The current wireless SAW (Surface Acoustic Wave) sensor based on langasite (LGS) can work at temperatures as high as 600 °C. Nevertheless, the propagation loss of LGS increases significantly as frequency and temperature increase, which limits the operating frequency of the SAW sensor based on LGS to 1 GHz. However, SAW resonator based on AlN/sapphire structure exhibits gigantic potential for high-temperature sensing applications due to its resistance to high temperatures, high Q-factor, and low propagation loss. In this work, an efficient model for SAW resonator based on AlN/sapphire is developed using the coupled mode (COM) theory combined with the finite element method (FEM). The influence of different numbers of interdigital transducers(IDT), reflective gratings, and different aperture lengths on device performance are investigated. Furthermore, the relationship between resonant frequency and temperature at various temperatures is simulated, which compares well with that of the experimental results. The investigation results show that the resonator works reliably in the temperature range of up to 500 °C and the operating frequency of up to 2.45 GHz. The frequency-temperature characteristics exhibit good linearity, with a temperature coefficient of-67×10^-6 °C^-1. This work provides an important reference for designing high-performance SAW high-temperature sensors.

Proper total pressure, temperature and humidity environment in the cabin are the basic conditions for the stable operation of the space station platform and the safety of astronauts. For the long-term and reliable measurement of the total pressure, temperature and humidity environment in the cabin, an intelligent integrated monitoring technology and instrument are introduced, including the overall design, sensor design, integration challenges and calibration scheme. From the verification of on-orbit data, it is concluded that the instrument can achieve high-precision and high-reliability measurement of the total pressure, temperature and humidity in the cabin. It not only improves the level of environmental measurement in China's space station, but also provides new technical support for environmental detection of the manned space program in the future.

Aiming at the temperature measurement requirements of fiber grating sensors, this paper proposes and implements a Zynq-based Fiber Bragg Grating (FBG) temperature demodulation system. The system adopts the Zynq architecture, and the acquisition of FBG spectra and the calculation of the center wavelength are realized in the hardware system. Through the analysis and optimization of Gaussian fitting peak-seeking algorithm, the matrix operation is used to reduce the amount of curve-fitting operations, and the FBG center wavelength solving is realized in the hardware system, which takes into account the demodulation accuracy and at the same time, improves the demodulation rate and real-time performance of the system. The miniaturized fiber grating temperature demodulation system developed has a wavelength demodulation range of 1 510 nm ~ 1 590 nm with a stability of ±2 pm, which has a good potential application value in structural monitoring and other fields.

Fiber Optic Gyroscope (FOG) is the core component of the fiber optic strapdown inertial navigation system, which has been widely used in aviation, aerospace, navigation and other fields. The scale factor is the main factor affecting the dynamic performance of FOG. Because the photoelectric devices inside FOG are highly sensitive to temperature vaviations, the scale factor error will be produced under the influence of temperature, which will affect the precision of FOG. In the variable temperature environment, each photoelectric device is heated unevenly, which leads to the hysteresis of the scale factor error. In this paper, the scale factor hysteresis error of FOG is studied, and a multi-temperature sensor measuring system is built. The source and characteristics of the scale factor hysteresis error are determined by the experimental results. Based on the above analysis, an error compensation algorithm based on gravitational search algorithm (GSA) and long short-term memory (LSTM) network is proposed. The parameters of LSTM network are optimized by GSA, and the LSTM model is used to compensate the scale factor hysteresis error. The experimental results show that the peak-to-peak value of scale factor error in the whole temperature range is reduced from 835.1×10^-6 to 38.02×10^-6 by the proposed algorithm. By comparing with the compensation results of multilayer perceptron (MLP) and traditional LSTM algorithm, the effectiveness of the proposed algorithm in scale factor hysteresis error compensation is further verified.

It is of great significance to obtain accurate heat flux data for understanding the thermal environment and optimizing the thermal protection system of the flight vehicles. The important aspects of heat flux measurement technology development are heat flux calibration and heat flux data processing. The heat flux sensor based on the hybrid heat transfer principle needs to utilize heat flux terms from different sources, such as the heat storage term and the temperature difference term to achieve heat flux results during data processing. The error sources and the magnitudes of influence of these terms are different, resulting in limitations of the common single-coefficient calibration method. Especially when the testing time is longer, some error factors will cause changes in heat flux measurement error. This paper proposes a multi-coefficient calibration method that corrects each heat flux term using different coefficients separately. Based on the temperature data in the calibration test and the known input heat flux, the calibration coefficient matrix is solved using the least squares method. The analysis and processing results of the calibration testing data show that using the multi-coefficient calibration method, the heat flux results are in better agreement with the input heat flux than those using the single-coefficient method. With enough testing data, the calibration deviation is less than 5% within the calibration time.

