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

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

Inertial integrated navigation synthesizes information from various sensors to deliver high-precision navigation and positioning capabilities for vehicles, including aircraft, maritime vessels, automobiles and so on. In response to the challenges associated with inadequate accuracy and robustness of the SINS/GPS integrated navigation system, particularly in scenarios involving large misalignment angles and system faults, a fault-tolerant integrated navigation method utilizing the adaptive federated strong tracking unscented Kalman filter(AFSTUKF) is proposed in this paper. Firstly, an integrated navigation system integrating SINS, GPS, polarization and geomagnetic information is constructed. Secondly, the AFSTUKF is designed with a feedback reset mechanism and incorporates an adaptive fading factor along with a strategy for updating measurement noise covariance, which collectively aim to bolster estimation accuracy and robustness amidst system uncertainties and noise. Furthermore, an adaptive information sharing factor based on the Tukey function is introduced to improve the stability and overall performance of the federated filter. Experimental findings indicate an average enhancement of over 29% in estimation accuracy when compared to conventional methods. The integration of polarization and geomagnetic data contributes to an additional increase in attitude estimation accuracy of nearly 40%, thereby significantly augmenting the robustness and precision of the navigation system.

To address the precise calibration challenge for high-precision inertial navigation systems, a system-level calibration method for three-autonomy inertial measurement units based on factor graph optimization is proposed. By accurately modeling high-precision IMUs, a pre-integration model is derived that takes into account Earth’s rotation, coning angular velocity, inertial device bias, scale factor, and installation error parameters. By constructing a residual model, a complete factor graph model suitable for this system is designed, and a sliding window optimization strategy is employed to iteratively solve for the calibration parameters. Simulation results show that this method is closer to the set values compared to the Kalman filter calibration method. Experimental verification using online self-calibration data from the three-autonomy IMU prototype demonstrates that the proposed method achieves performance comparable to that obtained via Kalman filter calibration. Pure inertial navigation experiments indicate that,compared with Kalman filter, parameters calibrated using this method reduce maximum eastward and northward position errors by 25.85% and 15.37%, respectively. Experimental findings confirm this method provides an effective solution for self-calibration of three-autonomy inertial measurement units, offering significant implications for expanding factor graph optimization techniques in calibration applications.

The accurate positioning of underground pipelines plays a crucial role in urban planning, construction, and management. A positioning algorithm based on IMU/odometer odometer is proposed to measure the trajectory of underground pipelines. This algorithm utilizes dead reckoning and incorporates the position information of the pipeline’s start and end points for trajectory correction. It operates on the basic principles of IMU/odometer dead reckoning and applies a compass leveling method to adjust the horizontal angle. By using the known coordinates of the pipeline’s starting and ending positions, the calculated trajectory curve is rotated and scaled, enabling correction and compensation of trajectory errors. Finally, the measured trajectory is obtained through weighted averaging. Experiment results show that this algorithm achieves a measurement accuracy of 0.3% for a 101.8 m pipeline, providing three-dimensional coordinate accurate measurements of underground pipelines.

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

Nuclear magnetic resonance gyroscopes measure the carrier’s angular rate by probing the Larmor precession frequency shift of noble gas within a stable magnetic field. The noble gas polarization is affected by the number density of alkali atoms. There is an optimal number density that maximizes the noble gas polarization. Traditional methods (e.g., empirical formulas, polarimetric detection) are unsuitable for miniaturized vapor cells. Thus, an in-situ measurement method for number density based on spectral absorption is proposed in this paper. Considering the effect of power broadening, laser intensity and detuning are adjusted to obtain the optical depth of alkali atoms firstly, and then fit the optical depth with a fitting function whose absorption cross-section is a multi-peak Lorentzian, to derive the number density. Experimental results demonstrate that the relative transition strength, full width at half maximum, transition frequency, and hyperfine energy level spacing obtained from the fitting are all consistent with theoretical values. It verifies the effectiveness of the proposed method. The method accomplishes in-situ measurement via laser, exhibiting high accuracy and strong reliability, and effectively supports the calibration of the gyroscope’s optimal operating state.

