The global satellite navigation system represented by global position systems (GPS) has been widely used in civil and military fields, and has become an important cornerstone of space-time information services. However, the frequency band of satellite navigation signals is open, and the frequency points overlap with some radars and communication systems, which brings challenges to the application of satellite navigation. Time-domain adaptive filtering technology is a typical anti-jamming method which can suppress the narrow-band interference faced by satellite navigation. However, in the process of suppressing narrow-band interference, the navigation signal will be distorted, which is mainly reflected in the distortion of the spectrum of the navigation signal, which will lead to the enhancement of the side lobes in the correlation function. In this paper, we focus on time-domain adaptive anti-jamming, study the mechanism of correlation function sidelobes lift caused by narrow-band interference suppression, and propose a correlation function sidelobes suppression method based on time-domain adaptive anti-jamming, which can be realized without losing anti-jamming performance. The simulation experiment verifies the validity of the mechanism analysis of the sidelobes lift of the correlation function and the effectiveness of the proposed method. The analysis results and the proposed method are of great significance, which is reflected in the improvement of the anti-jamming performance and acquisition performance of satellite navigation receivers.

With the rapid advancement of pulse technology, given the extraordinarily high repetition frequency of high-repetition pulse interference, with pulse widths typically ranging from a few nanoseconds to several hundred nanoseconds, it possesses ultra-wideband characteristics, research on how to avoid spectral conflict and malicious interference between these pulse signals and navigation receiver systems has emerged as a pressing and popular issue. This paper introduces the mathematical model of high repetition pulse interference signals. Following this, the paper summarizes and analyses the transient response, nonlinear distortion, and linear distortion that accompany signal processing at the Radio Frequency (RF) front end of the receiver. It concludes that the main source of interference in the limiter’s transient response is peak leakage, the primary factor in low noise amplifier’s (LNA) interference is third-order intermodulation distortion, and filter interference is due to the interlaced response from adjacent pulses. Lastly, the current research progress on the mechanism of high repeti-tion pulse interference with navigation receivers is reviewed, providing reference for future study.

With the widespread application of Global Navigation Satellite Systems (GNSS), the security testing of critical infrastructure against GNSS spoofing attacks has become a crucial research topic. Among these attacks, asynchronous spoofing can seamlessly infiltrate receivers and take control of their loops. However, existing studies lack comprehensive analysis on the pullin rate threshold of such attacks. In this study, by analyzing asynchronous spoofing scenarios and their impact on loop mechanisms, we establish an analytical model that summarizes the pull-in rate threshold of spoofing in typical loop structures. Through software receiver simulations, the effect of spoofing interference on typical code loop structures is demonstrated, leading to the determination of the pull-in rate threshold for asynchronous spoofing signals. We systematically analyze the influence of code loop order, loop bandwidth, and damping factor on the spoofing speed limit. The results show that increasing loop bandwidth and power gain significantly enhance the spoofing speed limit, while the damping factor and loop order regulate the speed limit by affecting system stability and dynamic tracking performance. This study offers critical insights and tools for enhancing the resilience of critical infrastructures against future navigation spoofing attacks.

This paper analyzes the performance of sample matrix inversion (SMI) filter used by antenna array receivers in pulse interference environment. Firstly, from the perspective of comparison, it is proved that the theoretical optimal signal to interference noise ratio (SINR) of SMI filter under pulse interference is the same with that under continuous interferences. Then the convergence characteristics of SMI filter under pulse interference are deduced, and the relationship between the convergence speed and length of training samples and duty of interferences is given. Finally, the results of signal simulation are consistent with those of numerical analysis, which verifies the correctness of theoretical analysis. The results show that the convergence speed of SMI filter decreases under pulse interference, and the SMI filter needs more training samples to suppress the pulse interference effectively.

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions.