122 results about "Dead reckoning" patented technology

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.

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 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 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 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 application relates to the field of dead reckoning correction technology and discloses a dead reckoning correction system and method based on radio frequency identification zero resetting, which comprises the following steps: performing dead reckoning on the angular velocity and acceleration from an inertial measurement unit to generate a first predicted pose of a vehicle; in an interval during which a radio frequency identification (RFID) reader does not receive a reading event signal of an RFID tag along a track, continuously correcting the first predicted pose based on the lateral distance collected by a lateral distance sensor and preset track geometric information to obtain an intermediate pose; when the reading event signal is received, resetting the along-track position state of the intermediate pose to the along-track position corresponding to the read RFID tag to obtain a reset pose; outputting the reset pose and taking the reset pose as the initial pose of the next dead reckoning; and the application realizes the full-range reckoning of the track vehicle pose in a long-distance label-free interval without satellite positioning, and improves the reckoning accuracy of the pose.

The present application belongs to the technical field of intelligent sensing and pose detection, and provides a precise pose linkage measurement system for underground equipment to solve the natural shielding of GNSS signals in the current underground environment. The preset starting point coordinate information is input into the upper computer, then the running state and basic navigation information of the tunneling equipment are obtained through the track walking pulse measurement device and the inertial navigation device respectively, and the movement amount of the tunneling equipment is collected through the signal transceiver device. When the tunneling equipment stops moving after completing the work, the anchor hoisting machine starts to move and follow, until reaching the preset interval distance, then the ultra-wideband cascade module measures the position change between the tunneling equipment and the preset starting point, the data solving module comprehensively calculates the dead reckoning result based on the above data, and the dead reckoning result is fed back to the upper computer, realizing the real-time detection of the precise pose of the whole tunneling equipment, and still being able to stably collect data in the harsh underground environment.

The invention discloses a lightweight inertial navigation positioning method and a lightweight inertial navigation positioning system based on a motion perception early-leaving mechanism, which are applied to mobile terminal positioning scenes such as pedestrian dead reckoning and the like. The method comprises the following steps: acquiring a three-axis acceleration and a three-axis angular velocity by using a built-in inertial measurement unit of a terminal, inputting preprocessed inertial data into an inertial navigation neural network, and sequentially outputting displacement estimation and covariance thereof at each layer by the network; and the early-backing judgment module takes the variance as a confidence index, compares the variance with a preset early-backing threshold value, terminates reasoning in advance at a current outlet and outputs a displacement result when the confidence meets the requirement, otherwise, continues to spread to a deeper layer, and realizes adaptive adjustment of the reasoning depth according to the motion complexity. According to the method, the reasoning efficiency is remarkably improved in low-dynamic scenes such as static scenes and slow scenes, the method has the advantages of being small in calculation overhead, low in energy consumption, suitable for real-time deployment on smart phones, wearable devices and augmented reality terminals and the like, and the endurance of a deep learning inertial navigation positioning system can be greatly improved.

An autonomous drone swarm system and method for subterranean and GPS-denied operations utilizing artificial intelligence for coordinated operations comprises command drones equipped with large language model processors and subordinate drones coordinated through a hierarchical Queen-Worker architecture. The system processes natural language commands, generates autonomous mission plans, and coordinates multi-drone operations through an encrypted self-healing mesh communication network utilizing multiple modalities including radio frequency, optical, acoustic, and visual channels. A Unified Ground Frame with Surface-Referenced Z (UGF-SRZ) coordinate system provides signed vertical coordinates enabling seamless operation across surface and subterranean environments. GPS-denied navigation combines multi-sensor dead reckoning, collaborative positioning, and distributed mapping with fog-layer processing for enhanced accuracy. The system implements federated learning protocols for distributed AI improvement, fault-tolerant operation with automatic leader election, and specialized adhesion Workers functioning as persistent infrastructure nodes.

