241 results about "Dead reckoning" patented technology

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 provides a visual navigation method and system based on an improved optical flow method, and relates to the technical field of computer vision and inertial navigation. A collaborative optimization framework of an inertial navigation system and visual feature navigation is constructed, carrier motion state parameters are obtained through dead reckoning of an inertial measurement unit, variance parameters of pose variation are calculated, and the layering depth of a pyramid LK optical flow method is dynamically adjusted based on the variance parameters. When the inertial navigation output variance exceeds a preset threshold value, the number of layers of an image pyramid is increased to cope with large-range motion, and otherwise, calculation levels are reduced to improve real-time performance. A dual-period optical flow / feature matching tracking mechanism is designed, improved sub-pixel-level optical flow tracking is adopted in a short period, the calculation complexity is reduced while the positioning precision is guaranteed, feature matching is switched to in a long period, accumulative errors are eliminated, and a period parameter N is determined by inertial navigation precision. According to the method, the calculation complexity can be reduced, and a better effect can be achieved on a low-calculation-performance platform.

The invention discloses a rapid measurement method and system for geometric irregularity of a track in a turnout zone. The method comprises the steps that complete three-dimensional point cloud data of the surface of a railway turnout steel rail are obtained in real time through a multi-angle line structure laser scanner on a special detection vehicle body; accurate attitude and position data of the detected vehicle body in the measurement process are obtained in real time through an inertial navigation measurement unit and an odometer; obtaining a high-precision pose of a detected vehicle body through a robust dead reckoning method of an IMU (Inertial Measurement Unit) assisted by a speedometer dynamic lever arm value based on a variable wheel-rail contact point; constructing a turnout area three-dimensional point cloud model; extracting geometric irregularity key points of turnout steel rail point cloud from the turnout area three-dimensional point cloud model; calculating parameters of track gauge, horizontal and rail direction irregularity and unevenness; the special detection vehicle body can quickly and stably pass through a turnout area to complete continuous and accurate measurement of geometric irregularity of a track.

The invention discloses an operator positioning method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target positioning scene corresponding to an operator in a plurality of preset positioning scenes according to comparison between a visible satellite number and a satellite number threshold value, comparison between a signal-to-noise ratio of a visible satellite and a signal-to-noise ratio threshold value and comparison between reference signal receiving power and a reference signal receiving power threshold value; wherein the preset positioning scene at least comprises an outdoor open scene, an indoor scene, an outdoor shielded scene and a special operation scene; determining a target positioning mode matched with the target positioning scene in a plurality of preset positioning modes; wherein the preset positioning modes at least comprise Beidou satellite positioning, 5G positioning and Beidou satellite and 5G fusion positioning, and the calculation positioning mode assists the Beidou satellite and 5G fusion positioning; and positioning the operator through the target positioning mode, and determining a target positioning result of the operator. According to the scheme, the accuracy of positioning the operator can be improved.

Embodiments relate to a method and apparatus for determining lane level localization of a vehicle with global coordinates. An automotive vehicle includes a vehicle navigation system, a database comprising standard-definition map data corresponding to road level latitude and longitude coordinates for a plurality of roadway features, at least one image sensor, and a processor in communication with the vehicle navigation system, the database, and the at least one image sensor. The processor is configured to determine an initial location of the vehicle, estimate a current location of the vehicle based on a dead reckoning process, detect, based on image data from the at least one image sensor, a lateral position of the vehicle within a lane of a road, and update the current location of the vehicle based on a difference between the estimated current location and the lateral position of the vehicle within the lane.

The invention provides a sonar-assisted underwater navigation positioning method and system, and the method comprises the steps: carrying out dead reckoning according to an attitude angle outputted by an IMU and a speed outputted by a DVL, and obtaining a dead reckoning odometer factor; after the first frame is set as the key frame, if the time difference between the current frame and the previous key frame is greater than a time difference threshold value or the pose change of the current frame relative to the previous key frame is greater than a pose change threshold value, the current frame is set as the key frame; performing filtering, feature extraction and matching on the sonar image; performing pose extraction according to the matched feature points to obtain a sonar odometer factor; key frames in a preset range are searched for loopback detection, and a loopback constraint factor is obtained through matching; and performing back-end optimization on the to-be-optimized pose by using a factor graph optimization method. According to the method, dead reckoning results of the IMU and the DVL are adopted as odometer factors, and calculation of back-end optimization can be greatly simplified.

