This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).

The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction.

With the increasing demand for high-sensitivity signal tracking in complex environments, GNSS receiver technologies have continuously evolved in both architecture and algorithmic design. This paper presents a systematic review of the development of high-sensitivity tracking techniques, with a focus on the transition from closed-loop to open-loop, hybrid, and deeply integrated architectures. Key strategies—such as coherent integration time extension, discriminator and loop filter optimization, vector tracking (VT), and Direct Position Estimation (DPE) are evaluated in the context of weak signal scenarios. To address the limitations of existing methods, including strong dependence on filter models, oscillator instability, and computational complexity, this study also outlines future research directions, including (1) integrating deep learning techniques into filter structures to enhance adaptability and robustness; (2) developing multi-channel collaborative estimation schemes to mitigate oscillator noise in weak signal environments; and (3) designing low-complexity open-loop tracking approximations to align with hardware resource constraints. Thus, enhancing the necessity of navigation continuity and robustness under low-signal-to-noise-ratio (SNR) conditions.

The recent rapid development of low Earth orbit (LEO) constellation-based navigation techniques has enhanced the ability of position, navigation, and timing (PNT) services in deep attenuation and interference environments. However, existing navigation modulations face the challenges of high acquisition complexity and do not improve measurement precision at the same signal strength. We propose a pulsed orthogonal time frequency space (Pulse-OTFS) signal, which naturally converts continuous signals into pulses through a special delay-Doppler domain pseudorandom noise (PRN) code sequence arrangement. The performance evaluation indicates that the proposed signal reduces at least 89.4% of the acquisition complexity. The delay measurement accuracy is about 8 dB better than that of the traditional binary phase shift keying (BPSK) signals with the same bandwidth. It also provides superior compatibility and anti-multipath performance. The advantages of fast acquisition and high-precision measurement are verified by processing the real signal in the developed software receiver. As Pulse-OTFS occupies only one time slot of a signal period, it can be easily integrated with OTFS-modulated communication signals and used as a navigation signal from broadband LEO satellites as an effective complement to the global navigation satellite system (GNSS).

Recently, there has been an increasing interest in modelling and simulation of time‐varying (TV) non‐line‐of‐sight (NLOS) channels for global navigation satellite systems (GNSS) as an assisted means of resolving the GNSS urban canyon problem. In the above context, modelling and simulation of TV NLOS channels for GNSS have been investigated and studied due to their rapidly TV characteristics. However, conventional simulation approaches for TV multipath channels suffer from inconsistency and lack physical soundness, resulting in a non‐smooth transition of the Doppler shift and low simulation output accuracy. The authors propose an improved satellite navigation channel modelling and simulation method for TV NLOS reception scenarios. The multiple‐point‐scatterer model framework is applied to the TV NLOS satellite navigation channel model. The major novelty of the proposed method is integrating the multiple‐point‐scatterer model with online planning of a kinematics trajectory to map changes in the continuous velocity to the Doppler shift, which can keep consistency and accuracy. As a side benefit of the authors' work, numerical integration based on the efficient composite trapezoidal rule is proposed to calculate the precise changes in signal path length, phase, and Doppler shift. The numerical simulation results show that the proposed modelling and simulation method can not only accurately simulate the TV characteristics of the channel multipath with the continuous phase and the Doppler shift, but also reproduce the Rayleigh fading statistics of the TV channel, thus effectively approximating the physical propagation model and generating the TV NLOS satellite navigation channel.