Navigation technology provides critical kinematic information and fundamental spatio-temporal references for human survival and development. In this paper, the evolution of classic navigation devices and physical principles is explored from three perspectives: application, scientific foundations, and functionality. By analyzing developmental patterns and structuring historical periods, the essential characteristics of navigation technology progression are outlined and its future progress trajectory is predicted. Furthermore, focusing on cutting-edge advancements in artificial intelligence, the emerging opportunities and challenges in construction of next-generation national comprehensive positioning, navigation, and timing(PNT) end-users are thoroughly studied. A transformative shift towards autonomy, multi-source and intelligent characteristics is highlighted, which provides strategic insights for innovation and development of future navigation systems.

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.

The micro hemispherical resonator gyroscope(μHRG) is an innovative micro-gyroscope that leverages the Coriolis effect, offering advantages such as a simple structure, compact size, low cost, high accuracy, excellent stability, and robust resistance to interference. Due to its significant potential, numerous researchers have been dedicated to advancing μHRG technology with a focus on enhancing precision and stability. Firstly, the working principle of the μHRG is summarized in this paper. Then, the manufacturing processes of the resonator are categorized into three main methods: micro glass expansion method, high-temperature blowtorch blowing method and thin-film deposition method. For each process scheme, the latest research progress of is domestic and international research institutions is discussed in detail, the advantages and disadvantages of each manufacturing process scheme are summarized. Finally, the research direction of micro hemispherical gyroscope resonator is prospected.

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.

MEMS gyroscopes belong to Coriolis vibratory gyroscopes, featuring advantages of small size, low mass, and low power consumption. With the development of MEMS design technology and domestic manufacturing processes, the zero-bias noise level of MEMS gyroscopes continues to decrease, having the potential to achieve navigation-grade performance. The research progress in the field of high-precision MEMS gyroscopes by domestic and foreign research units in recent years is introduced in this paper. The technical characteristics and development status of Type II gyroscopes represented by ring gyroscopes, quadruple-mass gyroscopes, and double Foucault pendulum structures, as well as Type I gyroscopes represented by fork structures reported by Honeywell and Polytechnic University of Milan are summarized. The key technical advantages and challenges faced by Type I and Type II gyroscopes at the current stage are discussed, which providing references and insights for domestic and foreign peers to carry out scientific research on this type of gyroscope structure and improve the performance of MEMS Coriolis vibratory gyroscopes.

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.

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.

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.

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.

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.

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.

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.

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.

In the use of Beidou receivers, the high dynamic characteristics of the system cause Beidou satellite signals to rapidly changing Doppler translations and code phases, which increases the difficulty of signal acquisition. The currently used PMF-FFT acquisition method consumes a large amount of hardware resources and running time, and is difficult to meet the speed requirements of Beidou receivers. In order to achieve Beidou signal acquisition in high dynamic scenarios, an capture algorithm based on improved PMF-FFT is proposed. Based on a detailed analysis of the mathematical theory of PMF-FFT, the correlator usage and noise interference of the acquisition system are reduced by integrating the internal storage of a single symbol. At the same time, multiple sampling clocks are introduced to ensure the clock conditions for chip integration. Finally, under the Beidou high dynamic scene, simulation is used to test the performance of this algorithm and the traditional algorithm. The results show that compared with the traditional algorithm, the noise suppression ratio of this algorithm is improved by about 1.3 dB and the running time is shortened by 7%. Therefore, this algorithm can reduce the use of correlator resources and improve the signal-to-noise ratio, and is suitable for Beidou under high dynamic conditions.

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.

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.

