Precision-guided munitions(PGMs) have become indispensable components in modern military systems due to their advantages of high strike accuracy, strong penetration capability, and extended operational range. Micro electro mechanical systems(MEMS)-based inertial guidance systems, primarily composed of MEMS accelerometers and MEMS gyroscopes, play a critical role in artillery projectile guidance. Consequently, ensuring the reliable operation of MEMS inertial devices under high-overload environments has emerged as a key research focus for academic institutions and research organizations. In this paper, the requirements and technical challenges of MEMS inertial devices in high-overload environments are systematically outlined, with emphasis on analyzing critical innovations in anti-high-overload technologies and recent advancements in high-overload testing equipment. By summarizing state-of-the-art research domestically and internationally, this paper proposes future development directions for anti-high-overload MEMS inertial devices and test equipment, providing theoretical references and practical guidance to advance this field.

Autonomous navigation technology serves as an indispensable core capability for critical platforms such as unmanned systems, with its strategic value and application prospects becoming increasingly prominent. However, in GNSS-denied environments like urban canyons, indoor spaces, and underwater settings, traditional navigation methods reliant on GNSS are susceptible to interference, leading to severe accuracy degradation or positioning failure. Recent rapid advancements in deep learning technology have provided novel approaches for constructing high-precision, highly robust, and fully autonomous navigation systems. Focusing on deep learning-assisted autonomous navigation technology under GNSS-denied conditions, an in-depth review and analysis of research progress in three key areas is conducted: deep learning-assisted inertial navigation technology, multi-source intelligent navigation technology under GNSS-denied environments, and deep learning-enhanced filtering and fusion techniques. Finally, the future development trends of deep learning-assisted autonomous navigation technology are outlined.

The presence of various end-axis disturbance torques in inertial stabilization platforms limits the further improvement of the dynamic control accuracy of traditional controllers. To suppress the impact of torques on the stabilization loop, a disturbance observer is introduced into the control loop, and the dynamic performance verification is completed in a three-axis fiber-optic gyro inertial platform. Tests on a three-axis FOG platform prototype show that the disturbance observer significantly improves stabilization accuracy under dynamic conditions. The maximum misalignment angle of the three axes of the platform does not exceed 3″ under sway test conditions 6°, 1.0 Hz; 0.95°, 2.5 Hz; 0.35°, 4.0 Hz. Frequency sweep comparison tests indicate that the disturbance observer can enhance loop gain in the low-frequency band while maintaining stability margins, thereby increasing loop torque stiffness and reducing dynamic errors.

With the increase of gyroscope quality factor, the bandwidth performance of MEMS gyroscope restricts its development in high-precision and high-dynamic fields. This paper focuses on the ultra-low bandwidth issue of ultra-high Q MEMS gyro. Firstly, modeling of the MEMS gyroscope is done. Then, a bandwidth extension algorithm is designed based on the traditional force-rebalance control loop, and the noise model of the system after bandwidth extension is established. The main noise source is analyzed and optimized. Test results show that the designed algorithm and optimization are suitable for ultra-high Q MEMS gyroscope with a Q-factor of 416k. Compared with the traditional PI control scheme, the method can increase the bandwidth from 1.5 Hz to 40 Hz while maintaining the same level of bias instability, and the bias instability is 0.4832 (°)/h. This method achieves bandwidth expansion and resolves the mutual restriction between bandwidth and noise performance.

In order to improve the engineering application accuracy of the navigation-grade MEMS resonant beam accelerometer based on the glass-silicon micromachining process, this paper introduces the technical solutions for suppressing the temperature drift, nonlinearity, and vibration and shock-induced errors of the accelerometer, as well as the performance test results of the developed prototypes. The temperature drift suppression methods composing of low-temperature drift structural design, low thermal-stress packaging process, experimental modeling and compensation are described firstly. The experimental results show that the mean stabilities of the bias and scale factor are 4.3 μg and 1.4 ppm over the temperature range from -40 ℃ to 60 ℃, respectively. A refinement of the acceleration measurement with squared difference of two resonant frequencies is then introduced with a reduced scale factor nonlinearity of 25.2 ppm within the measurement range of ±20 g. Finally, an effective suppression of the first-order mode interference of the sensing structure is accomplished using active damping design, which remarkably enhances the anti-vibration and shock performance of the prototypes experimentally.

