4202 results about "Navigation system" patented technology

The invention discloses an AR navigation system and method based on a visual language model, and the method comprises the steps: firstly carrying out video data collection, and constructing a memory database; secondly, querying a navigation target most related to a natural language request of a user in the constructed memory database to obtain a current AR equipment pose and a target pose; and then solving the shortest path from the current pose to the target pose by using the current AR equipment pose and the target pose according to the point cloud map, and optimizing the path direction to finally obtain an optimized path. And finally, guiding a user to move along the planned path through view superposition path indication and voice prompt in AR equipment by utilizing the optimized path, and updating the point cloud map and the memory database. According to the invention, high-precision real-time positioning and sparse point cloud map construction can be realized only by camera input in indoor and outdoor complex environments with low GPS precision, accurate navigation is carried out, and the flexibility and intelligent level of navigation are improved.

The invention belongs to the technical field of medical robots, and particularly relates to a mammary gland puncture surgical robot navigation system and device based on multi-modal image fusion. The multi-mode image data are registered and fused, high-precision three-dimensional reconstruction of the mammary gland tissue is achieved, more visual and accurate surgical navigation information is provided for doctors, the position of the puncture needle is tracked in real time, space registration is conducted on the puncture needle and the image data, and therefore the precision and safety of the surgery are improved, and the operation efficiency is improved. Meanwhile, puncture force feedback information of a puncture needle in the needle moving process is collected in real time, the implementation effectiveness of a puncture navigation path is evaluated, when it is found that the puncture path has risks, dynamic adjustment is conducted immediately, a new puncture path bypassing an obstacle area and reaching a focus is planned again, and the risk of the puncture path is avoided. And if the safe path cannot be obtained, the system automatically stops needle insertion and gives an acousto-optic early warning prompt, so that the surgical risk caused by manual misoperation in the traditional surgery is avoided.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention provides a GNSS deception jamming detection method and system based on LSTM, and relates to the technical field of global satellite navigation systems. Acquiring a target signal through a receiver in the GNSS tracking loop model; acquiring output information of the tracking loop; selecting four types of basic SQM detection quantities of Ratio, Delta, ELP and PCS as a detection quantity set; generating a characteristic parameter set according to the output information and the detection quantity set; preprocessing the feature parameter set by using a sliding window technology to generate standardized data; and inputting the standardized data into a preset LSTM model to obtain a detection result corresponding to the target signal. The technical effect of effectively identifying the navigation deception is realized.

Systems and apparatus are provided for a high-power microwave sensor using an unmanned aerial vehicle. The unmanned aerial vehicle may include a central body, at least one electric motor, and a barometric pressure feedthrough. The central body may include a first enclosure housing a plurality of low voltage components, a voltage feedthrough connector, and a second enclosure housing a plurality of high voltage components. The low voltage components may include a first signal processing system, a first radio transceiver, a flight controller, a second signal processing system, a second radio transceiver, and a navigation system. The high voltage components may include a plurality of electronic speed controllers, a power distribution module, an input power interface, and a plurality of high power filters. The components of each enclosure may be segregated such that the high voltage components do not create electromagnetic interference with the low voltage components.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention discloses an electric vehicle charging station ordered charging method based on intelligent queuing, which comprises the following steps: acquiring a remote charging queue adding request, and extracting a vehicle identifier and current position information; a sliding window mechanism is adopted, a fixed time interval is set, charging station traffic flow and charging pile state change data are collected, and the estimated waiting time is used for updating queuing information; according to the distance between the vehicle and the charging station, the priority queue algorithm is adopted to allocate the charging pile position in combination with the predicted waiting time, and it is ensured that the closer vehicle preferentially obtains the charging opportunity; acquiring position information of a plurality of vehicles entering the range of the charging station at the same time, processing the requests by using a timestamp sorting algorithm based on vehicle entering time, and determining a guiding sequence of each vehicle; and generating a navigation path from the current position to the specified charging pile position according to the guiding sequence and the distributed charging pile position, and sending path information to the vehicle-mounted navigation system. The charging efficiency can be remarkably improved.

