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.

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

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.

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

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

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.

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

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.

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

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

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

In complex underwater environments, multi-source autonomous navigation systems are often adversely affected by environmental interference, its navigation accuracy and robustness decreases. In response to the problem of degraded navigation performance in SINS/DVL/USBL integrated navigation systems under such conditions, a filtering algorithm based on a detectability quantification model is proposed. Firstly, error models are established for the SINS, DVL, and USBL subsystems, and their root mean square errors (RMSE) are calculated as quantitative indices of detectability. Then, an environmental interference factor is introduced to correct the multi-dimensional detectability model, which is constructed by considering coverage, accuracy, real-time performance, and data availability of the sensors. Based on the corrected model, an adaptive filtering algorithm is developed. Finally, the proposed algorithm is validated through test on the Yangtze River. Test results show that the proposed algorithm effectively mitigates the adverse effects of environmental interference on the SINS/DVL/USBL integrated navigation system, with RMSE reductions in the north, east, and vertical directions of 88.5%, 84.0%, and 97.4% compared to the traditional Kalman filtering algorithm, and reductions of 48.3%, 22.6%, and 18.6% compared to the maximum correntropy Kalman filtering algorithm.

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.

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

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

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.

Rotational modulation technology is a widely applied error self-compensation technique in inertial navigation systems. Ideal rotational modulation can suppress certain device error components in the orthogonal plane of the rotation axis. However, unavoidable control errors in IMU rotation affect modulation effectiveness. On the one hand, actual rotation control suffers from errors such as angle overshoot. On the other hand, the measurement errors inherent in feedback components such as gratings within the control loop also cause non-uniform rotational angular velocity errors. Based on the consideration of controller and actuator errors, a comprehensive control system model incorporating circular grating measurement errors is established, and the unique influence mechanism of non-uniform rotational angular velocity caused by the feedback control errors of the grating on high-precision azimuth alignment is quantitatively analyzed. Calculation and simulation results show that rotational angle overshoot caused by the controller and actuator has a minor effect on the rotational modulation performance. However, when the circular grating participates in the feedback loop for angle measurement, the resulting non-uniform rotational angular velocity leads to azimuth alignment errors, which cannot be ignored in high-precision inertial navigation systems.

When the existing inertial measurement unit works in the space-stabilized or strapdown working mode, the sensitive axis of quartz accelerometers cannot be aligned with the thrust vector direction. This not only degrades the output accuracy of quartz accelerometers affected by the cross-coupling quadratic term error, but also prevents gyro accelerometers to take advantage of its high overload accuracy under low axial overload conditions, meanwhile affects the navigation solution accuracy. Aiming at the above problems, a thrust vector tracking mode is proposed in this paper. The classical lead-lag control method is adopted to control the radial quartz accelerometers to the zero-overload direction, ensuring that the axial accelerometer tracks the thrust vector direction. This can eliminate the quadratic term measurement error of the quartz accelerometer. Experimental verification shows that the axial accelerometer can consistently track the overload direction, and the overload sensed by the radial accelerometer can be maintained at zero under the vector tracking mode. In addition, the high-precision inertial measurement unit is equipped with a gyro accelerometer. Considering the additional output error of the gyro accelerometer introduced by the base angular motion during the thrust vector tracking control process, an output error model is established, and the gain-scheduled linear quadratic regulator (GS-LQR) is adoped to optimize the control design of the thrust vector tracking process and suppress the output error caused by the base angular motion. Simulation results show that, compared with the implemented classical lead-lag vector tracking control method, the proposed method can effectively suppress the output error of the gyro accelerometer while maintaining an equivalent tracking speed.

GNSS spoofing attacks can enable attackers to covertly control autonomous vehicles, posing a serious threat to road traffic safety. Addressing limitations in existing detection methods, such as restricted application scenarios and insufficient real-time performance, a spoofing detection method based on LSTM and attention mechanisms is proposed. By constructing an LSTM-Attention model, the approach achieves multi-sensor data fusion and vehicle motion state estimation, thereby performing dead reckoning to generate a reference trajectory and employing trajectory consistency verification to detect spoofing. To mitigate the impact of cumulative errors on positioning accuracy, a sliding time window mechanism is introduced to correct errors within the window while comparing reference trajectory with GNSS navigation solutions. Experimental results demonstrate that the proposed method achieves 97.5% detection rate for abrupt spoofing attacks while maintaining low false alarm rates, outperforming existing baseline methods and meeting real-time detection requirements.

