185 results about "GPS signals" patented technology

The invention discloses an intelligent surveying and mapping system for catenary equipment in a tunnel, which relates to the technical field of catenary engineering detection and comprises a multi-source data acquisition module, a multi-source data fusion module, an identification and parameter calculation module, a modeling and interaction module and a data management and application output module. According to the invention, by deploying the multi-dimensional sensor, the limitation of traditional manual inspection or dependence of a single sensor is broken through aiming at the environments of insufficient illumination, high dust concentration and the like in the tunnel, the laser scanner can penetrate through the dust to obtain the three-dimensional geometric framework of the equipment, and the Beidou positioning assembly is bound with the collection action in real time; the problem of positioning deviation caused by weak GPS signals in the tunnel is solved, the environment sensor group monitors parameters such as temperature, humidity and illumination in real time, and through a dynamic adaptation fusion coefficient formula, sensor data weight is calculated, original coordinate deviation is corrected, a four-dimensional fusion data set is constructed, and errors caused by environment interference or data isolation in a traditional method are solved.

The invention relates to the technical field of GPS interference source monitoring and troubleshooting, in particular to a civil aviation GPS interference source monitoring and troubleshooting method and system. The method comprises the following steps: acquiring track position data, attitude parameter data, GPS signal receiving data and interference detection performance parameters of a plurality of aircrafts, and establishing aircraft nodes and interference nodes; generating a common edge between any two aircraft nodes, generating a corresponding special edge between each interference node and the aircraft node within a preset influence radius, and constructing a space-time interference propagation topological graph at each moment; calculating an interference behavior characteristic parameter to obtain an interference behavior characteristic parameter vector, and calculating and outputting an interference behavior type identifier and an interference evolution trend vector corresponding to each interference node by using an interference behavior evolution trend inversion algorithm; and predicting an early warning interference source and generating an interference early warning result. According to the invention, real-time positioning, behavior identification and future influence range prediction of multiple types of interference sources in the civil aviation airspace are realized.

The invention discloses an indoor and outdoor navigation method, device and equipment, and relates to the technical field of computers. The method is applied to an edge device, the edge device is carried on a robot, and the method comprises the steps that after it is detected that the robot is started outdoors, a first planned path is generated based on the initial position of the robot, an outdoor offline map and the indoor entrance position; when it is detected that the intensity of the entrance marker and the GPS signal at the indoor entrance position is smaller than an intensity threshold value, an indoor navigation mode is triggered; constructing an indoor coordinate system by taking the indoor entrance position as an original point; indoor motion data of the robot and image data of the environment where the robot is located are obtained through an inertial measurement unit and image acquisition equipment; determining a current indoor coordinate of the robot based on the indoor map, the indoor motion data and the image data; and generating a second planning path based on the indoor coordinate system, the current indoor coordinate and the target task point. According to the scheme, the navigation efficiency can be improved.

The invention discloses a position detection and cheating device and method for an unmanned aerial vehicle, and the device integrates a position information obtaining module, a GNSS signal receiving module, and a GNSS cheating signal generating and transmitting module, obtains the position of the unmanned aerial vehicle through a Wi-Fi network card or a Bluetooth receiver, and a USRP B210 device and related software collects and processes a GNSS signal and generates a cheating signal. The method comprises three processes of unmanned aerial vehicle position information detection, real GPS signal receiving and position deception signal generation, and the safety of the unmanned aerial vehicle is evaluated through multi-link analysis, signal receiving and simulation signal generation and emission. The unmanned aerial vehicle position and the GPS signal can be acquired in real time, the authenticity of the deception signal is improved, and the test effectiveness is enhanced; and the device has the advantages of high simulation degree, high precision, simplicity and convenience in operation, low cost and the like, and an efficient scheme is provided for unmanned aerial vehicle position safety testing and anti-deception capability evaluation.