The invention discloses a transcranial electrical stimulation intracranial vector electric field detection device and method, and belongs to the transcranial electrical stimulation intracranial electric field detection technology, the device inputs an individual head model in a stimulation system, sets a target spot and calculates transcranial electrical stimulation parameters; the implantation position and angle of the vector detection electrode are planned through the navigation system, electrical stimulation is applied to a brain target area through the stimulation electrode, and intracranial electric signals are collected in real time through the vector detection electrode; performing high-precision amplification on the acquired weak signal, and converting the weak signal into a digital signal; the collected electric field signals are calculated and processed in real time, and electric field distribution generated by transcranial electrical stimulation in the brain is analyzed and visualized; according to the method, time domain and frequency domain analysis is carried out on electric signals before and after stimulation, the influence of stimulation on neural signals in a detection area is evaluated, and high-precision measurement of the intensity and direction of a three-dimensional electric field is achieved through the design of a high-precision and multi-layer vector detection electrode.

The invention discloses a lung puncture navigation system for pneumology department, which relates to the technical field of lung puncture navigation and comprises a data collection module, a data processing module, a calculation module and a navigation module. The method has the advantages that the focus position can be accurately positioned in real time by collecting the lung image data and the respiratory movement data of the patient and utilizing calculation of respiratory compensation, the need of adjusting the position of a puncture needle for multiple times is avoided, the optimal puncture path can be calculated, and the puncture accuracy is improved. Therefore, intelligent biopsy puncture path planning can be carried out according to specific conditions of the patient, biopsy risks are reduced, focus position changes caused by respiratory movement can be accurately compensated through real-time respiratory compensation application, and it is ensured that navigation precision and biopsy effects are not affected by the respiratory movement.

The invention discloses a graph optimization-based UWB / IMU / GNSS multi-unmanned vehicle cooperative positioning method, which comprises a global satellite navigation system GNSS, an ultra wide band (UWB) system, an inertial measurement unit IMU and a robot motion control system, and is characterized in that the UWB system comprises a UWB label and three UWB base stations, the UWB label is deployed on a target unmanned vehicle to be positioned, and the UWB label is deployed on the target unmanned vehicle to be positioned; and the three UWB base stations are respectively arranged on the other three unmanned vehicles as mobile base station unmanned vehicles. The GNSS module and the IMU module are deployed on the unmanned vehicle to serve as a movable UWB base station, and GNSS absolute position information and IMU relative motion information are fused by adopting an extended Kalman filtering algorithm; and according to the determined position of the unmanned vehicle in the movable base station, a distance observation value between the label and the base station is obtained based on a single-side bidirectional distance measurement method, a factor graph model fusing UWB distance measurement constraint and IMU motion constraint is constructed, and a factor graph optimization algorithm is adopted to optimize the position of the target unmanned vehicle. According to the method, the cost of a traditional fixed base station is reduced through the deployment of a mobile base station unmanned vehicle, and the precision and robustness of UWB system positioning in a complex environment are improved in combination with a multi-source sensor data fusion strategy.

The invention provides a shoulder joint replacement surgery navigation system and method, and relates to the technical field of computers, and the system comprises a controller and a navigation positioning device; the controller is in communication connection with the navigation positioning device; the controller comprises a preoperative planning module, a preoperative simulation module and an intra-operative execution module. According to the navigation system and method for the shoulder joint replacement surgery, by combining the preoperative medical image of the affected bone and the medical image of the healthy bone corresponding to the affected bone, the healthy form of the affected bone can be reflected more accurately under the condition that the affected bone has bone defect and / or abnormal form, and then prosthesis planning can be conducted based on the three-dimensional bone model of the affected bone, so that the patient experience is improved. The preoperative surgical planning is more objective and quantified, the movement range of the shoulder joint on the operative side of the patient after the prosthesis to be implanted is implanted into the affected bone can be simulated based on the parameter information of the prosthesis to be implanted, the accuracy and rationality of the prosthesis planning are further improved, and the accuracy and reliability of the orthopedic surgery are remarkably improved.

The invention relates to a navigation equipment health management method based on an SAITS algorithm and a digital twin platform. The method comprises the following steps: acquiring navigation equipment operation data; adopting an improved SAITS algorithm to carry out interpolation on missing data, and predicting to obtain future navigation equipment operation data; identifying current and future abnormal states of the navigation equipment by adopting a self-adaptive anomaly detection algorithm; constructing an equipment fault mode knowledge graph according to the identified equipment exception; simulating the operation of the navigation system by using a digital twin platform, verifying the confidence coefficient of equipment abnormity, and generating a health assessment report; if the confidence coefficient is not smaller than a confidence coefficient threshold value, generating an exception coping strategy in combination with an equipment fault mode knowledge graph; incremental learning or retraining is carried out on an adaptive anomaly detection algorithm to improve the detection accuracy. The method has remarkable advantages in the aspects of improving the navigation equipment management efficiency, reducing the fault risk, optimizing the maintenance cost and the like, and is particularly suitable for water transportation scenes with strict requirements on reliability and real-time performance.