The MEMS accelerometer is an inertial sensor based on silicon micromachining technology, which is used to measure acceleration information. The temperature characteristics of its interface circuit determine the performance of the entire sensor. In this paper, three temperature characteristic optimization schemes for the zero output drift and hysteresis characteristics of the MEMS accelerometer interface ASIC chip under full temperature conditions are proposed. Firstly, an array capacitor compensation scheme is proposed to solve the temperature characteristic problems caused by operational amplifier offset and capacitor mismatch leading to zero offset. Secondly, a low-temperature-drift bandgap reference source is designed to provide carrier level and common mode level. Finally, a third-order fitting digital temperature compensation scheme is designed to further improve the output accuracy. The chip is tested, and the final measured results show that within the temperature range of -45℃ to 85℃, the peak-to-peak drift of the three axes analog output for the MEMS accelerometer is 8 mg, 12 mg, and 11 mg respectively, which can be reduced to within 2.8 mg after compensation, and the temperature hysteresis error is within 2.5 mg. The peak-to-peak drift of the three axes digital output is 50 mg, 22 mg, and 18 mg respectively, which can be reduced to within 6 mg after compensation, and the temperature hysteresis error is within 0.5 mg. This paper provides technical support and theoretical basis for the design of low-temperature-drift accelerometers.

Accelerometer is a key device for inertial navigation, vibration monitoring in aerospace and national defense equipment. It may face high-impact overload during its working process, resulting in functional failure. In order to meet the requirements for the impact overload resistance of MEMS accelerometers in high-impact overload scenarios, a high overload resistance capacitive accelerometer is designed in this paper, which adopts the “rigid + flexible” overload resistance structure limit scheme, with the characteristics of high rigidity, strong reliability of rigid limit structure, and flexible nonlinear limit structure is not easy to collapse, can absorb the impact energy. The structural damping and modal separation ratio are analyzed, and the key dimensions are comprehensively optimized to further improve the out-of-plane impact resistance and detection sensitivity. The performance calibration and drop hammer experiment show that the encapsulated sensitive chip has a measurement range of ±20 g, a resolution of 0.5 mg, a sensitivity amplitude linearity of 0.067%, and can resist the in-plane and out-of-plane impact acceleration of 3500 g, which is a good combination of performance and impact resistance characteristics.

Quadrature coupling is a key factor affecting the output of gyroscopes. During the startup of dual closed-loop MEMS gyroscopes, loop control parameters significantly impact the output stabilization time. Experiments show that increasing the quadrature loop integration parameter KIQ and aligning the detection demodulation phase with the actual phase of the sense axis enables the gyroscope to achieve zero bias stabilization within 30 ms after startup. However, under high and low temperatures, variations in the sensing modal quality(Q) factor cause the detection demodulation phase to deviate from the optimal value, prolonging the startup stabilization time. To tackle this, a control method combining a fixed quadrature stiffness correction configuration with a real-time closed-loop is proposed. This method configures a fixed quadrature correction bias to counteract inherent quadrature coupling, reducing the quadrature coupling deviation at the initial startup moment and significantly shortening the closed-loop stabilization time. As a result, the gyroscope can startup rapidly and reach a stable output state within 30 ms, even when the KIQ parameter is relatively small or the detection demodulation phase error is large, over a temperature range of -45℃ to 85℃, with markedly improved startup characteristics.

Autonomous navigation is a core capability for evaluating the robot’s level of intelligence. Traditional navigation frameworks heavily rely on continuous and precise positioning information, which often leads to system collapse in perception-degraded environments such as long corridors due to localization failure. Meanwhile, a single planning strategy is insufficient to balance efficiency and safety across diverse environments. To address these challenges, an adaptive navigation framework based on point cloud scene understanding and topological planning is proposed. A navigation strategy switching method based on SPVCNN scene understanding is developed, which effectively recognizes spatial structures such as open areas, narrow corridors, and rooms, designing an adaptive switching approach for scene-feature-oriented navigation strategies. An improved Zhang-Suen skeleton extraction method is introduced, combined with a skeleton-based pruning strategy to remove redundant nodes and branches, thereby enhancing the ability of the topological map to represent environmental spatial layouts. Furthermore, a heuristic A^* algorithm is designed, leveraging the extracted skeleton topology to generate path guidance aligned with corridor structures, improving the robot’s stability and safety margin in confined spaces. Experimental results show that, in narrow environments, the proposed method reduces navigation time by an average of 13.1% and improves average path smoothness by 34.6% compared to mainstream local path planning methods, while maintaining stable and safe operation even under localization failure.

Due to the limitation of materials and manufacturing process, MEMS gyroscope is susceptible to temperature, and the resulting temperature drift severely limits the measurement accuracy and further application of MEMS gyroscope. In this paper, a temperature compensation method for MEMS gyroscope based on signal decomposition and neural network is proposed. In this method, random noise is filtered by signal denoising based on interpolated complementary ensemble local mean decomposition with adaptive noise, and then a dynamic neural network model of gated recurrent unit is established to compensate for temperature drift, which can effectively reduce noise and improve the learning accuracy of temperature drift model. The verification experiment results show that, in the temperature range of -40℃ to 70℃, the proposed method reduces the bias instability of the MEMS gyroscope from 1.0406 (°)/h to 0.1228 (°)/h, and the angle random walk from 4.8309 (°)/h^1/2 to 0.1587 (°)/h^1/2, which improves the temperature performance of the MEMS gyroscope effectively.

