This article explores the current state of bionic multi-source navigation mechanisms and information fusion technologies, with the goal of offering fresh insights and approaches for advancing multi-source autonomous navigation technologies. At the beginning, the article examines the limitations of today's navigation systems and highlight why multi-source information fusion is essential. Next, the article focuses on how animals use multi-source information fusion for navigation, including their strategies, methods of information integration, sensory systems, and neural mechanism. Animals are remarkable in their ability to combine various sensory inputs for complex environmental awareness and precise navigation. The article also discusses bio-inspired multi-source navigation information fusion technologies, such as fusion models, algorithms, and bionic computing frameworks. The visual navigation mechanisms and strategies of insects can serve as valuable inspiration for designing efficient and intelligent autonomous navigation systems. Finally, the article anticipates future research directions and emphasizes the importance of gaining deeper insights into animal navigation mechanisms and developing bio-inspired navigation algorithms.

Inertial technology is widely used in navigation, positioning, stable attitude and direction control of various carriers such as the sea, the land, the air, the space, and the electricity, and has become an indispensable sensitive source for dynamic autonomous perception in emerging warfare modes. By reviewing the literature of inertial technology related conferences such as the 2024 IEEE International Symposium on Inertial Sensors and Systems, DGON Inertial Sensors and Systems Symposium and IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS), as well as the dynamic information disclosed by relevant institutions in the field of inertial technology, the current development status of inertial instruments and inertial navigation systems (INS) in the market, including optical gyroscopes, MEMS gyroscopes, hemispherical resonant gyroscopes HRGs, accelerometers, and quantum inertial sensors are reviewed, summarized, and concluded. And the development trend in the field of inertial technology is analyzed and prospected.

Inertial navigation technology relies on the inertial measurement device in the inertial navigation system to determine the spatial position and attitude of the carrier, which can provide reliable navigation accuracy in the satellite rejection environment. As the key core device of the inertial navigation system, the performance accuracy of the accelerometer directly affects the positioning and guidance accuracy of the inertial navigation system. The resonant accelerometer based on the force-frequency principle of quartz resonator has the characteristics of high-precision, small-size, low-cost and frequency signal output, which has attracted the attention of relevant research institutions at home and abroad. In this paper, the latest research progress of quartz resonant accelerometer is reviewed, the development status of quartz resonant accelerometer is summarized, and its future development trend is prospected.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The error equation under the earth coordinate system of the launch point is a nonlinear multivariate cross-chain equation, and a simplified scheme is adopted to solve the error coefficients of the guidance tool in engineering. In this paper, for the problem of mismatch between the velocity environment function and the telemetry velocity error caused by the approximate linearization of the simplified error model, a method to improve the accuracy of inertial guidance based on error feedback correction is proposed. Firstly, a high-order error model of inertial measurement system is established, in which there are 60 error coefficients in the error model of gyroscope and accelerometer, and then the attitude, velocity and position error equations based on the earth coordinate system of the launching point are given, and the simplified ambient function is derived according to the linear model and the computation method of its problems is summarized. Secondly, the attitude, velocity and position error feedback is utilized to correct and compensate the velocity error, and a new velocity environment function is obtained. Finally, the least squares method is used to solve the error coefficients of the guidance tool and set the insignificant terms to zero to compensate the inertial guidance telemetry observations, and the telemetry velocity error compensated by using the environment function based on the error feedback correction has a smaller mean and standard deviation than that compensated by using the simplified environment function calculation, which shows that the method proposed in this paper has some application value.

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.

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.

Navigation signal processing system is an important part of navigation system, which not only needs to have fast signal processing ability and high stability, but also to realize miniaturization and lightweight to meet the needs of sea, land, air and space equipment. In response to the various requirements, the navigation signal processing microsystem circuit based on SiP technology is developed by using high-density integration process and advanced system in packaging (SiP) technology. The internal integration of high capacity storage resource packaging (including FPGA, NOR Flash and multiple DDR3 chips) is realized by adopting fully domestic components. The size is reduced to 26.15 mm×18.45 mm, which is only 7% of the original board area; and the weight is 7.5 g, which is 45% of discrete devices. The top is covered with a high thermal conductivity shaped heat dissipation cover, which improves the mechanical performance of the circuit and heat dissipation capacity. When used with various kinds of data collection front-ends, the circuits can realize rapid coding and decoding of navigation signals, algorithm acceleration, high-speed signal type conversion, comprehensive information processing, high-speed communication and other functions, and meet the needs of miniaturization and lightweight.