The invention relates to the technical field of satellite navigation and wireless communication, and discloses an indoor positioning method and system based on feeder multiplexing Beidou double-frequency, and the method comprises the steps: receiving outdoor B1I and B2a signals through a high-gain Beidou double-frequency receiving antenna, carrying out the frequency separation and signal conditioning through a double-frequency separation filter, and carrying out the positioning of the B1I and B2a signals; and transmitting the B1I signal to an indoor distributed antenna for broadcasting through the existing communication feeder network, and transmitting the B2a signal to a central directional antenna to establish a time synchronization reference. A mobile terminal receives a dual-frequency signal, capturing and tracking are carried out through digital signal processing, and pseudo-range and carrier phase observation values are extracted. And performing pairing processing on observation data of the same antenna, calculating a dual-frequency pseudo-range difference value, eliminating system errors, and performing position calculation by adopting a weighted least square method and Kalman filtering. The method solves the problem that the traditional GPS signal cannot cover the indoor environment, and has a good application prospect in the scenes of underground commercial complexes, tunnels, parking lots and the like.

The embodiment of the invention provides a new energy inspection robot node positioning method and system, and the method comprises the steps: firstly carrying out the dual confidence degree dynamic evaluation of GPS and SLAM data, and quantifying the respective reliability in real time; and then, inputting the confidence into a fusion state machine decision engine, so that the fusion state machine decision engine can intelligently decide that the system should be in an optimal fusion state such as GPS leading, pure SLAM or GPS recovery transition. Finally, the state decision directly guides the adaptive adjustment of the measurement covariance matrix in the extended Kalman filtering, thereby dynamically adjusting the weights of different sensors at the algorithm level. According to the closed-loop design from state perception to strategy decision and then to parameter self-adaption, positioning impact caused by GPS signal sudden change can be effectively restrained, smooth transition during signal recovery is achieved, and therefore it is guaranteed that a positioning result has high precision, high continuity and high accuracy under various working conditions.

The invention belongs to the related technical field of construction safety patrol, and particularly relates to an unmanned aerial vehicle safety patrol method for a dianthus chinensis landform mountain construction area, which comprises the following steps: S1, preparation work before patrol: inspecting patrol equipment and confirming patrol environment conditions on the same day; wherein inspection of the patrol equipment comprises inspection of an unmanned aerial vehicle body and all parts, inspection of battery conditions, inspection of high-definition camera equipment and sensing equipment and inspection of communication and positioning conditions. According to the invention, the unmanned aerial vehicle equipment and the high-definition monitoring camera equipment are checked before the unmanned aerial vehicle carries out safety patrol, so that the situation that the safety patrol is difficult to complete smoothly due to equipment problems in the safety patrol process is avoided to a great extent; and meanwhile, the real scene information and the position information acquired in the safety patrol process can be ensured to be smoothly received by the monitored personnel by checking the transmission communication signal and the GPS signal before safety patrol.

The invention discloses a sparse GPS map matching method based on confidence and intelligent fragment retrieval, relates to the technical field of map data processing, and realizes real-time adaptive adjustment of weights along with scenes by constructing a three-dimensional dynamic confidence model fusing road complexity, track point quality and historical matching popularity; by constructing a quaternary composite trigger logic of a confidence threshold value + track point spacing + road section topology complexity + GPS signal quality, false triggering or missing triggering caused by a single threshold value is avoided; data reliability is complemented by establishing a credibility scoring system (fusing matching success rate, use frequency and time freshness) of historical track fragments and performing two-dimensional sorting according to similarity and credibility during retrieval; the method does not need multi-sensor fusion and GPU computing power support, only depends on GPS single-sensor data, realizes balance of high-precision completion and low-redundancy calculation through a dynamic strategy, and adapts to resource-limited scenes such as vehicle-mounted edge equipment.