This article reviews the current research status and development trends of multi-source information fusion and control technologies for bionic aircraft. Firstly, it provides an in-depth overview of the navigation and control mechanisms of insects and birds, elucidating how they integrate multiple sources of information, such as vision, olfaction, and geomagnetism, to achieve effective navigation. The article further analyzes their unique flight perception systems and advanced information fusion feedback control mechanisms. Subsequently, the article examines the state-of-the-art research on multi-source information fusion navigation and control technologies for bionic aircraft, encompassing multi-source information navigation technologies, dynamic modeling of bionic aircraft, motion control and navigation positioning technologies for bionic aircraft, as well as bio-inspired distributed perception-based flight control technologies. Finally, the article outlines potential future directions for the development of navigation and control technologies in bionic aircraft.

As the most common rate measurement mode in micro electro mechanical system(MEMS) gyroscope applications, the nonlinearity error of scale factor significantly restricts the widening of gyroscope application scenarios. Aiming at the conventional FTR excitation method of constant DC with variable AC, which is limited by the mutual constraints of signal update rate and noise, the FTR excitation method of constant AC with variable DC is proposed. Firstly, the FTR rate measurement control loop model based on the constant DC with variable AC excitation method is constructed to analyze the phase relationship between the excitation and pickoff signals of the two operating modes for the FTR rate gyroscope, and then a 90° phase-shift circuit is designed to convert the stable amplitude signals at the pickoff end of the drive mode into the FTR excitation signals. Finally, the performance comparison of the two excitation methods is carried out. The experiment results show that compared with the traditional constant DC with variable AC excitation method, the constant AC with variable DC excitation method reduces the scale factor nonlinearity error by 82.84% and the asymmetry error by 93.93%, and the change in bias instability and angular random walking for both excitation methods is 7.32% and 17.57%, respectively.

With the continuous expansion of international trade and maritime transportation, ship trajectory prediction based on the AIS faces increasingly stringent demands for accuracy and robustness as a core technology for smart shipping and maritime supervision. To address common observational disturbances and insufficient predictive performance in complex navigation environments, a ship trajectory prediction framework combining high-precision forecasting capabilities with strong noise robustness is developed. Specifically, a novel prediction model, GRU-MSAformer, is proposed, integrating GRU and multi-scale causal self-attention mechanisms. The model first captures local temporal dependency features via GRU, then employs a multi-scale self-attention mechanism to model trajectory behavior across different time scales, thereby enabling adaptive noise filtering. Experimental results demonstrate that GRU-MSAformer achieves superior performance under both noise-free and Gaussian noise conditions. It maintains low prediction errors across 10 to 40 minutes forecasting tasks while sustaining stable prediction accuracy under varying noise intensities.

Inertial measurement units play a crucial role in autonomous navigation and positioning, but their measurement errors increase exponentially over time. A pedestrian inertial navigation method based on time-frequency feature encoding neural networks is proposed. The time series sequence of inertial data and the frequency domain sequence obtained through Haar transformation are respectively used as imputs to the neural network. The time-domain and frequency-domain features are extracted separately by the inertial time-frequency feature encoder, and the dependencies between different time steps and frequency components are adaptively fused and learned through the multi-head attention mechanism. Then, the prediction results of the neural network are integrated with the inertial motion model through the extended Kalman filter framework to further optimize the state estimation. Experimental results on the public datasets TLIO and RoNIN show that, compared with the benchmark method TLIO, the proposed method reduces the ATE, RTE, and DR by 10.8%, 17.7%, and 12.9% respectively, demonstrating high accuracy and robustness in complex pedestrian motion scenarios.

With the deep expansion of informatization into multi-dimensional physical space, dominance over spatiotemporal information has become a core area of strategic competition among major countries around the world. In this paper, addressing the development needs of the national comprehensive positioning, navigation, and timing(PNT) system, the connotation and technological evolution of dominance competition systems are explored, the cross-domain collaborative development from command of the sea, air, space to electromagnetic and information dominance are reviewed, and the technological evolution path of navigation dominance in adversarial environments is specifically examined. By summarizing the strategies and development trends of the United States, Russia, and other countries in constructing technological systems for navigation countermeasures, it conducts a critical analysis of the vulnerabilities existing in current satellite navigation systems at both the service and application levels. Furthermore, it finds out the breakthrough technological directions such as space-based resilient PNT and intelligent multi-source autonomous navigation. Finally, the future trends of navigation dominance technology from dimensions including system confrontation and the cognitive domain is prospected, aiming to provide technological support for the development of new-generation comprehensive PNT system.

Quartz resonant accelerometers are characterized by high stability and low power consumption, and have become a hot topic of research in the field of inertial measurement. This study addresses critical performance limitations in scale factor and stability through systematic modeling and theoretical analysis, establishing a structural framework for resonant accelerometers and proposing an enhanced differential dual-opposed-pendulum configuration. Utilizing multiphysics-coupled finite element analysis for global parameter optimization, optimal chip dimensions are determined, wet-etching and precision manufacturing are implemented to develop a metal-integrated prototype. Experiment data shows that the prototype has a measurement range of ±3 g, dimensions of Φ25 mm×15 mm, a scale factor of 348.33 Hz/g, zero bias stability of 57.78 μg, and scale factor stability of 8.42 ppm. Test results indicate that this miniaturized device combines high scale factor, small measurement range, and high stability, offering a new inertial measurement solution for precision aerospace engineering applications such as deep space exploration.

