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.

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.

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.

With the development of high-precision long endurance inertial navigation systems, vertical deviation gradually replaces inertial component error as the main source of error. The existing methods for obtaining vertical deviation mainly include measurement and indirect estimation, both of which highly rely on high-precision equipment or a large amount of high-precision real measurement data. This study is using exponential anomaly, second-order Markov anomaly, second-order Markov undulation, and third-order Markov undulation models to model vertical deviation, establish the relationship between terrain and statistical model characteristic parameters: root mean square of vertical deviation, and inverse correlation time. Based on the vertical deviation statistical model, further research is conducted on the error propagation characteristics of inertial navigation systems, exploring the quantitative relationship between velocity error, position error, and statistical model characteristic parameters. This research is enriching the interpretability of vertical deviation data and is providing a reference for simulation research of navigation systems.

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.

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.

It is difficult to experience the complexity and variability of the modern battlefield in the set scenario of the simulation test, and it is difficult for a single unmanned system to adapt to the changing battlefield environment, so the unmanned cluster system has gradually become an important force in deciding the victory or defeat. Unmanned cluster cooperative navigation technology is one of the key technologies for unmanned system cluster energy efficiency. In view of the extensive involvement of unmanned cluster cooperative navigation technology in many application fields, this paper takes cluster UAVs as a typical example, analyzes in-depth the organizational configuration, navigation information resources, cooperative algorithms and optimization of unmanned cluster cooperative navigation technology, and discusses and looks forward to the future development trend of cooperative navigation in the light of the key problems it faces.

Wireless charging technology is a kind of technology that realizes energy transmission through non-contact method, with the advantages of high safety and strong environmental adaptability, which is one of the key technologies to enhance the unattended capability of underwater autonomous vehicles(AUV). To meet the special requirements of anti-misalignment and miniaturization design of receiving device in wireless charging system, an AUV wireless charging system based on dynamic traveling wave magnetic field is proposed in this paper. Firstly, a new magnetic coupling device consisting of orthogonal transmitting and dipole receiving overlapping is designed. The two layers of transmitting windings are decoupled from each other to form a constant amplitude traveling wave under the space-time coordination mechanism to suppress output fluctuation. Then, a wireless charging system circuit based on current doubler is proposed and analyzed. Finally, an experimental platform is built to verify the feasibility of the proposed system. The results show that the system can deliver 1.2 kW to the load with a system efficiency of 87.04%, and especially the proposal achieves stable output in the range of rotational misalignment of [-30°, 30°] and axial misalignment of [-30 mm, 30 mm].

The whole angle mode hemispherical resonant gyroscope has the characteristics of wide measurement range, small size, high reliability and low cost, and has a wide range of application prospects in land, sea, air, space and other fields. Due to the unavoidable geometric errors and damage of the gyrometer head, it leads to large losses and uneven distribution of the circumferential damping of the meter head, which will lead to the locking area of the gyroscope and affect the application of the gyroscope. On the basis of analyzing the influence of damping inhomogeneity on gyro-locking area, the suppression effect of different rotational modulation schemes such as unidirectional rotation, square wave, triangular wave and sine wave on gyro-locking area is studied. A dual-axis rotation test platform is built, and tests such as the detection of the Earth's rotation angle rate, the nonlinearity of the scale factor and the detection of the lock area are carried out for the gyroscope prototype of the 0.1 mHz damping inhomogeneity. The experimental results show that the new swing rotation modulation lock area suppression method reduces the gyro lock area from 38 (°)/h to below 0.048 (°)/h, which lays a foundation for the engineering application of hemispherical resonant gyroscope in whole angle mode.

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

With the continuous development of underwater environment perception technologies, acoustic-based sensing methods have gradually become mainstream, benefiting from the long propagation distance and wide coverage of sound waves in water. Among various acoustic sensing technologies, forward-looking sonar, with its ability to detect objects in the field of view in real-time, plays a crucial role in underwater environment perception and has been widely applied in fields such as fisheries, maritime safety, and military operations. However, the performance of forward-looking sonar is limited by the acoustic propagation characteristics and interference from the complex underwater environment. Its high noise and low signal-to-noise ratio data present significant challenges for sonar imaging and target detection. While traditional sonar image denoising methods have been extensively validated and applied in practical scenarios, deep learning-based sonar image denoising technologies have shown more prominent advantages in dealing with the complex noise found in forward-looking sonar data. The field of forward-looking sonar target detection has undergone a revolutionary shift from traditional algorithms to deep learning methods, significantly improving detection accuracy and generalization capabilities. This paper reviews the development of sonar image denoising and target detection in both traditional and deep learning methods, systematically summarizes current research progress and methodologies, and highlights emerging innovations based on deep learning. It also analyzes the prospects for application in complex underwater environments and discusses potential future research directions, including data fusion, algorithm optimization, and challenges in real-world applications.