A novel guidance model is proposed for low dynamic unmanned vehicle(UV). Different from traditional guidance model whose state only contains position information, the novel model further incorporates velocity information. For the new guidance model, a new path following algorithm is designed utilizing a control approach based on Lyapunov stability theory. The algorithm consists of proportional + integral limits with saturation constraints, which is simple in form and easy to implement in engineering. Based on stability theory, it is proven that the constructed closed-loop system possesses global asymptotic stability in the presence of model uncertainties and wind field disturbances. To verify the effectiveness of the proposed algorithm, the algorithm is applied as an outer loop in the simulation of path following control of an airship, in which the simulation model is actually used in engineering. Simulation results show that the algorithm exhibits strong robustness and still demonstrates favorable path following performance even under strong wind conditions. Moreover, the guidance algorithm proposed in this paper has better convergence characteristics in simulation compared to an algorithm designed using traditional guidance model.

Maintenance operations in the “electromagnetic quiet zone” near radio telescopes demand high passive characteristics from navigation systems, rendering conventional navigation methods unsuitable. To address the limitations of traditional methods under passive and low-speed navigation scenarios, a polarization/geomagnetic hybrid orientation method based on BiTAN-UKF is proposed. This method first achieves preliminary nonlinear fusion of polarization navigation and geomagnetic navigation data via UKF, subsequently, by constructing a BiTAN model to capture data time-varying characteristics, it fully leverages the complementarity between the two to enhance orientation accuracy. Experimental results demonstrate that the proposed BiTAN-UKF algorithm achieves orientation accuracy of 0.375 4° in dynamic tests, providing an effective solution for passive navigation requirements in the “electromagnetic quiet zone”.

Redundant sensor configurations can effectively improve the reliability and navigation accuracy of inertial navigation systems(INS). Research on their calibration helps to enhance the actual operational accuracy of inertial measurement unit(IMU). Taking a decuple redundant dual-axis IMU as an example, a universal position arrangement is designed through an internal dual-axis rotation mechanism, the self-calibration consistency of the IMU in different orientations is achieved. Innovatively, the sensors are divided into three groups for system-level self-calibration. An error model for the installation of non-orthogonal sensors is established, along with navigation equations, navigation error equations, and Kalman filtering model. Based on a universal system-level calibration algorithm, a coordinate transformation module is added to calibrate all sensors. Simulation and experiment results show that both the non-orthogonal and orthogonal sensors are calibrated correctly. The calibration algorithm proposed in this paper is clear, easy to implement, and has high engineering application value.

In autonomous driving and intelligent transportation systems, visual localization estimates vehicle pose by recognizing environmental landmarks using onboard vision sensors, while cooperative localization enhances positioning continuity and reliability in landmark-sparse scenarios through inter-vehicle information sharing. To address the challenge of global navigation satellite system(GNSS) denials, a cooperative vision-based localization analysis framework tailored for vehicular networks is proposed in this paper, and a unified stochastic geometry-based modeling and performance evaluation system is established. The spatial distribution of environmental landmarks is modeled via a homogeneous Poisson point process(HPPP), and vehicle locations are characterized by a Poisson line Cox process(PLCP). On this basis, the single-vehicle visual localization probability is derived, and a V2V-based cooperative localization mechanism is introduced when visual perception fails, incorporating Nakagami-m fading channels to obtain a closed-form expression for the cooperative localization probability. Simulation results demonstrate that the theoretical analyses match the Monte Carlo simulations with a mean square error below 1%, validating the accuracy of the proposed model. The framework reveals the coupled effects of landmark distribution, channel fading, and vehicle layout, thereby providing theoretical guidance for performance evaluation and parameter optimization of cooperative vision-based localization.

High-precision time and frequency standards are critical for time synchronization and navigation/positioning accuracy in key fields such as global navigation satellite systems (GNSS), transportation systems, power systems, and network systems. Optical atomic clocks exhibit uncertainty metrics two orders of magnitude higher than the current primary standard, the cesium fountain clock, and their adoption for redefining the SI second has been formally proposed. The ytterbium ion optical clock, possessing two clock transition spectrums both adopted as secondary representations of the second, is one of the leading candidate ion systems for the future definition of the second. The ytterbium ion system has significant advantages, including high sensitivity to fundamental physical constants, a high clock transition quality factor, and relatively simplified laser requirements. These characteristics confer substantial potential in fundamental physics research, performance metric enhancement, and engineering applications, gaining widespread attention from research institutions worldwide. This article describes the operating principle of the ytterbium ion optical clock, reviews the key physical effects that constrain the performance of the system and the corresponding suppression methods, summarizes research progresses in fundamental physical exploration, breakthroughs in core performance metrics, and engineering technologies, and discusses prospects for its future development.