Coded aperture snapshot spectral imaging (CASSI) technology enables efficient synergistic acquisition of spatial and spectral information through single-shot compressive imaging. It overcomes the limitations of traditional spectral imaging techniques, which rely on scanning mechanisms and incur high costs on data storage and transmission. This paper systematically reviews the research progress in CASSI technology, focusing on its hardware architecture, theoretical models, and reconstruction algorithms. The hardware design section explores the iterative optimization of system architecture and the impact of coded aperture design on imaging performance. The theoretical model section analyzes the physical modeling methods of single dispersion CASSI and summarizes optimization paths for the theoretical models, highlighting the importance of optical error correction in improving reconstruction accuracy. The reconstruction algorithms section talks about the performance bottlenecks of traditional algorithms introducing recent breakthroughs in deep-learning based reconstruction methods. While deep learning has demonstrated significant advantages in complex scene reconstruction and computational efficiency, challenges related to interpretability, data dependency, and hardware compatibility remain to be addressed. Finally, the paper discusses the future development trends of CASSI technology across multiple dimensions, including system architecture, hardware innovations, algorithm frameworks, and embedded terminal development, aiming to promote its widespread applications in fields such as aerospace remote sensing, biomedicine, deep space exploration, and real-time navigation.

Incremental angle sensors are typically used to measure the outer frame angular velocity of gyro accelerometers, making it difficult to obtain accurate outer frame position information and hindering error modeling research for gyro accelerometers. To address this, an output model of gyro accelerometer is established at small tilt angles under the gravity field. Two methods for identifying the outer frame position are proposed: one based on inner frame angle β drift and another based on characteristic points. Both methods can obtain accurate real-time outer frame position within a short power-on period and control the outer frame to halt at the target position at power-off. Test results demonstrate that the position identification and control accuracy of both methods is within ±4°. In terms of accuracy, the β-angle drift-based method outperforms the characteristic-point-based method; in terms of rapidity, the characteristic-point-based method significantly surpasses the β-angle drift-based method. The characteristic-point-based outer frame position identification method is more suitable for inertial navigation systems, meeting speed requirements while being simpler to implement. Both methods lay the foundation for error modeling and compensation of gyro accelerometers.

The readout circuit, serving as the front-end module of a sensor system, is a critical component that determines the overall performance of the system. To meet the low-noise readout requirements of MEMS capacitive gyroscopes, an integrated readout circuit incorporating a drive loop and a detection circuit was designed in 180 nm CMOS process. Based on the actual structure of the gyroscope, a gyroscope sensor model for co-simulation with CMOS readout circuits is constructed using Verilog-A. Leveraging chopper technology, a low-noise capacitive readout circuit is designed, achieving adjustable gain while reducing low-frequency noise. An integrated design approach is applied to the demodulation and filtering modules of the detection circuit, achieving a streamlined architecture. Simulation results demonstrate that at a chopping frequency of 250 kHz, the output noise of the readout circuit is 50.5 μV, with a minimum detectable capacitance of 20 aF and a dynamic range of 94 dB.

Traditional navigation technologies face challenges in dynamic and uncertain environments, including dependence on external signals, high energy consumption, and bulky hardware. Bio-inspired navigation technology, by mimicking biological perception and information fusion mechanisms, offers innovative solutions for efficient and robust navigation in complex environments. This paper systematically reviews the research progress in typical bio-inspired navigation sensors, categorizing them based on biological navigation modalities, with particular focus on the principles of biomimetic design, technological breakthroughs, and application potential across various sensor types. The study further summarizes current technical bottlenecks and proposes that future development should integrate brain-inspired computing with deep learning to advance the autonomous development of “perception-decision-action” full-chain systems. This work provides theoretical references and technical pathways for the engineering applications of bio-inspired navigation sensors and interdisciplinary innovation.

The rapid development of UAV technology is gradually transforming existing operational models and giving rise to new warfare styles. In this paper, the important role played by UAV in many military conflicts and the potential security threats to countries and regions are introduced, and the difficulties and challenges faced by space-based anti-UAV combat are analyzed, the urgency and necessity of developing space-based anti-UAV systems and researching key space-based anti-UAV technologies are clarified. Subsequently, the typical types of space-based anti-UAV combat are summarized, and the key space-based anti-UAV technologies are analyzed in depth from four aspects including anti-UAV technology based on UAV, anti-UAV cluster technology based on UAV cluster, intelligent game technology, and large model technology. Finally, the research direction and development trend of future key space-based anti-UAV technology are briefly summarized from the perspective of system operation, with a view to promoting the development of future anti-UAV technology.