Temperature stability and uniformity are critical determinants of precision in liquid floated gyroscopes, where efficient heat transfer from heating pads enables precision temperature control. The adhesive-bonded structure between the heating pad and housing assembly serves as the primary thermal pathway, whose heat transfer characteristics directly govern the temperature gradient distribution and thermal fluctuations within the instrument. A three-dimensional finite element thermal conduction model integrating the heating pad, adhesive layer, and housing components is developed. Heat transfer performance is characterized by spatial uniformity of the housing temperature field and dynamic response rate of temperature-sensing points under cyclic power variations. The influence of adhesive layer thickness, adhesive defects, and heating pad resistance wire distribution on the overall thermal resistance, temperature distribution, and transient response characteristics of the bonded structure is studied, and the effectiveness of the model is verified through temperature rise tests. The results show that the circumferential temperature variation of the housing assembly is lower than the axial variation under all interface conditions examined, with the circumferential temperature range being less than 14% of the axial range. For every 0.02 mm increase in adhesive layer thickness, the thermal resistance of the bonded structure increases by approximately 30%, and the response rate of the temperature measurement point to changes in heating power progressively decreases. The defects in the adhesive layer cause heat concentration, thereby preventing the gap distribution of the heating pad resistance wire from effectively reducing the axial temperature gradient. This work establishes design guidelines for optimizing gyroscope heating pad bonding processes, significantly enhancing inertial navigation accuracy through thermal management.

This study improves the performance of BDS common-view technology through a dual-path approach of “model optimization and terminal design” to meet the urgent demand for nanosecond-level high-precision time-frequency synchronization in distribution areas of power systems. At the algorithm level, an 18-parameter broadcast ephemeris model is adopted to reduce the space signal ranging error of satellite orbit determination by approximately 0.1 m, which is slightly improved compared with the 16-parameter model. At the terminal level, the integration scheme of coherent population trapping (CPT) atomic clock disciplining and system-on-chip (SoC) is utilized to achieve a 24-hour frequency accuracy of 9.16×10^-13, timing accuracy of ±10 ns, and a volume of only 0.71 L. Experimental results show that the common-view differential mechanism effectively suppresses ionospheric delay errors. The peak to peak error of the one-pulse-per-second(1 PPS) is controlled within ±10 ns, and the holdover performance achieved a time drift of 0.385 μs/d. The research results form a complete solution covering terminals to networking, breaking through the limitations of large volume and high cost of traditional equipment.

Significant viewpoint discrepancies exist between large tilt-angle UAV images and nadir view satellite maps. In the large tilt-angle UAV-satellite matching scenario, existing algorithms exhibit weak feature representation in texture-less regions, low localization precision, and insufficient robustness. To address these problems, a UAV-satellite image matching algorithm that integrates efficient channel attention with dual-stream spatial feature enhancement architecture is proposed. First, an attention mechanism is introduced into the encoder to strengthen multi-channel information interaction, thereby enhancing the network’s representation capability for blurred distant views and weak-texture regions. Second, a dual-stream spatial feature enhancement architecture is designed, which projects and fuses robust top-view features from the nadir perspective into the original oblique image via geometric transformations, achieving cross-view spatial feature enhancement. Experimental results on a multi-tilt-angle UAV-satellite image matching dataset demonstrate that the proposed algorithm achieves a localization accuracy of 57.40% within a 50 m error range, marking an 8.35% improvement over mainstream matching methods. This approach effectively resolves the problems of feature instability in weak-texture regions and information inconsistency across perspectives.

To address the issue that traditional direction of arrival (DOA) estimation methods suffer a significant decline in edge-angle estimation accuracy under low signal-to-noise ratio (SNR) and moving target scenarios, a two-stage intelligent direction-finding method is proposed. The proposed method first utilizes a residual convolutional neural network to perform end-to-end denoising of the array covariance matrix, and then employs a complex Transformer to extract sequence features to achieve DOA estimation of moving radiation sources. Experimental results indicate that, under low SNR and dynamic non-stationary scenarios, the proposed method reduces the root mean square error by an average of 63% compared with traditional algorithms.