The invention discloses a map navigation method and system based on integrated navigation, and particularly relates to the field of integrated navigation, comprising compass data acquisition, magnetic declination correction, map integration, navigation system data acquisition, navigation system data analysis and integrated navigation. According to the method, the precision and robustness of the navigation system are remarkably improved, different environments can be dynamically adapted, and the error influence is reduced; rich geographic information support is provided by presetting an electronic map and POI marks; the real-time navigation service provides visual road condition information and path planning, the user experience is enhanced, and on the whole, more accurate, reliable and convenient navigation service is provided for the user.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on inertial navigation, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance systems. The problem that an existing unmanned aerial vehicle is insufficient in obstacle avoidance capacity under the environment that GPS signals are weak, vision is limited or a sensor detection blind area exists is solved. Acquiring broadband high-frequency dynamic response data output by the inertial navigation system of the unmanned aerial vehicle in real time; establishing or acquiring a reference high-frequency dynamic response model when the unmanned aerial vehicle is in a specific flight state and a power system working mode and is not influenced by an external obstacle; the signal is dynamically responded according to high frequency; identifying whether a specific air medium disturbance echo feature formed by disturbance generated by motion of the unmanned aerial vehicle through an external obstacle exists or not; and finally, generating and executing an obstacle avoidance flight instruction according to the estimation information. The method does not depend on a traditional external environment sensor for initial detection, physical interaction is sensed through the INS, and the autonomous obstacle avoidance capacity and safety of the unmanned aerial vehicle in the complex and challenging environment are improved.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, and discloses an unmanned aerial vehicle indoor and outdoor seamless navigation method fusing Beidou and visual positioning. Comprising the following steps: S1, synchronously acquiring original observation data of a Beidou satellite navigation system of an unmanned aerial vehicle, an image sequence acquired by a visual sensor and inertial data of an inertial measurement unit; s2, processing the original observation data of the Beidou satellite navigation system to obtain the position of the unmanned aerial vehicle, according to the unmanned aerial vehicle indoor and outdoor seamless navigation method and system fusing Beidou and visual positioning, through a self-adaptive fusion filter module, the fusion weight of the unmanned aerial vehicle and visual positioning is dynamically adjusted according to the Beidou signal quality; smooth transition of indoor and outdoor navigation main sources is achieved, pose jump is effectively avoided, a tight coupling depth fusion algorithm is adopted, the precision and robustness of the system are improved, the continuous and stable flight capacity of the unmanned aerial vehicle in complex indoor and outdoor environments is ensured, and the application scene is expanded.

The invention relates to the technical field of communication engineering, in particular to a communication optical cable line intelligent inspection fault point rapid positioning method and device, and the method comprises the steps: obtaining optical cable line state data, constructing an intelligent fault detection model, recognizing an abnormal signal through wavelet transform and a threshold determination method, training an LSTM neural network in combination with historical fault data, and pre-judging a fault type. When a fault is detected, an unmanned aerial vehicle inspection unit is triggered, an inspection path is planned based on an improved Dijkstra algorithm according to the fault type and suspected fault area geographic information, an optical cable line is scanned and detected through a laser radar, physical fault features are recognized through a YOLOv5 algorithm, and geographic coordinates of a fault point are calibrated in combination with an inertial navigation system; and fusing sensor data and a detection result, evaluating a fault confidence level by adopting a DS evidence theory, and generating an alarm work order. Therefore, the problems of long positioning time consumption, high omission ratio, difficulty in distinguishing fault types and the like in the prior art are solved.

The invention discloses an underwater robot dynamic environment sensing and navigation device and method, and relates to the technical field of underwater robos.The device comprises an underwater robot, a sensor system, a data processing system, a control system, a navigation system and a power system, and the sensor system, the data processing system, the control system, the navigation system and the power system are arranged on the underwater robot; the surrounding environment can be comprehensively sensed in real time, and the optimal path is calculated through the path planning module according to the real-time environment data; performing obstacle detection according to the environment image to obtain an obstacle detection result; when an obstacle is detected, the control system sends out an obstacle avoidance decision instruction, after the path planning module receives the obstacle avoidance decision instruction, an adaptive algorithm is used for adjusting the optimal path according to the information of the obstacle to be detected, the adjusted path is obtained, and autonomous navigation for a complex underwater environment and autonomous navigation are achieved. And the autonomous navigation and operation capabilities of the underwater robot are obviously improved.