Resonant accelerometer is a new type of acceleration sensor, which has the advantages of small size, low power consumption and high accuracy. In this paper, the closed-loop self-excited driving technology of resonant accelerometer is studied, and the establishment and simulation of the electrical model for the sensitive structure is completed by analyzing the working principle of resonant accelerometer. On the basis of completing the design for the front-stage transimpedance detection unit and automatic gain control unit including low-noise operational amplifier and nonlinear multiplier, the closed-loop self-excited driving circuit is constructed and verified by simulation. The simulation results show that the closed-loop self-excited driving circuit completes the establishment of the driving signal within 100 ms, and the PI controller output has an average value of 3.076 V and a fluctuation amplitude of 209.2 μV. This technology provides support for the integrated design of resonant accelerometer driving circuit.

In complex emergency rescue scenarios such as semi-obstructed industrial sites, personnel positioning is easily affected by obstructions and signal attenuation, which makes traditional positioning solutions relying on fixed base stations difficult to be rapidly adapted to sudden rescue needs. While inertial navigation systems(INS) can provide autonomous positioning, they are afflicted with shortcomings such as cumulative errors over time and insufficient three-dimensional positioning accuracy. To address the issues of insufficient collaborative positioning accuracy and reliance on pre-existing infrastructure in emergency rescue operations within semi-obstructed industrial sites, a multi-person collaborative positioning technology combined with “ZigBee+INS” is focused on this study. The aim is to overcome the limitations of traditional positioning methods, as rapid deployment and high-precision positioning are enabled without the need for pre-established base stations, thereby rescue efficiency is improved and personnel safety is enhanced. Firstly, ZigBee anchors are deployed on the rescue personnel’s end, and the cross-power spectrum phase method is used to estimate the time delay of the time difference of arrival(TDOA). Secondly, by combining the location information of the rescue personnel and the commander, an improved TDOA algorithm is employed to suppress positioning errors. Thirdly, based on the results of the improved TDOA, the Taylor algorithm is used to determine the initial positions of the rescue personnel. Finally, position and heading constraints are established using ZigBee, barometers, and magnetometers, and multi-source information fusion and real-time position updates are achieved through an extended Kalman filter(EKF). Experimental results show that, compared with inertial navigation and classical collaborative positioning algorithms, the root mean square error and the absolute mean positioning error of the proposed algorithm are reduced by 55.42% and 62.36%, respectively. This algorithm achieves anchor-free and rapidly deployable multi-person collaborative positioning in semi-obstructed environments, and technical support is provided for precise command and control in emergency rescue operations.

Axis misalignment, scale factor deviations, and time-varying noise present in low-cost IMUs significantly degrade attitude estimation accuracy. Existing neural network-based denoising methods exhibit clear limitations in multidimensional error modeling. To address this, a gyroscope adaptive calibration network integrating temporal and channel attention mechanisms is proposed: a convolutional neural network performs feature extraction, channel attention optimizes multi-axis feature weighting, and the temporal attention balances feature contributions over time, thereby enhancing network accuracy and robustness. Experimental results on the EuRoC dataset demonstrate that channel attention substantially improves dynamic compensation accuracy, while temporal attention balances accuracy across the three axes without overall performance gains, combining both mechanisms yields certain improvements, their interaction may hinder optimal performance in certain scenarios. These results validate the effectiveness of multi-attention mechanisms in inertial sensor errors modeling and provide new insights for designing low-cost gyroscope dynamic compensation algorithms.

Micro-gyroscopes, as core sensors for angular velocity measurement, are critical for intelligent navigation and precision guidance. To address the dual problems of noise suppression improvements in traditional MEMS gyroscopes and the compatibility challenges between cavity optomechanical sensors and MEMS processes, this paper proposes a novel integrated architecture by synergistically designing a double-decoupled cavity optomechanical system and an anti-collapse ridge waveguide. Firstly, the double-decoupled structure reduces mechanical coupling between drive and sense modes, enhancing resistance to environmental vibration. Secondly, an innovative ridge waveguide is designed on a SOI substrate, where a 500 nm silicon layer is retained with 400 nm etched on each side, protecting the silica layer from collapse. The process requires only EBL and dry etching to release the proof mass, significantly simplifying the fabrication process. Finite element simulations and numerical calculations validate the performance: the cavity optomechanical system achieves an angular measurement sensitivity of 318.7 mV/[(°)/s] and an angle random walk of 0.16 (°)/h^1/2, while the ridge waveguide exhibits 82.9% transmission efficiency and 46.3% end-face coupling efficiency at 1550 nm. This study provides a scalable technical pathway for high-precision micro-gyroscopes based on cavity optomechanical systems, with reduced process complexity, and showing promising potential in navigation and control applications.