The invention discloses a GPS-free unmanned aerial vehicle autonomous navigation method based on multi-modal perception and reinforcement learning, and aims to solve the problem of obstacle avoidance failure caused by insufficient perceptibility of a transparent or reflective material. The method comprises the following steps: constructing a dual-channel occupancy graph containing an entity occupancy probability and a permeable layer probability, estimating a local plane normal in a suspected area and establishing an anisotropic safety barrier aligned with the normal, setting normal and tangential speed upper limits, combining safety reinforcement learning active verification and sensor parameter self-adaption, and cooperating with Bayesian updating and local path planning to obtain an anisotropic safety barrier. Reliable identification and safe detour of transparent or reflective obstacles are realized, the collision risk is reduced, and the robustness and stability of autonomous navigation in an environment without global satellite positioning signals are improved.

The invention provides a wireless distributed data synchronous acquisition method and system based on GPS time service. Each acquisition unit receives a GPS signal, acquires a GPS pulse per second signal and real triggering time thereof, and calculates and calibrates the drift rate of a local clock, so that the local clock is homologous with a GPS clock; after the fixed delay is calibrated, structural response data are collected, an original timestamp is generated, and a calibration timestamp is obtained by combining the fixed delay and drift rate correction. And the central computer generates a global unified time axis on the basis of the calibration timestamp, maps the data of each acquisition unit through linear interpolation and outputs a synchronization result. The system does not need wiring, is high in synchronization precision, and is suitable for structural health monitoring of large complex projects such as super high-rise buildings.

The invention provides a multi-source heterogeneous sensor adaptive fusion positioning and mapping method and device, and relates to the technical field of automatic driving and mobile robots. The method comprises the following steps: for a mobile robot in an indoor environment, acquiring sensor data through a sensor data acquisition module, inputting the sensor data into a front-end data processing module to obtain processed sensor data, and inputting the processed sensor data into an environment scene recognition module to obtain an environment scene type recognition result; and inputting the result into a back-end state estimation and optimization module to obtain a map construction and pose estimation result. Through innovative front-end feature extraction, a rear-end optimization mechanism and a multi-sensor fusion strategy, the problems in the prior art are solved. By introducing a low-cost magnetometer, the robustness of the system in a GPS signal missing or complex electromagnetic environment is enhanced, meanwhile, the calculation efficiency and precision in different environments are improved through a self-adaptive optimization mechanism, and finally, high-precision and high-robustness autonomous positioning and mapping functions are achieved.

The invention discloses a complex sea condition unmanned ship path planning method and system based on a cost grid map. The method and system are suitable for water area navigation tasks of complex environments including ocean currents, wind wave interference, unstable GPS signals and the like. According to the method, firstly, a navigation area is dispersed into a two-dimensional grid map, multi-source environment information such as ocean current velocity, wind speed, GPS signal intensity and visibility is fused, and a comprehensive path cost function is constructed; on the basis, the motion state of the unmanned ship is modeled into a triple containing the position and the course, and course change cost is introduced to represent the steering cost of the unmanned ship. In the path search process, a weighted A * algorithm is adopted, and a multi-target path cost function and a heuristic estimation function are combined to realize efficient path search. According to the method, high-risk areas can be effectively avoided, the steering frequency is reduced, the energy consumption of the unmanned ship is reduced, the safety and feasibility of path planning are improved, and the method has good engineering applicability and robustness.