A novel interferometric fiber optic gyroscope (I-FOG) based on a “four-in-one” multifunctional integrated optical chip and micro polarization-maintaining photonic-crystal fiber (PM-PCF) coil is proposed. The “four-in-one” multifunctional integrated optical chip integrates super-luminescent diode light source, couplers, thin-film lithium niobate (TFLN) modulator and photodiode detector employing a hybrid integration technology. To obtain a high sensitivity effect and minimize the I-FOG as much as possible, a type of 60 μm/100 μm ultra-thin diameter PM-PCF is customized to wind the interference ring, and a high-accuracy integrated I-FOG prototype with the volume of Φ30 mm is achieved. It experimentally demonstrates a smooth bias stability of 0.23 (°)/h(1σ)at the integration time of 10 s, with an angle random walk (ARW) of 0.012 (°)/h^1/2 and a scale factor nonlinearity of 2.83×10^-5 over the range of ±100 (°)/s at room temperature. It also shows a smooth bias stability of 0.51 (°)/h (1σ) at the integration time of 10 s over the temperature range from -30~60 ℃. Compared with conventional I-FOG with discrete photo-electric devices, the I-FOG proposed has both small volume and high accuracy, which has unique advantages for application in the field of new-type tactical weapons, unmanned systems and other fields.

In recent years, much research attention has been paid on resonant fiber optic gyroscope(RFOG). As an important inertial navigation sensor, RFOG has advantages of lightweight and high stability. However, the optical effects in the fiber ring resonator(FRR), including scattering and polarization mode crosstalk, limit the development of RFOG and become a core and urgent problem for researchers to solve. In this paper, the polarization stability of the fiber optic resonator has been researched in depth both theoretically and experimentally. Firstly, theoretical analysis has been made on the polarization noise in RFOG and the suppression effect of polarization noise by circular polarization maintaining fiber. Then, the temperature stability of circular polarization maintaining fiber resonators and polarization maintaining fiber(PMF) resonators are compared, and the drift of the intrinsic polarization phase differences for PMF and circular polarization maintaining fiber under temperature changes are simulated. Finally, a phase change acquisition experiment of the resonator with temperature is designed, which experimental results show that the resonant cavity of the circular polarization maintaining fiber is 5.496 rad, and the resonant cavity of the PMF is 663.65 rad. Compared to the PMF resonant cavity, the polarization stability of the resonant cavity using a circular fiber under the same conditions has been improved by 121.79 times. A new approach of solving the polarization noise problem in RFOG is provided in this paper, which lays an important foundation for the practical application of circular polarization maintaining fiber in RFOG.

The efficacy of the inertial sensor array is contingent upon the correlation between the sensors. The asynchronous acquisition time between the sensors represents a significant limiting factor in the accuracy of the measurements. To enhance the precision of measurements obtained from a consumer inertial sensor array, a parallel transmission bus data acquisition system is devised. This system employs a shared clock and data line to minimize the discrepancy in acquisition time between sensors. The impact of varying designs on the performance of inertial sensor arrays is evaluated, employing precision indexes such as zero bias stability and Allan variance. The experimental results demonstrate that the sensor array is capable of reducing random errors, such as zero bias stability and angle random walk. Furthermore, the parallel bus mode has been shown to enhance the zero bias stability of the sensor by approximately 28% and the angle random walk by approximately 10%. The parallel bus design offers a viable approach to augmenting the number of sensors in the array, thereby enhancing the practical utility of the inertial sensor array.