The function of capacitance detection circuit of micro electro mechanical system gyroscope is to convert the change of gyro’s sensitive capacitance into voltage that can be processed by digital system. Its performance is one of the important factors affecting gyro accuracy. In order to realize low noise and high performance capacitor-to-voltage circuit, a low noise capacitance detection circuit based on diode envelope demodulation is proposed in this paper. The core circuit consists of a charge sensitive amplifier and a diode envelope demodulation circuit. By analyzing the demodulation mechanism, the filter capacitor resistance and modulation signal range which do not significantly affect the demodulation efficiency are given. Then the noise model is established, the dominant noise parameters are defined, and the noise variation under different parameters is analyzed, in order to optimize that noise of the circuit. Experimental results show that the critical value of the modulated signal is 1.064 V, which is slightly lower than the theoretical value 1.201 V. The error comes from not considering the diode nonlinearity and the harmonic distortion of the readout signal within the critical value does not exceed 0.67%. At the same time, the measured noise voltage spectral density at the gyro operating frequency is -133.6dBV/Hz^1/2, which is in good agreement with the theoretical calculation result.

Regarding the fact that the circularly distributed bias error of HRGs is sensitive to mechanical environments and difficult to test rapidly, the error’s mechanism and manifestation are analysed, the testing method and measurement errors based on acceleration excitation from centrifuges are discussed, and a rapid identification method for acceleration-dependent circularly distributed bias error of HRGs based on driving vibration mode precession is proposed. By driving vibration mode precession in centrifuge test, the circularly distributed bias error under different accelerations is measured without gyro rotation, and the acceleration-dependent error coefficients are obtained by fitting. Experiment results show that this method enables rapid and accurate identification of acceleration-dependent circularly distributed bias error in HRGs, effectively evaluating HRG performance accuracy.

The rapid expansion of smart wearable device market has intensified the demand for pedestrian positioning. In GNSS-denied environments, existing pedestrian dead reckoning(PDR) technologies are constrained by the strict requirement for rigid attachment between the device and the human body, leading to unstable heading estimation. Current alternative solutions also face limitations, struggling to satisfy consumer-grade requirements for convenience and positioning performance. To address these issues, a multi-device collaborative dead reckoning method is proposed in this paper. Based on the hypothesis of average heading consistency across gait cycles for different body parts during normal walking, the method fuses heading data from head-worn, handheld, and wrist-worn nodes. It dynamically detects changes in device mounting modes, performs mounting angle compensation and transfer, and suppresses heading divergence caused by mode transitions. Experimental results over a 400 m walk show that the collaborative positioning accuracy improved by 58.62%, 64.08%, and 42.80% compared to individual wrist-worn, handheld, and head-worn nodes, while heading estimation accuracy also improved by 65.13%, 60.63%, and 43.40%, respectively. This approach offers advantages in low cost and universality, providing a new perspective for indoor navigation on consumer-grade wearable devices.

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.

Though satellite positioning, inertial positioning, wireless positioning and other technologies have been deeply penetrated into the fields of fire rescue, geological exploration, military equipment and so on, the problem of autonomous localization for personnel in underground and sheltered spaces has not yet been effectively solved. Inspired by the indoor localization of Wi-Fi, an autonomous localization method based on artificial geoelectric field for underground and sheltered space is proposed based on the fully explored geoelectric field distribution characteristics. This method does not require any auxiliary facilities to be set up in the underground and sheltered space, and constructs an artificial geoelectric field by injecting electric current into the earth, realizes the estimation of the distance from the detection point to the electric current injection point by detecting the electric field strength information of the geoelectric field. Then, this method realizes the accurate acquisition of personnel location information by fusing the distance information and coordinate information of multiple injection points based on the multilateral optimization positioning method. A geoelectric field positioning validation experiment with a range of 150 m×80 m is carried out in the field site, and the experimental results show that the localization method based on artificial geoelectric field can effectively obtain the personnel location information, with an average positioning error of 7.829 m and a single-point positioning accuracy of up to 1.352 m. The localization method based on artificial geoelectric field is expected to solve the problem of autonomous localization for personnel in underground and sheltered space environments, which has important theoretical research significance and engineering application value.

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.