The invention discloses an unmanned vehicle positioning method and system based on multi-sensor fusion, and belongs to the technical field of unmanned driving and robots. The method mainly comprises the following steps: fusing laser radar and IMU data through an LIO module, generating a laser radar odometer, and aligning the laser radar odometer with GPS data through SVD decomposition so as to deduce latitude and longitude information in a GPS-free environment; a deep learning model is introduced to enhance the robustness of label detection through an improved AprilTag visual module, and the precise longitude and latitude of a camera are calculated through nonlinear optimization in combination with IMU attitude data and the prior position of a label; and finally, according to error characteristics of the two modules, performing dynamic weighted fusion on positioning results, and performing optimization by using an error state Kalman filter (ESKF) to obtain a smooth and high-precision final pose. According to the invention, the problem of continuous and accurate positioning of the unmanned vehicle in a GPS signal interruption or complex environment is effectively solved, and the reliability and robustness of the system are improved.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The invention discloses an unmanned aerial vehicle autonomous homeward voyage method and system based on visual inertia fusion and path backtracking, and the method comprises the steps: obtaining data of multiple sensors, carrying out the fusion, obtaining the information of an unmanned aerial vehicle and environment, and enabling the information of the unmanned aerial vehicle to comprise the state space, time and track of the unmanned aerial vehicle; the environment information comprises an environment map and obstacle information; constructing a return flight decision model of unmanned aerial vehicle return flight multi-target weighted sampling distribution based on the unmanned aerial vehicle information and the environment information; the homeward voyage decision model is a hybrid model; solving and optimizing the homeward voyage decision model of unmanned aerial vehicle homeward voyage multi-target weighted sampling distribution to obtain an optimal homeward voyage path; and the unmanned aerial vehicle executes a return flight task based on the optimal return flight path. According to the method, the homeward voyage opportunity is autonomously judged, the homeward voyage route is planned and dynamically adjusted, dependence on external signals and manual operation is reduced, the method is particularly suitable for scenes where communication is interfered or GPS signals are weak, and the intelligence and automation level is remarkably improved.

The present invention relates to a technology related to a VILS-based test for an autonomous vehicle capable of performing a VIL test in an actual road environment by fusing actual surrounding vehicle information and surrounding vehicle information generated in a virtual environment. This VILS-based test system for an autonomous vehicle according to an embodiment may comprise: a sensor-fused precise positioning unit for estimating the current location of the vehicle on the basis of information collected from a GPS signal reception device, an in-vehicle sensor, and a LiDAR sensor; and an integrated processing unit for integrating real-world data collected from the sensors of the autonomous vehicle into a virtual environment of an autonomous driving simulator by applying an intelligent driver model (IDM) that approximates a driver's behavior and integrates responses.

The application belongs to the field of GPS positioning, and relates to a dynamic GPS signal interference influence evaluation method and system, which comprises the following steps: calculating the instantaneous position vectors and velocity vectors of visible satellites, a receiver and an interference source in the Earth-Centered Earth-Fixed coordinate system; obtaining the directional gain of a receiving antenna to multiple visible satellite signals and multiple interference signals respectively; calculating the noise power spectral density, the power of multiple visible satellite signals and the power of multiple interference signals arriving at the radio frequency front end of the receiver; modeling the equivalent carrier-to-noise ratio to obtain the equivalent carrier-to-noise ratio of multiple visible satellites; simulating the influence of the visible satellites on the code tracking loop of the receiver to obtain the pseudorange measurement error of multiple visible satellites; and performing positioning solution through a weighted matrix least square method to obtain the positioning accuracy, so that the interference effect can be accurately, real-timely and quantitatively evaluated.

Device and method for assisting a driver of a vehicle in optimizing a driving line (303) of the vehicle, particularly on a race track, wherein a signal characterizing a steering angle or steering input is received by an acceleration sensor, the acceleration sensor being located on the hand of a driver of the vehicle, particularly in a smartwatch, wherein a signal characterizing the position of the vehicle, particularly a GPS signal, is received, wherein the driving line (303) is determined depending on the signals, and wherein a map showing the driving line (303) and an ideal line (302), particularly the ideal line (302), for the race track is displayed on a display (300), particularly in the vehicle. The device comprises a vehicle.