The invention provides a train attitude precision keeping method and system based on inertial quaternion modeling, and belongs to the technical field of train operation positioning. Through a quaternion modeling train attitude feedback compensation model, auxiliary calibration of a gyroscope is realized by using an accelerometer, and calculation of an inertial attitude is realized; converting the acquired wheel speed sensor information into three-dimensional speed under a navigation coordinate system through attitude information resolved by a quaternion modeling train attitude feedback compensation model, and further integrating to obtain a position; and fusing the speed and position information of the wheel speed sensor after attitude conversion with the speed and position information obtained by inertial navigation dead reckoning to realize error correction of the inertial navigation system. According to the method, the problem that the train state sensing precision is degraded due to the fact that a satellite navigation / inertial navigation system positioning model is difficult to overcome inertial device error accumulation when a satellite fails is solved, the train attitude precision is kept, and then error divergence of a combined positioning system during satellite signal denial is effectively restrained.

The invention discloses an IMU online adaptive calibration method and calibration device and an automatic driving system, and relates to the technical field of deep learning, automatic driving and navigation. Comprising the following steps: acquiring original measurement data from an IMU in real time; inputting the original measurement data into an error prediction model to output predicted IMU error parameters, wherein the error prediction model adopts a hybrid neural network architecture, is trained and updated based on an end-to-end framework containing a differentiable physical model, and is configured to execute offline training in an offline stage and execute adaptive updating in an online application stage; and correcting the original measurement data by using the IMU error parameter and executing dead reckoning to generate a calibrated motion state track. According to the invention, a static off-line calibration system is improved into a dynamic on-line adaptive system, so that the long-term robustness and reliability are remarkably improved. A differentiable physical model is introduced as a training bridge to realize an end-to-end physical model training normal form, so that the training precision can be improved and the training convergence can be accelerated.

The invention provides a high-precision satellite navigation positioning method based on Android, and relates to the technical field of positioning navigation and dead reckoning, the compatibility and adaptability of a system are improved by constructing an abstraction intermediate communication interface between an application layer and an Android kernel layer; asynchronous processing is carried out through monitoring callback, so that deep dependence on a kernel is reduced; batch acquisition is realized by adopting an annular buffer area, and the acquisition frequency and the task priority are adjusted in real time according to the motion state of terminal equipment in combination with a dynamic sampling frequency and scheduling mechanism; the method comprises the following steps: constructing a multi-source data fusion model, fusing multi-source data, and synchronously aligning with timestamps to generate accurate position information; the CPU occupancy rate and the energy consumption in the fusion process are monitored in real time, and the sampling frequency and the scheduling mechanism are dynamically optimized in combination with terminal feedback and a preset threshold value, so that the positioning precision and the equipment load are balanced, and high efficiency and high reliability of the system are realized.

The invention discloses a vehicle positioning method based on Pareto and sensor fusion, and the method comprises the steps: building a dead reckoning model based on vehicle kinematics according to the vehicle driving data through obtaining the induction data, collected by a plurality of sensors, of an autonomous driving vehicle; the point cloud data of the 3D laser radar is recorded on a brightness frame generation module for brightness frame extraction to obtain a brightness map, a plurality of reference points of the brightness map are obtained, and a positioning model of the autonomous driving vehicle is constructed according to the plurality of reference points; the dead reckoning model and the positioning model are subjected to fusion estimation to obtain a position estimation result, the position estimation result is input into a Pareto positioning model for optimization analysis to obtain a vehicle positioning result, multiple standards can be optimized in real time by adopting multiple sensors and a Pareto optimization algorithm, no linear processing is needed, and the positioning accuracy is improved. Flexible and robust positioning is realized, and the reliability of autonomous vehicle positioning estimation is improved.