Securing control in complex low-altitude environments is of significant importance to modern warfare, and terrain-aided navigation is an effective means to achieve navigation and positioning in such environments. To address the issues of poor real-time performance and sensitivity to terrain undulations in traditional batch terrain matching algorithms, a terrain matching method based on adaptive particle swarm optimization is proposed in this paper. During the search and matching phase, the particle swarm optimization algorithm is utilized, superseding the traditional exhaustive search. A correlation computation model is established using sequential similarity detection, which is then employed as the fitness measure for the particles. Throughout the search iteration process, the inertia weight and acceleration factor are adaptively adjusted based on changes in particle fitness, thereby pinpointing the optimal match position. Then, based on terrain features, the usability of the matching results is assessd to further improve the match positioning accuracy. Simulation experiment results show that the positioning error of the method in this paper is only 67.4% of the traditional algorithm under steep terrain, and this result is 32.6% under flat terrain, the matching time is only 37.1% of the traditional algorithm.

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.

In the actual navigation process, the sensors of the multi-source fusion navigation system will change the measurement accuracy with the change of application scenarios. In this paper, a robust factor graph algorithm based on adaptive evaluation is proposed to address the issue of traditional factor graph algorithms being unable to handle dynamic changes in sensor measurement accuracy during the optimization process. By introducing measurement information evaluation indicators and adaptive weight functions, the real-time calculation of the residual between the inertial pre-integration prediction value and the auxiliary sensor measurement value during factor graph fusion optimization is dynamically adjusted, and the fusion information weight of the corresponding factor nodes is dynamically adjusted. Compared with traditional factor graph algorithms, this algorithm can improve the optimization accuracy and robustness of the factor graph algorithm when the measurement information of various auxiliary sensors is abnormal. The simulation experiment results show that the proposed robust factor graph navigation algorithm based on adaptive evaluation has higher robustness and accuracy compared to traditional factor graph algorithms when measurement errors occur in auxiliary sensors.

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.

With the continuous improvement of precision requirements for flight control systems in modern aircraft, the testing work of flight control systems has become increasingly important. Accurate input of initial attitude angle is crucial for evaluating the navigation performance of the aircraft during the flight control system testing process. The traditional manual input method has problems such as low efficiency and error susceptibility. To solve these problems, a method for automatically generating product attitude angles based on the angular velocity and velocity increment information output by the product itself is proposed, and its effectiveness is verified through experiments. The experimental results show that this method can effectively replace manual input and has been successfully applied in various processes such as product testing and delivery. It reduces the attitude setting time from an average of 30 min to 5 min, with a time reduction of 83.33%. At the same time, it avoids human errors and significantly improves the efficiency and reliability of the flight control system testing process.

During the flight of quadrotor unmanned aerial vehicle(UAV), it is often susceptible to uncertain environmental factors, leading to model uncertainties and unknown internal disturbances in the control system. A study is conducted on the path tracking control of quadrotor UAV under model uncertainty and internal unknown disturbances. An adaptive sliding mode control strategy based on recurrent neural networks is proposed for ensuring that the path tracking errors gradually converge using the adaptive sliding mode control strategy. Considering the lumped disturbance terms of the system, a recurrent neural network is used to estimate and compensate for the lumped disturbance terms, and adaptive control technology is introduced to estimate and compensate for the combined approximation errors of recurrent neural network, avoiding the complex calculation caused by the combined approximation errors on the system stability analysis and affecting the path tracking performance. The simulation results show that the control strategy proposed in this paper is effective and robust, and has good path tracking control performance.

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.

