A Reduced Complexity Cross-correlation Interference Mitigation Technique on a Real-time Software-defined Radio GPS L1 Receiver

The U.S. global position system (GPS) is one of the existing global navigation satellite systems (GNSS) that provides position and time information for users in civil, commercial and military backgrounds. Because of its reliance on many applications nowadays, it’s crucial for GNSS receivers to have robustness to intentional or unintentional interference. Because most commercial GPS receivers are not flexible, software-defined radio emerged as a promising solution for fast prototyping and research on interference mitigation algorithms. This paper provides a proposed minimum mean-squared error (MMSE) interference mitigation technique which is enhanced for computational feasibility and implemented on a real-time capable GPS L1 SDR receiver. The GPS SDR receiver SW has been optimized for real-time operation on National Instruments’ LabVIEW (LV) platform in conjunction with C/C++ dynamic link libraries (DLL) for improved efficiency. Performance results of said algorithm with real signals and injected interference are discussed. The proposed SDR receiver gains in terms of BER curves for several interferers are demonstrated.

💡 Research Summary

This paper presents the design, implementation, and testing of a computationally efficient cross-correlation interference mitigation technique for a real-time Software-Defined Radio (SDR) GPS L1 receiver. Recognizing the growing vulnerability and criticality of GNSS systems to both intentional (jamming, spoofing) and unintentional interference, the research addresses the need for robust, yet flexible receiver solutions. It positions SDR as an ideal platform for prototyping such advanced algorithms, bridging the gap between the inflexibility of hardware ASICs and the high computational demands of software processing.

The core technical contribution is an enhanced Minimum Mean-Squared Error (MMSE) interference mitigation algorithm, adapted from prior literature for practical, real-time execution. The algorithm tackles the problem of Multiple Access Interference (MAI) in spread spectrum systems like GPS, where strong, cross-correlating signals from other sources can disrupt the synchronization and despreading of weak satellite signals. The proposed method innovates by modifying the local despreading replica code used in the receiver’s correlators. Instead of using a fixed, known PRN code replica, it adaptively adjusts this code to simultaneously perform its traditional synchronization function and act as an interference-suppressing filter. The optimal adjustments are found by minimizing a Mean Squared Error (MSE) cost function, which maximizes the Signal-to-Interference-plus-Noise Ratio (SINR).

A key challenge is the high computational complexity of solving this optimization problem for the full-length despreading code (e.g., 1024 dimensions). The paper’s primary innovation is the “reduced complexity group-weighting method.” This approach collapses the original N-dimensional optimization space into a smaller M-dimensional space by grouping consecutive chips of the despreading code. While this yields a sub-optimal solution compared to the full-complexity method, it drastically reduces computational load from O(N^3) to approximately O(M), making real-time implementation feasible. The trade-off between performance and complexity is controlled by the group size parameter, g.

The algorithm is integrated into a real-time capable GPS L1 SDR receiver built on the National Instruments LabVIEW platform. For computational efficiency, critical processing blocks are implemented as optimized C/C++ Dynamic Link Libraries (DLLs). The implementation details how the MMSE correlator is embedded within the standard GPS tracking loop (after carrier wipe-off), replacing a conventional integrate-and-dump filter. It involves a multi-stage process: chip pre-integration, upsampling to a power-of-two length, partial correlation grouping, recursive autocorrelation matrix estimation, calculation of the MMSE weight solution, and final integration for navigation bit extraction. The design ensures deterministic, real-time operation by processing fixed 1 ms data blocks.

An interference injection testbed was developed to evaluate performance using live GPS signals alongside simulated interference. Results demonstrate the system’s effectiveness. The receiver successfully mitigated interference signals with power levels up to 30 dB higher than the satellite signal power. Furthermore, it handled up to three simultaneous interferers, each at this 30 dB power advantage. The system was shown to operate in real-time for up to 12 tracking channels. These results validate the practical applicability of the reduced-complexity MMSE technique, proving that single-antenna, signal-processing-based interference mitigation can be powerful enough to counteract strong, spoofing-like interference in a real-time SDR framework, offering a cost-effective alternative to complex antenna array systems.