The invention discloses a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. The method comprises the following steps: calculating the distance between a receiving end and a transmitting end according to the positions of a visible light transmitting end and a receiving end and visible light original data collected by the receiving end; calculating the collected inertial data by adopting a pedestrian dead reckoning method to obtain position information of a receiving end; and carrying out tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on a particle filtering algorithm to obtain a predicted pedestrian position. According to the method, the pedestrian dead reckoning positioning result and the visible light ranging information are fused, the defects of a single positioning technology are overcome, the method has good performance on a fusion positioning system of visible light and other signals susceptible to environmental interference and inertial and other high-noise signals, the adverse effect of environmental noise on the fusion system is weakened, and the positioning accuracy is improved. And the migration capability and the adaptive capability of the fusion system are effectively improved.

To solve the problem that it is general to generate a response on the basis of single input and an ability for continuously tracking emotions and intentions of a user is limited, and thus, it is difficult to maintain a natural dialog in a conventional AI dialog system.SOLUTION: The present invention applies a technique named dead-reckoning for estimating a current position from a position and speed in the past in navigation and aviation to a dialog with AI, extracts important information from dialog data in the past of a user, and estimates current needs and emotions. Thus, the intentions of the user are understood, directivity of the dialog is defined, the emotions and degrees of interest are grasped as speed and force of the dialog, a psychological state of the user and context of the dialog are more accurately analyzed, and a response adaptable to the analyzed psychological state and context are generated. For that purpose, the emotions of the user are estimated, and directivity of the dialog is analyzed to generate a personalized response by a response generation unit to be constituted of a user emotion analysis unit, a dynamic dialog navigation analysis unit, a personalized response generation unit, and a learning and evolution unit.SELECTED DRAWING: Figure 3

The invention discloses a trajectory reduction and map reconstruction method capable of solving a time drift error and application, and relates to the technical field of data processing. The method comprises the following steps: constructing a robot kinematics model constraint, fusing real-time synchronous data streams of multiple sensors, and combining a dynamic sensor weight distribution function and Kalman filtering to generate an initial motion track; adjusting the trajectory based on dead reckoning and a pipeline kinematics model in a non-feature region, matching pipeline feature points through an ICP algorithm to calculate a pose deviation, and correcting a trajectory error by using Kalman filtering; iMU, vision and pipeline geometric constraints are optimized and fused through a sliding window graph, and an optimal track is obtained; generating a three-dimensional digital twinborn model with defect mapping in combination with laser point cloud and Poisson reconstruction; according to the method, the problems of large sensor accumulative error, feature deficiency positioning failure and low modeling precision are solved, high-precision positioning in a complex pipeline environment is realized, the detection efficiency is remarkably improved, and the manual dependence is reduced.

The invention discloses a high-precision indoor positioning system based on a smart bracelet, and the system comprises a data processing module which is used for obtaining and preprocessing multi-source sensing data collected by the smart bracelet; the gait reckoning module is used for executing gait event detection and pedestrian dead reckoning processing; the wireless observation module is used for carrying out preprocessing and credibility evaluation on indoor wireless positioning observation data; the fusion positioning module is used for carrying out credibility weighted fusion; the trajectory constraint module is used for applying trajectory rationality constraint and correcting abnormal position points; the error correction module is used for constructing a positioning error dynamic correction model and executing online correction processing; and the track output module is used for carrying out smoothing and time resampling processing and extracting a human body position determination result set. The method is based on the multi-source perception fusion and federated Kalman dynamic correction method, achieves the indoor high-precision positioning of the smart bracelet, and has the advantages of continuous track, controllable error and high environmental adaptability.

The invention discloses a green cutting robot navigation method and system, relates to the field of green cutting robot navigation, is used for improving the navigation precision of a green cutting robot, and comprises the following steps: acquiring different types of environmental information in front; the different types of environment information comprise image data and radar point cloud data; determining target crop path information independently identified according to the respective environment information; the reliability scores of the target crop path information independently identified according to the respective environment information are evaluated respectively; comparing the spatial geometric consistency of the target crop path information independently identified according to the respective environment information and the respective reliability score, and quantifying the overall credibility of the current environment information; when the total credibility is greater than or equal to a credibility threshold value, navigation is performed according to different types of environment information; when the total credibility is smaller than a credibility threshold value, navigation is carried out according to internal dead reckoning.

