Power-inversion (PI) adaptive arrays are widely used in Global Navigation Satellite System (GNSS) receivers for interference mitigation. The effects of element patterns on the performance of PI adaptive arrays are investigated in this paper. To this end, the performance of adaptive arrays is investigated by Monte Carlo simulations, using CST Microwave Studio (Dassault Systems, Vélizy-Villacoublay, France) to calculate the radiation patterns of circular microstrip elements which are used to compute the adaptive weight and the adaptive array gain. It is shown that the performance of PI adaptive arrays is mainly dependent on the gain pattern of the reference antenna element rather than the non-reference elements because the algorithm essentially pushes the elements into an unequal position. Furthermore, the results show that the impact of mutual coupling on the performance of the antenna array can be associated with the radiation patterns of the reference element, which is helpful in selecting the optimum reference element without increasing computational complexity, especially for small GNSS arrays.

The binary offset carrier (BOC) modulation, which has been adopted in modern global navigation satellite systems (GNSS), provides a higher spectral compatibility with BPSK signals, and better tracking performance. However, the autocorrelation function (ACF) of BOC signals has multiple peaks. This feature complicates the acquisition process, since a smaller time searching step is required, which results in longer searching time or greater amounts of hardware resources. Another problem is the high Nyquist frequency, which leads to high computational complexity and power consumption. In this paper, to overcome these drawbacks, the band-pass sampling technique for multiple signals is introduced to BOC signals. The sampling frequency can be reduced significantly. Furthermore, the ACF of the sampled signal has only two secondary peaks, so that the code phase can be searched with a larger searching step. An acquisition structure base on dual-loop is proposed, to completely eliminate the ambiguity and compensate the subcarrier Doppler. The acquisition performance and the computational complexity are also analysed.

The Global Navigation Satellite System (GNSS) adaptive array suffers from phase distortions owing to beam forming processing which in turn affect the navigation precision. In high precision GNSS applications, the distortions must be evaluated. Traditional phase performance evaluation approach is based on the fixed reception pattern antenna. A new approach to evaluate phase performance of adaptive array is presented. The method takes into account the interfering scenes and the average phase centre variation performance for different adaptive algorithm and array geometry can be obtained.

The BeiDou-3 Satellite Navigation System (BDS-3) serves as a critical national space-based information infrastructure. However, the signals received at user terminals are characterized by power levels that lie significantly below the noise floor, rendering them highly vulnerable to both intentional suppressive jamming and unintentional interference in complex electromagnetic environments. Traditional anti-jamming techniques predominantly rely on blind suppression strategies. While effective against simple jamming scenarios, these methods suffer from inherent limitations, including low resource utilization and poor adaptability to complex, time-varying interference. To achieve precise suppression and real-time detection of interference, it is necessary to simultaneously perform classification and parameter estimation of the jamming signal. To this end, this paper proposes a multi-task dual-branch convolutional neural network (CNN) learning framework that encompasses four typical types of suppressive jamming: continuous wave interference (CWI), wideband noise interference (WNI), pulse interference (PI), and chirp interference (CI). Taking time-frequency spectrograms of signals as input, the framework achieves accurate identification of interference types and simultaneous estimation of key physical parameters. Simulation and experimental results demonstrate that the proposed method maintains stability and feasibility under low-INR conditions.

Global Navigation Satellite System (GNSS) spoofing is an effective method for suppressing the unmanned air vehicle (UAV) threat. However, spoofing the unauthorized UAV is a complex task, it is necessary to maintain the signal strength and carrier to noise ratio (CNR) in the rational range. In this paper, an improved signal strength and CNR controlling method based on ranging radar and (APL) is presented. the range between the spoofing transmitter and UAV is measured by ranging radar, and the gain difference between the spoofing and authentic signal are considered by the APL, the spoofing signal strength and CNR are adaptive adjusted based on the measuring value of ranging radar and searching results of the APL. A numerical example is presented to demonstrate the efficiency of the method. Compared with the previous methods, the proposed method not only improves the signal strength stability, but also improves the CNR stability, and the effective range based on the method can cover all areas under arbitrary incident angle of spoofing signal.