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.

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.

The operation of underwater vehicles necessitates the implementation of navigation systems, which are responsible for the provision of positional data and the delivery of navigational services. It is not possible to provide effective satellite navigation services in an underwater environment, as the signals in question are unable to enter the water. In response to the demand for underwater navigation applications for underwater vehicles, the underwater variation characteristics of Loran-C signals are analyzed and an underwater Loran-C signal reception method is proposed. Firstly, the land propagation capability of the Loran-C system is investigated. Secondly, the influence for the Loran-C signal in an underwater environment is explored. Finally, the influence of dispersion effects on signal strength and perimeter difference under different water depth conditions is analyzed in detail. The simulation results demonstrate that as depth increases, the third cycle identification point of the underwater Loran-C signal is shifted forward, and the perimeter difference increases. Furthermore, the perimeter difference of the Loran-C signal exceeds the usage limit after 3 m underwater, resulting in signal unavailability.

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.

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.

In order to achieve autonomous fault-tolerant navigation for vehicles in satellite signal rejection environment, an integrated navigation method using a strapdown inertial navigation system and distance sensor is proposed. For the velocity sensor, a vehicle Doppler velocity radar is selected, and for the distance sensor, an odometer is selected. The error analysis and modeling of strapdown inertial navigation system, Doppler velocity radar and odometer are carried out respectively, and the state equation of the integrated navigation is established by taking the errors of the strapdown inertial navigation system and others as states. The measurements of inertial/Doppler radar and inertial/odometer integrated navigation are respectively constructed by using the velocity and attitude output of strapdown inertial navigation system, the velocity output of Doppler velocity radar and the distance output of odometer, and the corresponding measurement equations are established. The federated filtering fault-tolerant structure is adopted to fuse the results of local filtering inertial/Doppler radar and inertial/odometer integrated navigation globally. The simulation results show that this method can not only achieve autonomous fault-tolerant navigation for vehicles, but also has high navigation accuracy. The positioning accuracy is better than 27.1 m without sensor failure, the heading accuracy is better than 5.6′, and the horizontal attitude accuracy is better than 0.2′, while the positioning accuracy is better than 49.6 m, the heading accuracy is better than 8.5′, and the horizontal attitude accuracy is better than 0.3′ in the event of a sensor failure.

In order to solve the problem of selecting high-precision observation stars from multiple stars as navigation star points in the astronomical positioning of inertial navigation-aided star sensors, a stargazing accuracy evaluation method based on object image conjugation based on point light source was proposed. In this method, the star vector successfully recognized by the star sensor is converted into a horizon coordinate system parallel to each axis of the star sensor, and the stargazing accuracy is evaluated by calculating the values of the acute angles between the object vector and the image vector of the star recognition. After the accuracy evaluation, the observation star with high accuracy is used as the navigation star and applied to the astronomical positioning of the inertial-assisted star sensor. The results of field data processing show that the proposed method can effectively select navigation stars with high accuracy, and the positioning results of the selected navigation stars are higher than those obtained by astronomical positioning before selection, and the data variance and standard deviation are 1 to 3 orders of magnitude lower.

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.

The primary challenge faced by the INS/GNSS is to achieve reliable and cost-effective positioning during GNSS outages. Under certain conditions, such as tunnels, tall buildings in urban areas, and adverse weather conditions, prolonged GNSS signal loss may occur, rendering the INS/GNSS fusion navigation system to degrade to a standalone inertial navigation system. To address this issue, a novel INS/GNSS fusion navigation algorithm based on GRU neural networks in the presence of GNSS denial is proposed. This algorithm operates by utilizing the inertial navigation parameters as inputs to the GRU neural network when GNSS signals are available, while simultaneously utilizing the three-dimensional position information provided by GNSS as the output of the GRU neural network and training it. Subsequently, when GNSS signals disappear, the inertial navigation parameters are used as inputs to the trained GRU model to obtain the three-dimensional position information of GNSS, enabling INS/GNSS fusion navigation even in the presence of GNSS signal denial. The simulation results, with a maximum positioning error of 5.646 m, demonstrate that this algorithm effectively ensures the accuracy and robustness of INS/GNSS fusion navigation under GNSS signal denial.