The invention relates to the technical field of robot navigation, and discloses a safety inspection robot multi-mode navigation system fused with subconscious learning. The system obtains a visual image, a depth distance, an inertial measurement unit and other multi-dimensional perception data of an inspection environment, performs environment feature extraction to generate an environment semantic topological graph, and constructs a dynamic navigation cost map and a preliminary navigation path according to the environment semantic topological graph. During path execution, the system detects abnormal navigation behaviors in real time, extracts the features of the abnormal navigation behaviors and obtains a first correction coefficient. And the system judges whether the implicit environment semantic identifier is identified, and if the implicit environment semantic identifier is identified, the corresponding path key node is taken as a semantic navigation reference point, a final navigation path is generated through fusion, and a time stamp is recorded. And if the time stamp exceeds the preset tolerance, extracting a corresponding second correction coefficient. And the system combines the basic navigation performance evaluation value, the first correction coefficient and the second correction coefficient to output a multi-modal navigation decision result.

The invention relates to the field of cardiology department operations through vascular intervention, and discloses an interventional operation navigation system based on multi-modal image fusion, which comprises a preoperative three-dimensional reconstruction and planning module, which is mainly used for generating a three-dimensional model of a heart through preoperative image data, and planning the position and section of an intraoperative ICE ultrasonic probe based on the model, the intraoperative multi-modal fusion navigation module is mainly used for fusing a three-dimensional model generated before an operation with an intraoperative real-time image and providing accurate navigation support for a doctor; and the dynamic feedback optimization module is mainly used for updating a dynamic track in real time and performing safety protection. According to the invention, through the preoperative three-dimensional reconstruction and virtual path planning module, in combination with the improved 3D U-Net network and the high-precision segmentation algorithm, a three-dimensional model containing key anatomical markers is generated, 20 groups of ICE probe alternative paths are planned, seamless connection of preoperative three-dimensional anatomical information and intraoperative real-time images is realized, doctors can quickly call pre-planned paths, and the accuracy of the ICE probe anatomy is improved. Adjusting time during operation is shortened, and operation efficiency is improved.

The present invention discloses a fire monitoring method and system based on three-dimensional laser scanning technology, which relates to the field of fire monitoring technology, including collecting a fire scene structure diagram for path planning, deploying sensors on a robot for real-time path navigation and obstacle avoidance; performing three-dimensional laser scanning and collecting data after reaching the target point, processing the collected data and performing three-dimensional modeling. The present invention combines three-dimensional laser scanning technology with a robot intelligent navigation system, utilizes high-precision three-dimensional point cloud data and sensor fusion technology, realizes comprehensive monitoring of the fire scene, and through the robot's autonomous path planning and obstacle avoidance, can dynamically collect data at the fire scene and perform three-dimensional modeling, analyze heat source distribution and smoke diffusion trajectory, and then predict the fire spread path, and generate a real-time updated three-dimensional fire scene map, which can provide efficient and accurate fire information in a complex environment, and provide an important basis for decision support and emergency rescue at the fire scene.

The invention provides a Kalman filtering navigation information fusion method based on intelligent optimization algorithm optimization, and relates to the technical field of integrated navigation, and the method comprises the following steps: obtaining inertial navigation data and auxiliary measurement data of a navigation target at a current moment, obtaining a historical state prediction result and a historical state covariance matrix at a previous moment adjacent to the current moment; based on the inertial navigation data and the auxiliary measurement data, performing adaptive search estimation on the measurement noise matrix of the navigation target to obtain an optimal measurement noise matrix of the navigation target; calculating Kalman gain according to the initial state covariance matrix and the optimal measurement noise matrix; and correcting the initial state prediction result according to the auxiliary measurement data and the Kalman gain to obtain a state prediction result of the navigation target at the current moment and a state covariance matrix corresponding to the state prediction result. The precision of the integrated navigation system can be improved under the condition that no additional sensor is added.

The invention provides a fruit stem clamping and shearing agricultural robot and a picking control method thereof.The robot comprises a control cabinet, a mechanical arm and a visual navigation system which are installed on a crawler-type chassis supporting platform, and an upper computer control system and a lower computer control system are installed in the control cabinet; and the tail end of the mechanical arm is provided with a movable end effector with an identifying and positioning system and a light supplementing device. The movable end effector can flexibly adapt to picking requirements and complex working conditions of different crops; the upper computer control system is combined with a depth camera and a laser radar to collect data, and a YOLOv8-MC model is utilized to perform identification and key point positioning on a target crop in an image, so that the crop identification precision in a complex environment is remarkably improved, the probability of missing collection and mistaken collection is reduced, and the damage to fruits is reduced; when the upper computer control system constructs a global map, depth correction and map optimization are carried out on illumination changes, and the path planning and operation accuracy of the robot is further improved.

