Derivation and Validation of a Higher-Fidelity GPS Correlator Model

Blake Baker and Scott Martin

Peer Reviewed

Abstract:	The process of validating GPS receiver algorithms can be quite time consuming due to the process of collecting or simulating signal data. The generation of navigation solutions with GPS signals requires the correlation of estimated signal replicas with a received signal. As a consequence, the only purpose of operating directly on received samples is to produce correlations. If the results of these correlations are modeled with sufficient accuracy, then the process of validating algorithms may be sped up by multiple orders of magnitude. Models presently exist to approximate the results of correlators; however, their shortcomings limit their range of application. Namely, they do not account for scenarios with significant higher order receiver dynamics that cause large changes in carrier and code frequencies. Additionally, standard correlator models do not address the cross-covariance between the additive noise of different correlators. This work derives and validates the accuracy of a new correlator model that addresses these issues. The proposed model is demonstrated to maintain a consistent level of accuracy for scenarios involving code and carrier phase errors that may be represented as first-order polynomials. In addition, an extension of the model is shown to accurately model more complex scenarios involving higher-order error terms. Index Terms—Satellite navigation systems, Global Positioning System, Mathematical models, Digital signal processing, Code division multiplexing
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	583 - 594
Cite this article:	Baker, Blake, Martin, Scott, "Derivation and Validation of a Higher-Fidelity GPS Correlator Model," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 583-594.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In