In recent years, underwater navigation technology has garnered increasing attention for its potential applications in deep-sea exploration and underwater operations. However, traditional navigation methods face certain limitations in complex underwater environments. In contrast, marine organisms exhibit remarkable adaptability and precise navigation capabilities, showcasing unique advantages in survival and localization within such challenging conditions. In this paper, a comprehensive review of the navigation mechanisms for eight categories of underwater organisms is provided. The processes through which these organisms perceive, acquire, and process multidimensional information are thoroughly examined, including magnetic, electric, acoustic, visual, chemical, and gravitational/inertial cues, thereby uncovering the biological principles underlying their navigation behaviors. Furthermore, how these mechanisms can inspire advancements in underwater biomimetic navigation technology is explored in this paper. Finally, the limitations and challenges in current research on biological navigation behaviors are summarized and the future directions are discussed, aiming to provide theoretical foundations and technical support for the optimization and application of underwater biomimetic navigation technologies.

During the landing process of spacecraft for asteroid exploration, due to solar array power generation or to avoid image navigation difficulties, the small solar incidence angle results in the spacecraft’s shadow appearing in the navigation images, affecting navigation accuracy. To address this issue, a navigation algorithm based on the shadow edge features of the solar array for spacecraft is proposed. Firstly, the shadow edge features are extracted, and equations are constructed using the geometric relationship between the shadow shape and size and the spacecraft’s attitude and altitude to calculate the spacecraft’s attitude and altitude. Secondly, the obtained attitude and altitude are used to calculate the inter-frame rotation matrix and scaling factor, the current image is rotated, scaled, and interpolated to obtain the image to be matched, and the horizontal component of the spacecraft’s translation is calculated through template matching. Finally, a simulation platform is built using Unity3D for simulation verification. The results show that under the possible influence of noise, as the spacecraft’s altitude decreases, the position error and attitude error continuously decrease, with the final position error within 0.5 m and the attitude error within 0.2°.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The atomic spin inertia measurement system in spin exchange relaxation free (SERF) regime has the potential for ultra-high sensitivity surpass traditional inertial measurement instruments. However, the temperature error leads to the signal drifting which sufficiently decreases the inertial measurement system’s long-term stability. This paper proposes a working point optimization method to suppress the system’s sensitivity to temperature fluctuations, thereby reducing the system error caused by temperature drift. Firstly, based on the steady-state solution of the Bloch equations, the system output equation is derived, and the variation law of the system’s sensitivity to temperature fluctuations is studied. Then, an atomic spin inertia measurement device is built, and the influence of temperature working points on the temperature sensitivity and the long-term stability of the inertial measurement system is measured through experiments. The experimental results show that by using the working point optimization method, the temperature sensitivity of the system can be effectively suppressed, thereby reducing the zero-bias instability of the system by 50% and improving the long-term stability of the system.

Spin exchange relaxation free (SERF) atomic inertial measurement instruments, due to their ultra-high theoretical accuracy, have become a critical development direction for the next generation of inertial measurement devices. To address the low-frequency noise introduced by the cell temperature control process in SERF-based inertial measurement instruments, a noise feature extraction method based on variational mode decomposition (VMD) is employed. The method combines the artificial lemming algorithm (ALA) for adaptive optimization of the parameters of the VMD modal decomposition, and then quickly and accurately extracts the low-frequency interference noise features in the air chamber temperature control system. Based on the extracted noise features, the interference introduced by the data post-processing method of sliding median filtering in the temperature measurement process is localized, and an optocoupler isolation circuit based on the ACPL-C87A is designed to inhibit the reflection of electromagnetic noise by electrically isolating the power components. The experimental tests show that the designed optical isolation circuit significantly improves the device’s performance: in PID temperature control mode, the inertial measurement sensitivity at 1 Hz increases from 2.65×10^-5(°)/s/Hz^1/2 to 4.65×10^-6(°)/s/Hz^1/2, and the inertial measurement sensitivity at 2 Hz can be improved from 8.31×10^-6(°)/s/Hz^1/2 to 2.48×10^-6(°)/s/Hz^1/2. These results verify that the method effectively suppresses low-frequency noise interference, allowing the SERF inertial measurement device to maintain the fast response and high adjustment precision advantages of PID control while achieving sensitivity performance comparable to PI control.

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.

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.

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.