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.

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.

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.

The marine gravimeter for unmanned small vessels must not only posses excellent dynamic adaptability but also employ a gravity anomaly extraction algorithm capable of effectively filtering non-periodic motion acceleration interference. To addressing these challenges, a gravity data processing method combining forward-backward Kalman filtering with FIR low-pass filtering based on a free-space gravity anomaly state-space model is proposed. Marine gravity measurement experiments are conducted using a small-sized strapdown marine gravimeter with high dynamic adaptability mounted on an unmanned small boat. The test data processing results indicate that compared with direct low-pass filtering method, the combined forward-backward Kalman filtering with FIR low-pass filtering improves accuracy by 48%; compared with forward-backward Kalman filtering method, the accuracy improves by 49%; and compared with traditional Kalman filtering, the accuracy improves by 79%.

With the growing demand for high-precision north-finding capability of weapon systems in modern warfare, the requirement for high-performance, integrated gyroscopic north finders has become increasingly urgent. The micro-hemispherical resonant gyroscope (μHRG),with its advantages of compact size, low cost, and excellent structural symmetry, has emerged as an ideal choice for developing compact, miniaturized north-finding systems. Nevertheless, its bias error severely restricts the overall accuracy of north-finding systems. To mitigate the impact of bias error, the influence mechanism of μHRG bias error on north-finding system is firstly investigated, and a north-finding accuracy model is established. Subsequently, a multi-position harmonic error compensation method is proposed according to the bias error characteristics of μHRG. Finally, the proposed method is experimentally validated. North-finding experiment results demonstrate thatthe μHRG-based north-finding system achieves an accuracy of 0.168° within 5 min using the multi-position harmonic error compensationmethod, which validates the effectiveness of the proposed method and provides strong technical support for overcoming performance limitations in μHRG-based north-finding.

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.

The traditional three-axis inertial navigation system generally limits the rotational range of the inner frame axis and lacks consideration of the stability for the servo loop caused by the cross-coupling of the frame moment of inertia. Aiming at the cross-coupling problem of the frame moment of inertia caused by structural changes during the frame rotation process of three-axis inertial navigation system, a frame moment of inertia decoupling and compensation method based on frequency-domain series compensator is proposed. Firstly, the decoupling position of the frame moment of inertia is determined through the three-axis inertial navigation motion relationship model. Secondly, the decoupling matrix of the frame moment of inertia based on the mechanical model is obtained by using the series compensator decoupling method. Furthermore, in order to avoid the problem of inaccurate moment of inertia testing, the gain variation is numerically calibrated through test to obtain a fitting function with the inner frame axis angle as the independent variable and the gain variation value as the dependent variable, which adaptively compensates for the gain variation of the servo loop. Finally, the adaptive compensation method is verified through simulation and test. The test results show that after compensating for the frame moment of inertia, the amplitude margin at 70° of the inner frame axis angle increases from 4.2 dB to 7.7 dB, meeting the basic performance requirement of an amplitude margin of 6 dB. It realizes the compensation for the gain value variation of the multi-input multi-output servo loop and the compensation for the cross-coupling torque between each frame axis.

In indoor or satellite environments-denied, the foot-mounted inertial pedestrian dead reckoning(PDR) based on zero-velocity update(ZUPT) is the mainstream technology for pedestrian autonomous positioning. However, when pedestrians move at a constant speed with a moving carrier (such as a box elevator), due to the lack of obvious inertial changes, the traditional ZUPT algorithm may misjudge that the pedestrian is in a zero-velocity state(“pseudo-stationary”), leading to significant positioning drift. To address this key issue, an inertial PDR positioning method resistant to carrier motion is proposed in this paper. Taking the typical indoor carrier, the box elevator, as an example, a detection algorithm for the carrier’s motion states (acceleration, uniform speed, and deceleration) based on the “trapezoidal” fluctuation characteristics of the vertical specific force in the navigation frame (n-frame) is innovatively proposed. Furthermore, motion constraint models based on the rated speed in the elevator industry specifications and the inertial integral speed are respectively constructed and effectively into the extended Kalman filtering framework to accurately constrain the divergence of the positioning error for pedestrians during the carrier’s uniform motion. In the tests in an actual office with building elevator scenarios, the proposed method significantly outperforms the traditional ZUPT and barometric fusion methods. In the elevator-riding test on an eight-story building(with a vertical height difference of 29.12 m), the height estimation error is effectively controlled at around 1 m, which verifies the effectiveness of the method.