The invention relates to a multimode positioning work card system integrating a speed sensor and Beidou positioning and a method thereof. The system comprises a Beidou positioning module, an IMU (Inertial Measurement Unit) and a data processing module, and can dynamically fuse Beidou and IMU data and output an accurate positioning result. And the data processing module integrates a sensor calibration unit, a self-adaptive Kalman filter and a map auxiliary correction unit, and performs IMU error calibration, signal fusion and trajectory correction. The system dynamically adjusts the trust weight according to the Beidou signal quality through adaptive Kalman filtering, IMU dead reckoning is started when the signal is interrupted, a stride frequency detection and other motion constraint models are introduced, and the positioning error is remarkably reduced. In addition, the map auxiliary correction function corrects the IMU reckoning trajectory through preset geo-fence data, and the problem of positioning drift of a traditional system in a complex environment is solved.

The invention provides an unmanned aerial vehicle forest fire prevention smoke intelligent early warning method, device and equipment and a medium, and the method comprises the steps: detecting a smoke concentration value in a target inspection region at a current moment, when the smoke concentration value is greater than a preset concentration value, acquiring a multi-frame image sequence in a first preset time period and acquiring a multi-frame image sequence in a second preset time period; smog feature analysis is carried out on the multi-frame image sequence in the first preset time period and the multi-frame image sequence in the second preset time period to obtain smog multi-dimensional features; a thermal imaging data sequence in a second preset time period is collected, and flame multi-dimensional features are extracted; based on the smog multi-dimensional features and the flame multi-dimensional features, a fireproof early warning grade is judged; if the fireproof early warning level is greater than the preset early warning level, fusing the fire source navigation positioning information and the fire source dead reckoning information to obtain a fire source position coordinate; and generating a fireproof early warning prompt based on the fire source position coordinates, and sending the fireproof early warning prompt to a ground control center. Therefore, the forest fire prevention efficiency is effectively improved.

The invention discloses an underwater three-dimensional reconstruction and sonar pose joint optimization method based on a multidirectional sonar, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining and preprocessing multi-modal sensor data, and converting coordinates into a world coordinate system; dead reckoning is carried out through the IMU and the DVL to obtain a rough pose; constructing and optimizing an implicit occupancy probability model; screening effective frame pairs; calculating point cloud loss; a total loss function is constructed, the pose and the model parameters are jointly optimized, and a reconstruction result and an optimization track are output; according to the underwater three-dimensional reconstruction and sonar pose joint optimization method based on the multidirectional sonar, the reconstruction problem caused by lack of features and unstable sensor data in a turbid water area is effectively solved.

The invention relates to the technical field of computer vision, and discloses a precise medicine delivery method and system, and the method effectively strips illumination artifacts through the construction of a local gradient structure tensor field and an anisotropic screening mechanism, remarkably improves the robustness of a visual front end in a light and dark alternating environment, and improves the accuracy of medicine delivery. Rigid body motion constraint and Lie algebra continuous modeling are utilized, high-precision space-time alignment between heterogeneous sensors is realized on the premise that scene depth does not need to be recovered, hardware delay errors are eliminated, a physical-driven probability weight dynamic allocation mechanism is constructed by introducing a multipath scattering index and a structural information entropy flux, and a dynamic space-time alignment algorithm is established. Environment degradation is sensed in real time, the observation weight is adjusted in a self-adaptive mode, the influence of the non-line-of-sight multipath effect and visual texture missing is effectively restrained, high-precision inertial dead reckoning can still be maintained in a blind area where a sensor is in full failure in combination with momentum prior information generated through Schel complement marginalization operation, and the method has the advantages of being high in precision and high in precision. And the navigation continuity and reliability of the distribution robot in a complex scene are ensured.

The invention provides a Bluetooth and cellular fusion positioning method for indoor asset positioning, which realizes dynamic noise suppression through adaptive Kalman filtering, and senses environment change in real time and intelligently allocates Bluetooth and cellular positioning weights in combination with a dynamic weight fusion mechanism driven by an environment credibility factor, so that the positioning accuracy is improved. Meanwhile, by means of indoor map topology constraint and Bezier curve trajectory optimization, the positioning precision and trajectory rationality are effectively improved. In the aspect of power consumption management, graded awakening, dynamic voltage frequency adjustment and solar energy and vibration energy mixed collection technologies are adopted, and precision and energy consumption are balanced; through multi-sensor complementary fusion (IMU dead reckoning and barometer auxiliary floor identification), adaptive anomaly detection and environmental prior information utilization, the robustness of the system in complex scenes such as signal shielding is enhanced. In addition, through lightweight hardware design, low-density beacon deployment and asset classification operation and maintenance strategies, the initial deployment and long-term operation and maintenance cost is reduced.