In response to the demand for in-situ continuous measurement of molten steel temperature in the field of metallurgy, a novel contact-type continuous temperature measurement sensor structure scheme is proposed. The thermal response characteristics are analyzed by establishing a three-dimensional heat transfer model. Then a high-pressure process and a high-temperature annealing process are used to realize the preparation of an integrated sensitive component of thermocouple/ceramic protection body, which is then encapsulated inside the lance-type support structure of MgO-C material for making a temperature measurement sensor; Continuous temperature measurement tests are carried out under the environment of molten steel of Ladle Furnace, and the results show that in the 1 600 ℃~1 650 ℃ high temperature molten steel environment, the sensor can effectively measure temperature continuously for more than 16 minutes.

With the rapid development of 5G communication and millimeter-wave technology, the performance requirements for high-frequency passive interconnect structures in radio frequency (RF) microsystems have become increasingly critical. To address the efficiency bottlenecks caused by fragmented process data and isolated models in traditional design workflows, this paper proposed an independently developed 1~40 GHz silicon-based passive interconnect Process Design Kit (PDK). By integrating equivalent circuit models with HFSS full-wave electromagnetic simulation data, parameterized models for core structures such as grounded coplanar waveguide (GCPW) and micro-bump interconnects are established, and high-precision model matching is achieved through gradient optimization algorithms. The PDK development is completed on the Keysight ADS platform, including symbol libraries, parameterized cells, design rules, and verification workflows. Experimental results demonstrate that the developed PDK achieves a root mean square error (RMSE) of S-parameters below 10% across the 1~40 GHz frequency band. Based on this PDK, the simulation design of an X-band RF micro system was completed. The microsystem meets the specified performance requirements, verities the validity of the PDK. This PDK provides for reliable support for efficient design-process co-optimization in high-frequency RF systems.

In recent years, low-orbit navigation enhancement system has been gradually incorporated into the construction of integrated PNT system, and the capture and tracking of low-orbit signals has gradually become a hot research issue. In the scenario that the integrated PNT application terminal needs to receive BeiDou + LEO navigation augmentation signals at the same time, due to the large resource consumption of the capture module, using two sets of hardware resources to compatibly receive high, medium and low orbit-signals will cause a great waste of resources, and it is not possible to realize the small size and low power consumption requirements of the user terminal. This paper further optimizes the algorithm on the basis of the existing BeiDou signal capture module, and through the design of flexible and reusable matched filter plus FFT structure, on-chip distributed processing and off-chip ultra-long storage architecture, and techniques such as high Doppler pseudo-code, carrier frequency compensation, etc., it realizes the compatible reception of high, medium and low-orbit signals, and at the same time, it can realize the capture of high and medium orbit BeiDou B1C signals with a capture sensitivity of -145 dBm capture sensitivity and 40 kHz STL burst signal in low orbit.

This study explores the combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to address the challenges in multi-parameter radar signal sorting. As the complexity of radar signals has been increasing, traditional signal processing methods have revealed limitations. t-SNE effectively extracts essential features from the data by reducing dimensionality, eliminating noise and redundant information, and providing a clearer boundary for subsequent DBSCAN clustering. In the experiment, we generated five different types of radar signal data and conducted analyses using t-SNE and DBSCAN. The results show that the t-SNE dimensionality reduction combined with the DBSCAN clustering algorithm performs well in terms of purity and silhouette score, confirming the effectiveness of this method in complex radar signal sorting.

Spaceborne synthetic aperture radar is one of the important means of detecting ocean internal waves, and sea surface wind speed has a significant impact on the ability of synthetic aperture radar to detect ocean internal waves. Based on the theory of the influence of sea surface wind speed on the ability of synthetic aperture radar to detect ocean internal waves, combining the insitu measured parameters of ocean internal waves, additional with the corresponding ocean environment and synthetic aperture radar data, this paper analyzes the imaging mechanism of ocean internal waves on synthetic aperture radar images, discusses the different manifestations of upward and downward ocean internal waves, and elaborates on the ability of synthetic aperture radar to detect ocean internal waves under the combined influence of internal wave amplitude, thermocline depth, and thermocline intensity under different wind speed conditions. Simulation analysis shows that the smaller the sea surface wind speed, the larger the internal wave amplitude, the shallower the thermocline depth, and the stronger the thermocline intensity, the stronger the ability of synthetic aperture radar to detect internal waves in the ocean. The results can provide technical support for the planning of satellite observation tasks in the early stage of detecting ocean internal waves using spaceborne synthetic aperture radar.