MEMS inertial sensors, which mainly include MEMS gyroscopes and MEMS accelerometers, are miniature components for navigation, positioning, and attitude measurement. However, the performance is significantly degraded by bias drift arising from structural design, packaging process, environmental fluctuations, and hardware circuits. Therefore, investigating bias drift compensation techniques holds considerable research significance. Firstly, the classification and fundamental principles of MEMS inertial sensors are introduced, and then the primary sources of bias drift are analyzed, including frequency splitting, packaging-induced stress, temperature fluctuations and phase shifts in circuits. The research status of bias drift compensation technology is summarized, mainly covering modal matching, stress compensation, temperature compensation, and phase compensation. The technical advantages and characteristics of each method are systematically discussed. The current research status and future development directions are summarized and discussed, which is of great significance for the advancement of high-precision MEMS inertial sensor technology.

With the development of MEMS gyroscope-related error technologies, phase error has become a significant factor limiting their performance as high-precision, high-stability inertial devices. To address issues such as quadrature error coupling into angular rate output caused by phase error, the phase error in control circuits is mathematically modeled and derived. Subsequently, a parabolic interpolation method is proposed to identify and compensate for phase error by continuously updating the search interval through iteration to locate the minimum drive voltage amplitude, thereby achieving identification and compensation objectives. Compared with traditional methods, the approach proposed is more efficient and well-adapted to the closed-loop control of gyroscopes. Experimental results demonstrate that after phase error compensation, the drive amplitude is minimized, the quadrature coupling error in angular rate output is eliminated, and the scale factor performance is improved. While the angle random walk remains unchanged, bias instability is reduced significantly from 0.762 (°)/h to 0.117 (°)/h. This method effectively resolves the issues of slow identification and insufficient compensation accuracy for phase errors.

Quantum sensors have the potential to transcend the limitations of classical measurements. Substituting classical sensors with quantum sensors and developing high-precision quantum navigation technology emerges as a potential technological approach to boost navigation capabilities. Nevertheless, the engineering progress of quantum navigation technology has been sluggish, mainly constrained by several issues which include the complexity of quantum navigation systems, the degradation of measurement accuracy in dynamic environments, and the relatively large size and weight. In this paper, drawing on quantum navigation experiments conducted in recent years, the latest solutions to the aforementioned problems are systematically reviewed. Moreover, the most promising development directions are identified, aiming to provide valuable references for the engineering advancement of quantum navigation technology.

Atomic inertial measurement systems based on the spin exchange relaxation free (SERF) principle offer advantages of high sensitivity and miniaturization but exhibit extreme sensitivity to laser power fluctuations. To address the limitations of traditional proportional integral derivative (PID) control in handling model uncertainties and low-frequency disturbances, this paper proposes a laser power stabilization method based on linear active disturbance rejection control (LADRC). For the transfer function between liquid crystal control voltage and main-path optical power established through system identification, a linear active disturbance rejection controller incorporating an extended state observer (ESO) is designed to achieve online estimation and active compensation of total disturbances. Simulation and experimental results demonstrate that this method outperforms PID control in tracking performance and disturbance rejection capability. The root mean square value is reduced from 3.07×10^－4mW to 1.17×10^－4mW, while the noise power spectral density of the gyroscope signal at 1 Hz is reduced from 1.02×10^－5(°)/s/Hz^1/2 to 7.39×10^－6(°)/s/Hz^1/2, improving stability by approximately 27.5%. This study meets the power stability requirement of SERF atomic inertial measurement systems at the level of 10^－4mW, and further provides a highly robust optimization pathway, extending the application boundaries of active disturbance rejection control in the field of quantum precision measurement.

Bionic flapping-wing aerial vehicles(FWAVs) imitate the flight mode of natural organisms(such as birds, insects), which have the advantages of strong concealment, high aerodynamic efficiency and strong ability to adapt to complex wind environment and can be used in information reconnaissance, environmental monitoring, disaster rescue and other scenarios. How to realize their autonomous navigation in complex scenes is a research hotspot in academia. Firstly, the inertial-based multi-source autonomous navigation system of the FWAV is focused on, including inertial/visual navigation, inertial/satellite navigation, inertial/visual/satellite navigation and other methods. The major advance of typical FWAV navigation systems at home and abroad is analyzed. Then, the key technologies of multi-source autonomous navigation of the FWAV are introduced. The technologies of image stabilization, perception-based localization and mapping, trajectory planning and autonomous obstacle avoidance of the FWAV are analyzed, which highlights the particularity of autonomous navigation of the FWAV and the necessity of multi-source fusion. Finally, the future development trend of multi-source autonomous navigation system of the FWAV is introduced, including open architecture, performance improvement, information fusion, group perception and other research directions.