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 complex indoor environments where GNSS signals are rejected, ultra wideband(UWB) technology has attracted much attention for its advantages such as having high accuracy and high stability. Aiming at the problem of unmanned vehicle localization in indoor complex environments, a combined INS/UWB/altimeter/magnetic compass navigation algorithm based on adaptive federated Kalman filter algorithm for line-of-sight reconstruction(Re-Los-AFKF) is proposed based on inertial navigation system and UWB. The algorithm establishes the mathematical model of each sub-filter and uses a vector-based adaptive information allocation factor algorithm. The INS/UWB tight combination sub-filter is designed to predict the position parameters of the unmanned vehicle by using the characteristics of INS with high short-time accuracy to realize the non line-of-sight(NLOS) error detection in the UWB ranging information, use the data of adjacent moments to reconstruct the NLOS information for the line-of-sight(LOS) range, and carry out the over-compensation identification and the over-compensation correction for the reconstructed distance information. The experimental results show that the localization accuracy of the Re-Los-AFKF algorithm is comparable to that of high double sided two-way ranging(HDS-TWR) and movmean-federated Kalamn filter(MFKF) in LOS environment. In the complex NLOS environment, Re-Los-AFKF reduces the mean value of error in horizontal position by 46.24% and 29.35%, and reduces the RMS of error in horizontal position by 41.40% and 29.18% compared to HDS-TWR and MFKF, respectively. In summary, it is shown that the algorithm is not only capable of stable localization in LOS environment, but also has good localization performance in NLOS environment, which has certain adaptability, robustness and engineering 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.

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°.

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.

Pressure sensor applied to the measurement of pressure in the comprehensive harsh working environment of high temperature vibration and radiation shock, not only requires strong anti-interference ability, but also needs to have a high degree of linear response and stability. In this paper, a precise dispensing packaging system for special MEMS silicon pressure sensor is developed to solve the problems of low efficiency and poor precision in manual assembly. Firstly, the leadless packaging technology of piezoresistive chip and metal tube shell with nano conductive silver paste is determined, and the influence of the amount of glue and positioning error on the performance of the sensor is simulated. Then, combined with the leadless packaging process of the sensor, the system assembly structure is designed, and the key technologies including binocular micro-vision, micro-operating tools, coarse limit tooling, precision dispensing, high-precision motion platform and feature recognition algorithm are studied. Finally, the micro-assembly process of the sensor is developed, that is, the platform provides movement through micro-visual positioning, and after precision dispensing, the micro-operating tool transfers the position of the metal tube and adjusts the attitude to realize the high-precision assembly of the sensor. The experimental results show that the automatic micro-assembly accuracy of the sensor is better than 10 μm, the assembly time is less than 20 s, and there is no obvious rubber overflow phenomenon.

The servo stability loop constitutes a pivotal component within the high-precision inertial navigation systems, where its performance significantly influences the navigation accuracy. The friction interference torque emerges as a primary external disturbance within the servo stability loop, exerting considerable impact on the error dynamics of the platform stabilization loop. Consequently, addressing the compensation of friction and mitigating its effects on the servo stability loop is identified as an urgent and critical issue. Given the challenges associated with modeling and compensating for friction interference at the shaft end, this study delves into the friction model parameter identification and friction compensation control methodologies. Through the establishment of the LuGre axial friction model, predicated on the outcomes of the identification process, this research utilizes the friction feedforward compensation approach based on the LuGre model to enhance the servo stability loop, which integrates a fiber-optic gyroscope. The efficacy and enhancement in reliability and accuracy of the inertial navigation system are substantiated through the Simulink simulation results. When the input step disturbance torque amplitude is 0.06 N·m, the angle position error decreases by 14.33" and 47% after adding friction feedforward compensation.

The conductive slip ring transmits power and signal through the contact between slip ring and brush wire, and its reliability and service life depend on the contact force and contact stress. In order to explore the contact characteristics of slip ring-brush wire under the installation deviation of V-groove slip ring and the molding and springback of brush wire, finite element method is used to simulate the assembly process of slip ring-brush wire. The variation law of contact force and contact stress under different slip ring installation inclination angles, rotation angles, center distances and offset distances, as well as brush wire opening distances are analyzed. In addition, the contact resistance of slip ring-brush wire is measured by vibration test. The test results show that the inclination angle and rotation angle have a significant impact on the contact position and contact stress, while the installation center distance, offset distance, and the brush wire opening distance have a more significant impact on contact force. When the brush wire opening distance is adjusted to 2.5 mm, the contact force is 0.0518 N, and the fluctuation of contact resistance can be controlled less than 100 mΩ.

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.

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 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.