Abstract
The majority of 3G mobile phones have an integrated GPS chip enabling them to calculate a navigation solution. But to deliver continuous and accurate location information, the satellite tracking process has to be stable and reliable. This is still challenging, for example, in heavy multipath and non-line of sight (NLOS) environments. New families of Galileo and GPS navigation signals, such as Alternate Binary Offset Carrier (AltBOC), Composite Binary Offset Carrier (CBOC), and Time-Multiplex Binary Offset Carrier (TMBOC), will bring potential improvements in the pseudorange calculation, including more signal power, better multipath mitigation capabilities, and overall more robust navigation. However, GNSS signal tracking strategies have to be more advanced in order to profit from the enhanced properties of the new signals.In this paper, a tracking algorithm designed for Galileo E1 CBOC signal that consists of two steps, coarse and fine, with different tracking parameters in each step, is presented and analyzed with respect to tracking accuracy, sensitivity and robustness. The aim of this paper is therefore to provide a full theoretical analysis of the proposed two-step tracking algorithm for Galileo E1 CBOC signals, as well as to confirm the results through simulations as well as using real Galileo satellite data.
Highlights
New GPS and Galileo signals use new modulations, such as Alternate Binary Offset Carrier (AltBOC), Composite Binary Offset Carrier (CBOC), and Time-Multiplex Binary Offset Carrier (TMBOC) that have the potential to improve navigation through advanced signal properties, such as more signal power, better multipath mitigation capabilities, and overall improved signal cross-correlation properties
GNSS signal tracking strategies have to be more advanced in order to profit from the enhanced properties of the new signals.In this paper, a tracking algorithm designed for Galileo E1 CBOC signal that consists of two steps, coarse and fine, with different tracking parameters in each step, is presented and analyzed with respect to tracking accuracy, sensitivity and robustness
The data collection setup consists of a Spirent GSS8000 simulator [17] to emulate the Galileo E1 OS signal, a Fraunhofer triple-band front-end [18] with 18 MHz of bandwidth to filter, downconvert, and digitize the signal of interest (i.e., E1 CBOC signal), and a postprocessing architecture that contains a software receiver implemented in MATLAB, based on a modified version of the Kai Borre GPS L1 defined software radio [20]
Summary
New GPS and Galileo signals use new modulations, such as AltBOC, CBOC, and TMBOC that have the potential to improve navigation through advanced signal properties, such as more signal power, better multipath mitigation capabilities, and overall improved signal cross-correlation properties. The main algorithms that were proposed to address the problem of biased tracking for BOC(1,1) and can be applied to CBOC tracking too are Single Side Lobe, bump and jump algorithm [4], ASPeCT (Autocorrelation Side-Peak Cancellation Technique) [3], and Double Estimator [5, 6]. Note that the ASPeCT algorithm modifies the International Journal of Navigation and Observation shape of the autocorrelation function and eliminates side peaks that can be points of false locking It requires a different and more complex correlator architecture. Two outputs are linearly added, such that by changing the values of the weights the tracking can be modified [8] These two E1 CBOC tracking techniques assume separate correlations, which degrades the tracking and brings additional complexity. DP (Dot-Product) and HRC (High Resolution Correlator) discriminators are used in the fine tracking step, depending on the tracking conditions
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