This application relates to the field of Internet of Things (IoT) technology, and in particular to a method and system for personnel safety monitoring in chemical industrial parks based on dual-channel heterogeneous data transmission and multi-source fusion. The method synchronously collects environmental parameters and inertial data through mobile sensing terminals; constructs an extended Kalman filter model to tightly couple and fuse ultra-wideband positioning coordinates with inertial dead reckoning data to correct cumulative drift in predicted values; employs a vertical distance threshold trajectory compression algorithm at the edge to eliminate redundant positioning points on linear paths, reducing transmission load; dynamically adjusts the radius of the electronic fence based on gas concentration and determines fall events by combining acceleration amplitude temporal characteristics; and utilizes a dual-channel WiFi collaborative mechanism to normally upload lightweight data via a low-power channel, only activating the high-speed channel to upload on-site images when boundary crossings or fall anomalies are detected. This application effectively solves the technical problems of low positioning accuracy, multimedia data transmission bandwidth conflicts, and delayed safety warnings in industrial sites.

The invention discloses a high-precision real-time track positioning method and system and a computer readable storage medium, and the method comprises the steps: firstly building a path digital model containing discrete high-precision azimuth angle information, carrying out dead reckoning through employing a low-cost IMU when a mobile carrier runs, and carrying out the dead reckoning through employing a low-cost IMU when the mobile carrier travels for a small fixed distance; and obtaining an accurate azimuth angle at the position from the digital model as an external observed quantity. Through a data fusion algorithm, continuous calculation of the IMU and discrete absolute azimuth information are optimally fused, and the real-time arc length position of the carrier on the path is accurately solved, and then the real-time arc length position is mapped into global coordinates. According to the method, the accumulative error of inertial navigation is effectively inhibited, continuous and high-precision track positioning which does not diverge along with time is realized at low cost, and the method is particularly suitable for scenes of water gate opening and closing, rail transit, AGV (Automatic Guided Vehicle) and the like running along a fixed path.

The application discloses a fusion PDR / GNSS indoor and outdoor seamless positioning system and method, and applies to the satellite navigation and dead reckoning technical field, and comprises a GNSS module, a PDR module, a joint step estimation module, a joint direction estimation module and a position updating module.The application effectively reduces the significant accumulated error of pedestrian dead reckoning (PDR) due to the low performance of MEMS devices with time growth, fuses PDR and GNSS, and thus optimal direction and step estimation are given.

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).

The invention relates to the technical field of navigation, in particular to a mine monorail crane navigation method and system based on an odometer and inertial navigation. The method comprises the steps that the clamping force of a monorail crane driving wheel and the pulse number of an encoder are collected; based on a preset nonlinear relation model between the clamping force of the monorail crane driving wheel and the radial variable of the tire, obtaining the radial variable corresponding to the clamping force; determining the actual distance of the speedometer according to the radial variable and the encoder pulse number; calibrating the odometer measurement error based on the actual distance; after calibration is completed, establishing a strapdown inertial navigation / dead reckoning combined positioning model according to strapdown inertial navigation errors and dead reckoning errors; based on the combined positioning model, performing optimal estimation by using Kalman filtering; and performing feedback correction on inertial navigation error parameters according to an estimation result. The navigation precision of the mining monorail crane can be improved.

The invention provides a method for realizing high-precision underwater integrated navigation by fusing the dynamic speed of an AUV (Autonomous Underwater Vehicle) and acoustic data of a single beacon, and belongs to the technical field of underwater navigation and positioning. The method comprises the following steps: estimating a water navigational speed under a carrier coordinate system by using a propeller rotating speed and a motor current, decomposing the water navigational speed into a navigation coordinate system by fusing an SINS attitude, obtaining an AUV three-dimensional motion speed, and obtaining an initial position based on dead reckoning; and a navigation strategy is adaptively selected according to the operation duration and acoustic availability: pure dynamic dead reckoning is adopted in medium and short time tasks, single-beacon distance information is introduced in long-endurance operation, a dead reckoning result is subjected to periodic constraint through a geometric projection filtering algorithm with a reset starting point, and a corrected navigation position is obtained. Compared with a single data source, the method has the advantages that the navigation precision and reliability are remarkably improved, and the technical problem that the application of the acoustic sensor in the deep sea environment is limited is effectively solved.

