To study the dissemination stability and phase noise characteristics of the cascaded fiber-based RF dissemination, we perform an experiment using three sets of RF modulated frequency dissemination systems. The experimental results show that the total transfer stability of the cascaded system can be given by ${\mathit{\sigma }}_T^2=\sum _{i=1}^N\underset{i}{\overset{2}{\mathit{\sigma }}}$ (σ_i is the frequency dissemination stability of the ith segment and N is the quantity of segments). Furthermore, for each segment, the phase noise of recovered frequency signal is also measured. The results show that for an N-segment, cascaded dissemination system, its stability degrades only by a factor of $\sqrt{\mathit{N}}$ . This sub-linear relation makes the cascaded, RF-dissemination method a very attractive one for long-haul, time and frequency dissemination network.

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

Frequency dissemination and synchronization in free space play an important role in global navigation satellite system, radio astronomy, and synthetic aperture radar. In this paper, we demonstrated a portable radio frequency dissemination scheme via free space using microwave antennas. The setup has a good environment adaptability and high dissemination stability. The frequency signal was disseminated at different distances ranging from 10 to 640 m with a fixed 10 Hz locking bandwidth, and the scaling law of dissemination stability on distance and averaging time was discussed. The preliminary extrapolation shows that the dissemination stability may reach 1 × 10⁻¹²/s in ground-to-satellite synchronization, which far exceeds all present methods, and is worthy for further study.

Accurately predicting satellite clock deviation is crucial for improving real-time location accuracy in a GPS navigation system. Therefore, to ensure high levels of real-time positioning accuracy, it is essential to address the challenge of enhancing satellite clock deviation prediction when high-precision clock data is unavailable. Given the high frequency, sensitivity, and variability of space-borne GPS satellite atomic clocks, it is important to consider the periodic variations of satellite clock bias (SCB) in addition to the inherent properties of GPS satellite clocks such as frequency deviation, frequency drift, and frequency drift rate to improve SCB prediction accuracy and gain a better understanding of its characteristics. In recent applications, deep learning models have significantly improved handling time-series data. This paper presents four machine learning prediction models that take into consideration periodic variations. Specifically, we utilize precision satellite clock bias data from the International GNSS Service forecast experiments and assess the predictive effects of various models including backpropagation neural network (BPNN), wavelet neural network (WNN), long short-term memory (LSTM), and gated recurrent units (GRUs). The predicted sequences of the four machine learning models are compared with the quadratic polynomial(QP) model. The average prediction accuracy of forecasting has improved by approximately (39.45, 57.57, 27.28, 29.14)% during 1-day forecasting. The results indicate that the machine learning models incorporating periodic variations outperform the standard quadratic polynomial model in terms of predictive accuracy, and the WNN model is better than that of these three machine learning models. This highlights the promising potential of deep learning models in forecasting satellite clock bias.

In this paper we present a magnetic probe structure based on magneto-fluidic and laser internal cavity modulation. We used a single-mode-no-core-single-mode fiber structure coated by a magnetic fluid as a sensing element, and inserted the sensing element in the inner cavity of a laser. Highly sensitive, high signal-to-noise ratio (SNR), narrow half-height width (FWHM) sensing signals have been obtained using intra-laser cavity modulation. The magnetic detection sensitivity of the sensor was improved, and a sensing system with a magnetic detection sensitivity of 1.76 × 10⁻³ mw/Oe and a linearity of 99.709% was achieved. The temperature sensitivity is 0.035 μW/°C. The limit error of the magnetic field sensor is ±0.006035%. Meanwhile, output signal index of the sensing system is FWHM<40pm.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.