The accuracy and reliability of geomagnetic navigation technology depend on the high-precision measurement of magnetic field information. To address the problem of inaccurate magnetic field measurement in geomagnetic navigation, a vector magnetic field real-time tracking and calibration method based on diamond nitrogen-vacancy(NV) center magnetometry is proposed. The method combines optical detection magnetic resonance technology with multi-channel microwave frequency modulation to extract the magnetic field information of each NV-axis from fluorescence signals captured by a single photodetector. Real-time demodulation and resonance frequency tracking are performed, and the voltage values are converted into three-axis magnetic fields through vector calculation, followed by calibration of the coordinate system and orthogonality. A vehicle-mounted vector magnetic field measurement experiment is conducted and compared with a fluxgate magnetometer. The three-axis errors are all less than 4%, verifying that the proposed method can perform real-time measurement of rapidly varying magnetic fields. Magnetic field changes can be detected within 10 ms, meeting the requirements for high-precision and high-sensitivity vector geomagnetic field measurement.

The system of a nuclear magnetic resonance gyroscope requires heating to 100℃~110℃ to increase the density of alkali metal atoms, thereby enhancing the spin-exchange collisional polarization effect, improving the polarization level of inert gas atoms and the signal intensity measured in the experiment. In this paper, the influence of the AC Stark effect on the measurement results for the nuclear magnetic resonance gyroscope system is considered when high-frequency current is used to heat the atomic vapor cell. A scheme is proposed to suppress the magnetic field generated by the heating coil using the interaction of multiple parallel magnetic moments in the plane. This scheme optimizes the radius distribution and current direction of each coil in space, enabling mutual cancellation of low-order magnetic field components when multiple circular coils are present, significantly reducing the residual magnetic field near the origin. Numerical calculations show that this coil design can reduce the magnetic field generated by each milliampere of current to 0.6 pT. When considering the actual heating requirements, the coil will produce a magnetic field of 25 pT, the measurement error introduced by the heating system is reduced to 10^-12 Hz, meeting the heating requirements of the nuclear magnetic resonance gyroscope while effectively improving the measurement accuracy of the system.

Frequency modulated continuous wave(FMCW) laser ranging technology has great potential for application in the fields of satellite formation flight, spacecraft rendezvous and docking, with its high-precision ranging and speed measuring capability. However, the inherent nonlinearity of the laser leads to the broadening of the signal spectrum, which restricts the improvement of measurement accuracy. In order to solve this problem, a nonlinear correction technology and device based on semiconductor butterfly laser are proposed, which generates specific phase points as sampling trigger clock by beating the auxiliary interference optical path to realize resampling of the measurement signal, thus suppressing the signal spectrum broadening and realizing the nonlinear correction of the laser. The nonlinear correction technology is verified experimentally by setting up optical device, and the repeated measurement accuracy is less than 20 cm within 100 m measurement range. The experiment results show that this technology can effectively restrain the signal spectrum broadening caused by nonlinearity on the basis of system miniaturization, and improve the stability of distance measurement.

Achieving efficient obstacle avoidance and stable navigation in complex dynamic environments remains a critical challenge for the development of service robots. Traditional methods often rely on static maps or frequent iterative computations of locally optimal trajectories, which are prone to local optima or collisions in dynamic and narrow corridors. To address these limitations, an end-to-end navigation framework integrating spatio-temporal perception with knowledge distillation is proposed. Specifically, a PredRNN-based structure is employed to model image sequences and capture spatio-temporal features. It further incorporates a Teacher-Student architecture. The Teacher network, which utilizes LiDAR and prior knowledge of dynamic obstacles, generates high-quality policies to distill both perception and behavior into the Student network, which takes depth images as input. This enables the Student network to inherit Teacher knowledge while achieving robust decision-making. Experimental results demonstrate that the proposed method improves the success rate of reaching target destinations by 13% compared to the best-performing baseline in complex dynamic scenarios. Overall, this framework overcomes the limitations of traditional approaches in dynamic, narrow, and perception-constrained environments, exhibiting stronger generalization and adaptability, and offering a novel perspective for ensuring safe and efficient operation of service robots in real-world complex environments.

Facing the demand for improving the efficiency of inertial navigation systems in emerging industries such as low altitude economy and unmanned systems, traditional discrete component interferometric fiber optic gyroscopes face constraints such as large volume and high cost. The new fiber optic gyroscope based on integrated optical chips has disruptive advantages in balancing gyroscope accuracy, volume, cost, and power consumption, demonstrating enormous application potential. This paper reviews the working principle and system scheme of interferometric fiber optic gyroscopes, introduces the material characteristics of thin film lithium niobate, analyzes the on-chip structure required for thin film lithium niobate chips for fiber optic gyroscopes based on its technical status, and summarizes the process steps for chip preparation. Subsequently, the relevant achievements published in the field are introduced, revealing the ability of thin film lithium niobate chips to improve system integration. Finally, in response to the difficulties and challenges encountered in the research and application process, the performance requirements for thin film lithium niobate based chips used in fiber optic gyroscopes are summarized, and prospects for future development directions are proposed.