Novel systems and methodologies for precise indoor positioning employs a plurality of synchronized beacons emitting uniquely identifiable acoustic positioning signals in synchronized fashion to one another for receipt by microphone-equipped tracking devices, and beacon controllers emitting wireless synchronization signals receivable by RF receivers of those devices, at least one of which has multiple microphones to enable calculation of both position and orientation. An onboard IMU of each tracking device is used for dead reckoning between acoustic epochs, and for efficiently targeted searching of expected acoustic signals in the captured audio samples. Audio and IMU data captured by the target device is used for event detection purposes, and in the case of the former, to diagnose and remediate or mitigate environmental noise of detriment to the acoustic positioning functionality.

The invention discloses an indoor positioning method and system based on 5G channel characteristics and pedestrian dead reckoning, and belongs to the technical field of indoor positioning. The method comprises the following steps: firstly, converting input complex 5G channel impulse response (CIR) data into a two-dimensional characteristic matrix; inputting the two-dimensional feature matrix into a deep convolutional neural network to perform feature transformation and obtain a 5G positioning position track, and then performing time sequence smooth optimization on the 5G positioning position track by using a Kalman filter to obtain an optimized 5G position track; according to the invention, a deep convolutional network is adopted to fully extract amplitude-phase composite features of 5G channel impact response CIR, and machine learning step length measurement and calculation of multi-dimensional gait features are combined to accurately position an indoor position track, so that the utilization efficiency of positioning data is improved; and intelligent complementation can be carried out between the optimized 5G position trajectory and the dead reckoning trajectory, and the indoor positioning precision and positioning stability in a complex indoor environment are improved.

The invention discloses an indoor positioning track tracing and area early warning method and system, and relates to the technical field of indoor positioning, the method comprises the following steps: receiving a positioning signal, and synchronously collecting inertial navigation data of an inertial measurement unit; filtering processing is carried out on the positioning signal, non-line-of-sight interference and multipath effect interference components are identified, and interference compensation parameters are generated; after a positioning signal is calibrated by combining the parameters, real-time position coordinates are calculated; running a pedestrian dead reckoning algorithm on the basis of inertial navigation data to obtain continuous relative position coordinates, and correcting by taking the real-time position coordinates as reference to form initial movement track data; presetting an early warning area boundary and a triggering condition, and if the condition is met, starting an early warning response and recording an early warning event; and integrating the continuous real-time position coordinates, the initial movement track data and the early warning event records, and constructing a complete movement track file to realize track tracing. The system is matched with the method, the positioning reliability in the indoor complex environment can be improved, and continuous track tracing and accurate area early warning are supported.

The invention provides a sound source localization method using time delay and dead reckoning. The method comprises the following steps: acquiring an original sound signal and carrying out noise reduction preprocessing; virtual sound rotation processing is carried out on the two sound source directions, interpolation simulation rotation is carried out on effective sound signals after noise reduction, and angle differences before and after rotation are compared to eliminate false angles; and finally calculating the position of the sound source through a geometric positioning algorithm by combining the unique sound source direction, the movement distance and the movement direction of the equipment and the time difference between the sound source and the two microphones. According to the method, acoustic orientation and a smart phone inertial navigation technology are deeply fused, a displacement vector is obtained by utilizing dead reckoning, and multi-position TDOA measurement information is combined, so that sound source three-dimensional space positioning only depending on single mobile equipment is realized, and the inherent defect that a traditional pure acoustic positioning method cannot measure distance is overcome.

The invention discloses an error real-time compensation method for GNSS (Global Navigation Satellite System) dynamic positioning, which relates to the field of GNSS dynamic positioning and comprises the following steps of: obtaining a to-be-corrected track fragment with a continuous motion trend; calling the digital map data of the corresponding area from a local storage and / or a cloud server; determining one or more candidate matching roads according to the matching degree of the track course change features and the road curvature features; performing short-time dead reckoning to obtain a predicted position at the current moment; based on the road traffic direction constraint and the connectivity constraint, the projection points are verified; and taking the final corrected positioning result as a reference point of dead reckoning at the next moment, and continuously and progressively correcting the GNSS dynamic positioning error. The method has the advantages that through short-time dead reckoning prediction, dynamic positioning errors and inertia accumulation drifting are restrained, a positioning result is forced to be reasonably attached to a real road network all the time, and high-reliability, low-cost, continuous and stable dynamic positioning is achieved.