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.

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.

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.

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.

Due to the specific task limitations of underwater vehicles and the need for long endurance and covert navigation positioning, there are no prerequisites for the carrier to achieve position correction within the matching area in certain specific scenarios. To achieve entire course navigation positioning, a gradient difference entire course matching method based on particle mass filter algorithm is proposed. Extract the gravity anomaly characteristic parameters and adopt the comprehensive feature entropy weight method to select the matching area, effectively providing prior navigation area support for the implementation of the entire course gravity matching algorithm. According to gravity measurement circle probability error constraint, gradient difference is applied to gravity anomaly values to determine the sampling frequency of matching method. Fully explore the gravity anomaly information in the large background field and achieve adaptive frequency filtering in areas where gravity anomaly changes are not significant. The fully affine transformation using least squares optimization parameters is used to smooth the matching trajectory, ensuring the accuracy and stability of matching localization. The simulation experiment results show that the proposed algorithm can effectively control the matching positioning accuracy within half a grid, which can meet the navigation and positioning requirements of entire course gravity matching for underwater vehicles.

The number of seafloor acoustic navigation beacons determines the solvability of acoustic navigation. For example, when using a single seafloor acoustic beacon for navigation, it is necessary to integrate information such as the vehicle’s heading or attitude. The precision level of active sonar navigation system under varying numbers of seafloor acoustic navigation beacons is explored using vehicle state estimation model within a sliding window. Designing a simulation experiment to analyze the navigation accuracy of an active-sonar navigation model as the number of seafloor acoustic navigation beacons increased from one to four. The results obtained from the simulation experiment are further validated using actual measured data which show that: the accuracy of the active sonar navigation is directly proportional to the number of observable seafloor acoustic navigation beacons. When there are four beacons, the decrease in accuracy caused by reducing the number of beacons is within 1 m. However, the accuracy significantly decreases when there is only a single beacon, but the navigation accuracy can still be maintained within 10 m.

Polar regions occupy a significant position in strategic, economic, and scientific research fields. Due to the unique natural environment of the polar areas, underwater vehicles have become key to human exploration and development of the poles. Affected by factors such as large-scale ice coverage and satellite denial, there are numerous challenges in the operation of underwater vehicles beneath the ice, with the primary issue being the acquisition of precise positional information of the vehicles. Sound waves are the only effective information carriers in the polar under-ice environment, and acoustic positioning and navigation technology is the main means for high-precision positioning and navigation of underwater vehicles. In this paper, the difficulties existing in the navigation tasks of polar under-ice vehicles are firstly analyzed, including the impact of high latitude effects, sustained low-temperature seawater, and large-scale ice coverage on the navigation and positioning technology of underwater vehicles. Then, the navigation methods of polar under-ice vehicles are reviewed and the current status and limitations of these methods in polar under-ice environments are analyzed. The research status of polar under-ice acoustic positioning and navigation technology is subsequently discussed, including main application cases and achieved performance outcomes both domestically and internationally. Finally, the challenges faced by polar under-ice acoustic positioning and navigation technology and its future development trends are summarized.

In low signal-to-noise ratio(SNR) situations, magnetic anomaly generated by magnetic target is usually buried in the magnetic noise, leading to a decline in the detection performance of traditional magnetic anomaly methods. To improve the detection performance of weak magnetic anomaly under low SNR, a weak magnetic anomaly detection method using ResNet-GRU network is presented in this paper. In this method, the Conv1D modules based on ResNet and the GRU modules are employed to extract multidimensional features from magnetic anomaly signals, enabling the detection of such signals through the fusion of multiple features. To train the model, a real-world magnetic anomaly dataset is constructed, consisting of 8646 positive samples and 8431 negative samples. Experimental results demonstrate that proposed method using ResNet-GRU has an accuracy of 90.39%, a precision of 91.33%, and an F₁ score of 90.18% on the test set, outperforming the performance of fully connected neural network model and one-dimensional convolutional neural network model. The proposed method has good detection performance of weak magnetic anomaly under low SNR.

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.

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.