For1 Compressive Sensing Software Receiver with Combined Correlation For GPS-CA and Galileo-OS Signals. The Institute of Navigation. (Submitted)

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Abstract

This is a 2for1 receiver because it acquires both GPS and Galileo signals at less than 50% of the complexity and processing time required by a Matched Filter acquisition receiver. CSSR is a new implementation of a dual-GNSS-signal Software Receiver using, for the first time, the compressive sensing technique to process the two GNSS signals at the same time (in this implementation, GPS C/A code and Galileo OS code signals are used). This paper describes this CSSR implementation, focusing on: (a) how we remove the subcarrier frequency effect from the Galileo signal, and combine it with the GPS signal as a BPSK like signal; (b) the pre-processing stage of the resultant BPSK signal to generate the non-Doppler shift vectors that compensates for the matching measurements in the compressive sensing process; and finally (c) the compressive sensing process to acquire both signals simultaneously by combining their dictionaries, or correlators. CSSR has been simulated using various actual signal conditions/scenarios. The results are compared to those obtained from running the same tests on 3-other matched filter receivers. CSSR achieves similar probability of detection to the others, and has a higher frequency resolution of 10Hz for the same 4ms dwell time. With Application processors on-board Smartphones getting more powerful and cheaper, and with 60% of the 3.1 billion dual GNSS offering on-board current Smartphones are based on side-by-side implementations, we believe that CSSR is a good candidate to saving cost and valuable battery energy when implemented on-board Smartphones.

Item Type: Article
Uncontrolled Keywords: GPS-Galileo signal acquisition; Compressive sensing; Dictionary matrix
Subjects: Q Science > Q Science (General)
Divisions: School of Science > Applied Computing
Depositing User: Ihsan Lami
Date Deposited: 03 Aug 2016 09:14
Last Modified: 03 Aug 2016 09:14
URI: http://bear.buckingham.ac.uk/id/eprint/61

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