The invention discloses a robust pedestrian dead reckoning method and device based on geomagnetic self-adaption and time-frequency pace fusion, electronic equipment and a storage medium. The method comprises the following steps: carrying out preprocessing and attitude calculation on data of an inertial sensor and a geomagnetic sensor; based on geomagnetic module value fluctuation, direction consistency and frequency domain energy characteristics, geomagnetic disturbance self-adaptive judgment and quality grading are achieved; time-frequency energy features are extracted through short-time frequency domain analysis, and robust pace event detection is achieved; on the basis of a residual feedback mechanism, fusing an environment structure and external positioning constraint to carry out online correction step length estimation; dynamically adjusting the weight of the magnetometer according to the geomagnetic quality to realize heading robust fusion; and finally, step-level trajectory recursion is carried out. Through geomagnetic perception-time frequency gait-residual step length-course fusion multi-module closed-loop cooperation, track continuity, positioning precision and long-term stability are effectively improved, and the method is suitable for continuous and reliable positioning of smart phones and wearable terminals in indoor, underground and other weak GNSS environments.

The invention relates to the technical field of non-networked indoor autonomous positioning and navigation, in particular to a smart phone positioning and navigation method and system in a non-networked indoor scene, and the method comprises the steps that a smart phone receives data returned by a Bluetooth AOA positioning base station through point-to-point communication, and an AOA position estimation result is obtained through calculation of a single-base-station AOA fusion positioning model; the smart phone collects real-time data of an IMU and a magnetometer, and user coordinates and course angles are recursively estimated through a pedestrian dead reckoning (PDR) method; according to an AOA base station deployment condition of an actual indoor space, multi-base station centroid positioning, AOA fusion positioning and PDR positioning are automatically switched, so that a position result can effectively cover a whole space scene; the method comprises the following steps: constructing an obtained binary grid map through an indoor map model, carrying out position post-processing based on map matching on positioning coordinates to obtain pedestrian coordinates, and carrying out digitization on a map model to realize map display and finally realize pedestrian position display. The method can be reliably operated in a scene lacking network coverage.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

The invention belongs to the technical field of positioning and navigation, and provides a geomagnetic sequence positioning method based on BiLSTM and multi-head attention. The method comprises the following steps: firstly, acquiring data of geomagnetism, an accelerometer, a gyroscope and other sensors by using a smart phone, and dividing a geomagnetism sequence into a plurality of subsequences to enhance the time sequence modeling capability; then constructing a multi-scale feature extraction model: extracting deep time sequence features of each sub-sequence by adopting a bidirectional long-short term memory network (BiLSTM), and constructing global representation through splicing; a self-adaptive multi-head attention mechanism is introduced, magnetic signal key features are captured from multiple dimensions, feature weights are dynamically adjusted in combination with a gating mechanism, and important information enhancement is achieved. And the fused multi-scale features output initial position estimation through a full connection layer. In order to further improve the positioning precision, data of a magnetometer and a gyroscope are fused through complementary filtering to correct a course angle, a geomagnetic positioning result serves as an observation value, a pedestrian track plotting result serves as a state prediction value, and final fusion positioning is achieved through particle filtering.

The present invention relates to a high-precision combined navigation and positioning method and device. The method comprises: S1. acquiring downlink navigation enhancement information from low-orbit satellites and navigation signals from medium- and high-orbit satellites, and using the observation information from the low-orbit and medium- and high-orbit satellites to initialize real-time navigation and positioning, outputting first positioning information; S2. acquiring observation signals from gyroscopes and accelerometers to perform inertial navigation and positioning, and outputting second positioning information; S3. acquiring measurement signals from the gyroscope assembly and odometer sensor to perform dead reckoning, and outputting third positioning information; and S4. fusing the first, second, and third positioning information, and using the Kalman filter method to solve the combined navigation information. The present invention can achieve continuous, high-precision, stable navigation and positioning services.