MEMS gyroscopes exhibit significant temperature-dependent performance variations due to their micro-mechanical structures and the thermally sensitive characteristics of silicon-based materials. To address this issue, a temperature compensation method based on Bayesian-optimized adaptive segmentation polynomial fitting is proposed. Overcoming the limitations of conventional polynomial fitting in temperature interval partitioning and parameter optimization, the proposed approach incorporates three key technical innovations. An adaptive, data-driven temperature range segmentation using K-means clustering; the simultaneous optimization of optimal polynomial order and regularization coefficients within each temperature interval through Bayesian optimization; and the integration of L2 regularization to effectively suppress model overfitting while enhancing generalization capability. Experimental results demonstrate substantial performance improvement for the MEMS gyroscope across a wide temperature range (-40 ℃ to 60 ℃), with bias stability enhanced from 1.2 (°)/s to 0.047 (°)/s and bias instability reduced from 0.0023 (°)/s to 0.0016 (°)/s.

Large-scale constellation networking needs to solve the problems of orbital coordination for thousands of satellites, as well as the efficient networking of inter-satellite links and satellite-to-ground links. In the face of a vast amount of spatial data information, spatial laser communication, as a transmission method with large capacity, strong anti-interference ability, high security and fast communication rate, has become the preferred choice for spatial information transmission. As the core component of laser communication systems, the photoelectric transceiver module plays a significant role in promoting the conversion of optical and electrical signals and improving the quality of signal transmission, and it is the foundation for achieving high-speed and highly reliable laser communication. In order to meet the requirements for an optical communicator that can be used on the space satellite, a fully hermetically sealed butterfly packaged optoelectronic transceiver module is designed. Corresponding anti-radiation optimization designs are carried out in three aspects: light source, software and materials. The optoelectronic transmission performance before and after irradiation is tested, and the irradiation effect results produced by different particles are analyzed. Experiments are conducted using ⁶⁰Co γ-ray radiation and proton radiation as irradiation sources, and a measurement system for radiation field and performance parameter acquisition is built. Data comparison between the anti-radiation module and the traditional module shows that, with the increase of irradiation dose, the optical power and extinction ratio of the traditional module both decrease significantly, while the optical power of the anti-radiation module remains ≥-3 dBm, the extinction ratio still meets ≥4 dB, and the errors are all within the required range. The experimental results demonstrate the stability and good anti-radiation performance of this optoelectronic transceiver module.

With the widespread application of nuclear technology, the use of mobile robots to replace human operators in executing nuclear emergency tasks within unknown radiation environments has become increasingly important. However, due to limitations in detection time and sensor performance, robots can only obtain sparse radiation data. Nonetheless, in order to facilitate nuclear safety monitoring, it is essential to obtain the radiation fields distribution and the locations of radioactive sources in the environment. To address the above problems, a source localization method integrating two-dimensional laser SLAM and radiation features is proposed. This method uses mobile robots equipped with nuclear radiation detectors, LiDAR, and other sensors to collect radiation data and construct environmental maps. Subsequently, it uses Gaussian process regression method to invert the regional radiation field and integrates the inverted radiation field into the SLAM environmental map. Finally, the Hough transform method is applied to locate unknown radioactive sources. In addition, experimental verification is conducted in real environments where radioactive sources are present. The experimental results show that based on occupancy grid maps constructed using three laser SLAM algorithms (Gmapping, Hector, and Cartographer), the fusion of global radiation environment maps can be completed in both open space and factory environments, with localization accuracy exceeding 0.29 m.

The behavioral learning of embodied intelligence relies on high-quality robot manipulation data. However, real-world robotic data collection is costly and limited in scale, while internet-scale video data, though abundant, lacks action and state annotations. To address the challenge of extracting state representations from unlabeled videos, a video pre-training-based behavioral learning method for embodied intelligence is proposed. Firstly, an unsupervised video pre-training framework is constructed to achieve latent state extraction through feature extraction encoding, static-dynamic feature separation, and cross-frame consistency constraints. Secondly, a multimodal Transformer architecture is designed, integrating patch-wise attention mechanisms with dynamic action heads to accomplish multimodal information fusion and adaptive action generation. Simulation results demonstrate that the proposed method achieves up to 32.96% performance improvement over the baseline method Moto in task execution on CALVIN and SIMPLER simulation environments. It also exhibits significant advantages in both unknown environment generalization and environmental robustness testing, effectively enhancing the behavioral learning capabilities of embodied intelligence.

In recent years, along with the development of new equipment carriers such as unmanned aerial vehicles and unmanned submarine vehicles, inertial navigation systems have generated an urgent demand for gyroscopes that combine high performance with miniaturisation, low cost and lightweight features.With the rapid development of integrated photonics, integrated optical gyroscopes have emerged, and interferometric integrated optical gyroscopes are one of the important ones.This paper outlines the basic working mechanism of IIOG based on the Sagnac effect, and provides a detailed summary of the current research status of interferometric integrated optical gyroscope from two major aspects, namely, the performance enhancement of discrete devices and the integrated coupling package of the system.The main challenges encountered in the current research and applications are analysed, and the future development trend of interferometric integrated optical gyroscopes (IIOGs) is envisioned.