A low-power Bluetooth key control method and system based on multimodal perception, relating to the field of Bluetooth keys, is disclosed. The method includes: The vehicle end determines the parking environment type using multi-source sensor data such as GPS signal strength, ambient light intensity, and the number of nearby Bluetooth devices, encoding this data into an environment identification code. This code, along with vehicle status information such as door lock status and the last unlock timestamp, is encapsulated in a Bluetooth broadcast packet and broadcast. The key end receives and parses the environment identification code, retrieving the corresponding target weight vector (including RSSI, motion state, and ranging accuracy weight coefficients) from a pre-stored database. The key end collects multi-dimensional feature parameters such as the current RSSI value, motion state, and CS ranging distance value, calculating the RSSI matching score, motion matching score, and ranging reliability score respectively. These are then combined with the target weight vector to calculate the unlock confidence score. A tiered unlocking strategy is executed based on the score's range. Implementing this method can improve the accuracy of Bluetooth car key unlocking judgment.

This application provides a UAV positioning method, system, UAV, and medium. In this method, firstly, inertial motion data and visual perception data are acquired in real time. The characteristic of inertial sensors, which are not limited by signal environment, continuously provides inertial motion data to avoid positioning interruption when the positioning signal is weak. Simultaneously, spatial pose data is generated by performing pose analysis on the visual perception data, and first position information is generated by matching it with a preset satellite map database, compensating for the accumulated errors of inertial navigation. By fusing the inertial motion data, spatial pose data, and first position information to generate second position information, and converting it into a GPS signal and connecting it to the UAV's GPS system, the error defects of a single sensor can be effectively suppressed, achieving continuous and accurate positioning in complex environments. This fundamentally solves the application limitations of traditional GPS positioning systems in special environments such as weak positioning signals.

The invention relates to a vehicle positioning method, a medium, a product, electronic equipment and a vehicle, and the method comprises the steps: determining the pose information of the vehicle according to the driving information of the vehicle, the driving information being the information outputted by a vehicle driving module; and obtaining positioning of the vehicle according to the pose information and the scene information of the vehicle. According to the method, the predicted value of the pose information is obtained through the driving information of the vehicle, and the scene information is processed by using the predicted value of the pose information, so that the vehicle positioning precision and the system reliability are improved, the method can adapt to various complex environments such as tunnels and underground parking lots, and even under the condition of weak GPS signals or low-speed driving, the vehicle positioning accuracy is improved. And reliable positioning can also be provided.

The invention provides an illumination synchronization method, system and device based on a GPS and a cloud end, and a storage medium. The method comprises the following steps: establishing local synchronization time of each light master controller in each building; receiving a GPS signal and analyzing first UTC time information; communicating with a cloud time service server through a network to obtain second UTC time information; respectively performing signal quality evaluation on the first UTC time information and the second UTC time information; dynamically selecting or weighting and fusing the first UTC time information and the second UTC time information based on the evaluation result to generate a reference time; and taming and calibrating the local clock source by using the reference time, and outputting the updated local synchronization time. According to the invention, through double time service source guarantee, extremely low time service errors among the main controllers can be maintained, accurate synchronous playing and timing control of cross-building light show are realized, and the reliability, stability and picture consistency of a large-scale light show system are effectively improved.

This application provides a method and system for wireless distributed data synchronization acquisition based on GPS timing. Each acquisition unit receives GPS signals, acquires the GPS second pulse signal and its actual trigger time, calculates and calibrates the local clock drift rate to ensure that the local clock is consistent with the GPS clock; after calibrating a fixed delay, it acquires structural response data and generates an original timestamp, and obtains a calibrated timestamp by combining the fixed delay and drift rate correction. The central computer generates a globally unified timeline based on the calibrated timestamp, maps the data of each acquisition unit through linear interpolation, and outputs the synchronization result. This application requires no wiring, has high synchronization accuracy, and is suitable for structural health monitoring of large and complex projects such as super high-rise buildings.