The invention discloses an autonomous visual semantic navigation system and method based on interactive semantic mapping, a semantic scene graph is constructed by sensing the environment through a first visual angle of a robot and combining top-down environment semantic priori, and the method comprises regional spatial distribution identification, object attribute and layout analysis and room type clustering. And in combination with priori knowledge, complete scene representation is formed, the scene graph can be dynamically adapted and updated according to real-time observation of the robot, and the accuracy and timeliness of scene representation are ensured. Besides, a high-level and low-level AI agent module cooperation mechanism is adopted, and the high-level AI agent module is responsible for understanding and decomposing navigation tasks and distributing subtasks and supervising the execution process; the low-layer AI agent module is responsible for further dynamic decomposition and execution of the subtasks and feeds back execution results to the high-layer Agent, a complete task closed-loop monitoring mechanism is formed, and the execution efficiency and robustness of the navigation tasks are improved.

The invention relates to the technical field of ship navigation system safety, and provides a Beidou satellite navigation deception detection method and system based on multi-source information, and the method comprises the steps: obtaining Beidou satellite navigation data, AIS information, navigation radar data and sea chart data; performing denoising processing on the Beidou satellite navigation data, and performing space-time compensation on the AIS information; clutter suppression is carried out on the navigation radar data; calculating ship body position deviation, and verifying the ship body relative position according to the ship body position deviation; calculating the real position of the ship body and the Beidou positioning deviation, and verifying the absolute position of the ship body; performing hull course consistency verification; and performing Beidou satellite navigation deception detection according to the ship body relative position verification, the ship body absolute position verification and the ship body course consistency verification. According to the invention, multi-dimensional detection is carried out on key information such as the position and course of the ship, so that the probability that the ship falls into danger due to wrong navigation signals is greatly reduced, and the navigation safety of the ship is guaranteed.

The invention discloses a heterogeneous sensor fusion method and system for dynamic covariance optimization, and the method comprises the steps: constructing a multi-source heterogeneous sensor network which comprises an underwater inertial navigation unit, a Doppler velocity sensor, an ultra-short baseline positioning system, a submarine topography matching device and a geomagnetic gradient detector; the method comprises the following steps: designing a distributed integrated navigation system by adopting a hierarchical fusion architecture, establishing a sensor confidence coefficient dynamic evaluation model based on online estimated error covariance matrix eigenvalue analysis, carrying out state prediction on a local filter level by applying Kalman filtering, and then carrying out measurement updating by adopting a novel variational Bayesian algorithm; a sensor autonomous switching and adaptive weighted dual-mode adjustment mechanism is developed on the main filter level, and self-healing improvement of navigation precision in a non-stationary environment is realized, so that the problems of dynamic reliability evaluation and fusion efficiency optimization in underwater multi-sensor multi-source navigation are solved.

The invention relates to the technical field of unmanned systems, and discloses an unmanned ship directional operation system cooperating with unmanned aerial vehicle cruise, and the system comprises an unmanned aerial vehicle module which carries a high-precision sensor and is used for scanning a water area environment in real time and generating a high-precision map; the unmanned ship module is provided with a navigation system and an operation device and is used for executing water surface orientation operation according to the high-precision map; the cloud platform module is connected with the unmanned aerial vehicle module and the unmanned ship module through a wireless communication network and is used for data interaction, task scheduling and remote control; and the intelligent charging shore base is used for providing automatic charging support for the unmanned ship module. A high-precision water area map is generated in real time through the unmanned aerial vehicle module and is transmitted to the unmanned ship module, so that the unmanned ship can optimize the route and the obstacle avoidance operation according to the dynamically updated environment data, and the task execution precision is remarkably improved; therefore, the effectiveness of patrolling, cleaning and monitoring tasks is improved.

The technology disclosed teaches distributed networks and methods for cloud processing for global navigation satellite system (GNSS) interference data from a plurality of GNSS receivers to alert aircraft personnel to GNSS spoofing of aircraft guidance systems. The technology disclosed includes receiving at a cloud-based server, GNSS interference data from the plurality of GNSS receivers, analyzing the GNSS interference data upon receipt, identifying an interference event from the analyzed GNSS interference data, and in response to an identified interference event, the cloud-based server providing an alert to EFB tablet devices onboard the aircraft thereby causing notification of aircraft personnel of a potential spoofing event.