The application discloses a vehicle path point determination method and device, a vehicle and a storage medium. The method comprises the following steps: obtaining an original vehicle path point column of a vehicle in a geodetic coordinate system; converting the original vehicle path point column into an intermediate vehicle path point column in a coordinate system with a starting point of a vehicle path as an origin; determining a slope of each point in the intermediate vehicle path point column; and converting the intermediate vehicle path point column into a target vehicle path point column in a dead reckoning (DR) coordinate system according to the slope of each point. The method can conveniently and accurately obtain the coordinates of the vehicle in the DR coordinate system by converting the original vehicle path point column of the vehicle in the geodetic coordinate system into the intermediate vehicle path point column in the coordinate system with the starting point of the vehicle path as the origin, and by determining the slope of each point in the intermediate vehicle path point column and converting the intermediate vehicle path point column into the target vehicle path point column in the DR coordinate system.

This invention relates to a multi-sensor fusion positioning method for autonomous trucks in industrial parks, comprising: generating a high-precision point cloud map and a gridded high-precision positioning layer based on sensor data acquisition; acquiring positioning information from a laser positioning module, GNSS positioning module, 6G positioning module, and dead reckoning module in real time; performing single-source verification on laser positioning, GNSS positioning, and 6G positioning, marking each positioning source as valid, suspected invalid, or invalid; performing cross-source consistency verification based on 6G positioning as a benchmark to identify hidden faults; dynamically adjusting the weights of each positioning source according to the verification results; fusing positioning information through an error state Kalman filter to output a robust positioning result. This invention, by fusing 6G, GNSS, laser radar, and dead reckoning data, constructs a multi-source redundant positioning system, significantly improving the positioning robustness of autonomous trucks in complex park environments, accurately identifying and suppressing hidden faults, and ensuring reliable positioning results.

This disclosure discloses a method for determining auxiliary positioning information, a dead reckoning method, and related products. The method for determining auxiliary positioning information includes: matching a DR (Dead Reach) location with map data to obtain one or more candidate map matching points and their matching data, wherein the matching data includes at least: matching degree, matching location, and matching road segment; determining one candidate map matching point as the map matching point from the candidate map matching points; determining the distance between the map matching point and the divergence point; and generating map matching feedback information for correcting the DR location, wherein the map matching feedback information includes the distance between the map matching point and the divergence point and the matching data of at least one candidate map matching point. The generated map matching feedback information can improve the accuracy and rationality of DR location correction.

The present application relates to the technical field of non-networked indoor autonomous positioning and navigation, and particularly to a smartphone positioning and navigation method and system in a non-networked indoor scene, which comprises the following steps: a smartphone receives data returned by a Bluetooth AOA positioning base station through point-to-point communication, and obtains an AOA position estimation result by solving a single-base-station AOA fusion positioning model; the smartphone collects real-time data of its own IMU and magnetometer, recursively estimates the user's coordinates and heading angle by a pedestrian dead reckoning (PDR) method, and autonomously switches among multi-base-station centroid positioning, AOA fusion positioning and PDR positioning according to the actual AOA base station deployment in the indoor space, so that the position result can effectively cover the entire space scene; a binary raster map is obtained by constructing an indoor map model, position post-processing of the positioning coordinates is performed based on map matching, pedestrian coordinates are obtained, the map model is dataized, map display is realized, and finally pedestrian position display is realized. The present application can reliably operate in a network-lacking scene.

The application discloses a dead reckoning method and device and electronic equipment, comprising: constructing a current state quantity corresponding to a filter according to forward speed information of a target vehicle at a current time, and predicting the current state quantity to obtain a state quantity prediction result of the filter; obtaining a residual error between a current observed vehicle speed value corresponding to the target vehicle and the state quantity prediction result, determining a residual error replacement quantity matched with the residual error, and updating the state quantity prediction result of the filter according to the residual error replacement quantity; and determining a dead reckoning result corresponding to the target vehicle according to the updated state quantity prediction result and steering information of the target vehicle. The technical scheme of the embodiment of the application can improve the reliability of the dead reckoning result.