The invention discloses an auxiliary driving vehicle foresight real-time aerial view seamless splicing method, which comprises the following steps: acquiring a vehicle-mounted monocular image of vehicle foresight in real time, and converting the vehicle-mounted monocular image into a real-time BEV aerial view Bc; updating the pose conversion matrix RT based on the real-time driving data; calculating the real-time BEV aerial view Bc to obtain a real-time weight map Wc under a vehicle coordinate system; based on the pose conversion matrix RT, converting the real-time BEV aerial view Bc into a global real-time BEV aerial view GBc, and converting the real-time weight map into a global real-time weight map GWc; and in a global coordinate system, seamless splicing based on the global real-time weight map is carried out on the global real-time BEV aerial view GBc and a historical global BEV aerial view, and a global BEV aerial view GBt at the current moment t is obtained. The seamless splicing method for the foresight real-time aerial view of the auxiliary driving vehicle has the advantages that the problem that the splicing effect of the foresight aerial view in a tunnel, an underground parking lot and other areas without GPS signals is poor can be solved with low computing power cost, and the like.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

This invention discloses an adaptive control system for inspection drones, belonging to the field of drone intelligent control technology. This adaptive control system can automatically inspect the furnace, greatly reducing workload and improving inspection efficiency compared to traditional inspection methods. Furthermore, the automatic inspection avoids high-altitude operations, reducing the danger of inspection work. This invention can achieve continuous self-positioning of the drone based on preset reference objects within the furnace. This method can operate independently of GPS navigation, enabling rapid updates of the drone's spatial position even in furnaces with weak GPS signals. The entire process can be performed without signal interaction with other devices or platforms, significantly reducing the impact of signal interference. Additionally, when performing inspection tasks, the drone can operate outside of visual range without affecting its autonomous positioning, greatly enhancing the drone's inspection range and capabilities.

The invention discloses a method for preventing TBOX rack test data from being mistakenly collected, and the method comprises the steps: carrying out the feature analysis of vehicle condition information data collected by TBOX equipment, recognizing the rack test features in the data (for example, a GPS signal is lost or fixed, and a vehicle speed signal is zero or changes regularly), marking the data as rack test data based on an analysis result, and carrying out the recognition of the rack test features. And the marked part is filtered, and only the unmarked data is reported to the Internet of Vehicles cloud. According to the method, manual operation is replaced by automatic feature recognition, and the problem of false report of rack test data is solved from the technical level. The feature analysis ensures the distinguishing between the bench environment and the real vehicle environment, and the marking and filtering steps block the path from noisy point data to the production environment, thereby effectively ensuring the accuracy and reliability of the Internet of Vehicles cloud data, and avoiding the interference on the real vehicle analysis and driving safety caused by the misinformation data.

A navigation method and apparatus, a device, and a storage medium. The method comprises: respectively filtering a raw GPS signal, a raw dead reckoning (DR) signal, and a raw Wi-Fi positioning signal to obtain a filtered signal (S101); determining a first position difference between a GPS filtered position and a DR filtered position, and performing filter-based fusion on a signal of the GPS filtered position and a signal of the DR filtered position according to the first position difference to obtain a position signal to be determined (S102); and determining a target position by using a position in the position signal to be determined and a Wi-Fi positioning filtered position, and using the target position to perform navigation (S103).

A vehicle distribution display method, a storage medium and an electronic device, the present application relates to the technical field of Internet of Vehicles, solve the problem of low data security and timeliness of the prior art, the present application can be applied to Internet of Vehicles big data analysis business.The display method comprises the following steps: S1, the cloud accesses the vehicle GPS signal, and divides the vehicle into the grid table of the corresponding dimension;S2, the Web end displays the vehicle distribution, then the user selects the vehicle range is transmitted to the cloud;S3, the cloud is filtered according to the vehicle range The grid that meets the condition, draws the grid layer and extracts the boundary point position information, returns to the Web end;S4, the Web end obtains the corresponding range map from the third party map merchant according to the boundary point position information, and the map and the grid layer are fused according to the point position information and are displayed.The present application also provides a storage medium and an electronic device for realizing the above-mentioned vehicle distribution display method.