Intelligent terminal single point positioning method, system and product

Abstract

The application relates to a kind of intelligent terminal single point positioning method, system and product.The method comprises: obtaining the acceleration of intelligent terminal, and generating characteristic vector according to acceleration;According to the characteristic vector, the motion mode classifier is trained, and the current pedestrian motion mode is identified using the trained motion mode classifier, to determine the pedestrian motion state;When the intelligent terminal sends a positioning request, obtain the RTCM ephemeris data and GNSS original observation value received by the intelligent terminal, calculate the satellite ephemeris using the RTCM ephemeris data;Smooth the GNSS original observation value to generate the smoothed GNSS observation value;Using the RTCM ephemeris data, the quality control is carried out on the smoothed GNSS observation value, to determine the integrated GNSS observation value;Based on the precise point positioning model, according to the pedestrian motion state, the integrated GNSS observation value, the satellite ephemeris, the initial coordinates of the intelligent terminal are iteratively processed using Kalman filtering algorithm, and the position coordinates of the current pedestrian are output.The application can improve the positioning accuracy of intelligent terminal.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and in particular to a single-point positioning method, system and product for intelligent terminals. Background Technology

[0002] Currently, the BeiDou-3 Global Navigation Satellite System has been fully completed, and navigation satellite services have basically covered most industries with positioning needs. However, in the most widely used smartphone GNSS positioning service, the quality of raw GNSS observation data acquired by the phone is poor due to limitations in the phone's antenna and chip quality. Traditional positioning technologies are insufficient to meet the needs of some high-precision application scenarios, resulting in poor positioning accuracy for smart terminals. Summary of the Invention

[0003] The purpose of this invention is to provide a single-point positioning method, system, and product for smart terminals to solve the problem of poor positioning accuracy of smart terminals.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A single-point positioning method for a smart terminal, comprising:

[0006] The acceleration of the smart terminal is obtained, and a feature vector is generated based on the acceleration; the feature vector includes frequency domain feature values ​​and time domain feature values; the frequency domain feature values ​​include the main frequency and the amplitude corresponding to the main frequency; the time domain feature values ​​include variance, mean, and slope.

[0007] A motion pattern classifier is trained based on feature vectors, and the trained motion pattern classifier is used to identify the current pedestrian motion pattern and determine the pedestrian motion state; the pedestrian motion state includes stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0008] When the smart terminal sends a positioning request, it acquires the RTCM ephemeris data and GNSS raw observations received by the smart terminal, and calculates the satellite ephemeris using the RTCM ephemeris data; the GNSS raw observations include pseudorange observations and carrier-to-noise ratio.

[0009] The raw GNSS observations are smoothed to generate smoothed GNSS observations;

[0010] The smoothed GNSS observations are quality controlled using the RTCM ephemeris data to determine the integrated GNSS observations.

[0011] Based on the precise single-point positioning model, according to the pedestrian's motion state, the integrated GNSS observations, and the satellite ephemeris, the Kalman filter algorithm is used to iteratively process the initial coordinates of the smart terminal and output the current position coordinates of the pedestrian.

[0012] Optionally, generating the feature vector based on the acceleration specifically includes:

[0013] Gravitational acceleration is obtained by separating it from the acceleration;

[0014] Determine the linear acceleration based on the gravitational acceleration;

[0015] Extract the horizontal and vertical components of the linear acceleration;

[0016] Feature vectors are generated by extracting and selecting features based on the horizontal and vertical components.

[0017] Optionally, the smoothing process of the raw GNSS observations to generate smoothed GNSS observations specifically includes:

[0018] Obtain Doppler observations;

[0019] The pseudorange observations are smoothed using the Doppler observations to generate smoothed pseudorange observations.

[0020] The GNSS raw observations with gross errors removed are the raw GNSS observations.

[0021] The original GNSS observations after removal and the smoothed pseudorange observations are used to generate smoothed GNSS observations.

[0022] Optionally, the step of using the RTCM ephemeris data to perform quality control on the smoothed GNSS observations to determine the integrated GNSS observations specifically includes:

[0023] The ionosphere is obtained using the RTCM ephemeris data, and the ionosphere is accumulated for error, cycle slip detection is performed, and a threshold judgment mechanism is established.

[0024] When the smoothed pseudorange observation does not meet the threshold judgment mechanism, the smoothing window is reset, a new smoothed pseudorange observation is generated, and the new smoothed pseudorange observation and the removed original GNSS observation are integrated to generate an integrated GNSS observation.

[0025] When the smoothed pseudorange observations satisfy the threshold judgment mechanism, the smoothed GNSS observations are used as the integrated GNSS observations.

[0026] Optionally, the step of smoothing the pseudorange observations using the Doppler observations to generate smoothed pseudorange observations further includes:

[0027] Calculate the residuals of the smoothed pseudorange observations and verify the residuals.

[0028] Optionally, the step of acquiring the RTCM ephemeris data and raw GNSS observations received by the smart terminal, and calculating the satellite ephemeris using the RTCM ephemeris data, specifically includes:

[0029] Obtain the raw GNSS observation values ​​received by the intelligent terminal;

[0030] The RTCM ephemeris data is obtained through the NTRIP protocol within the smart terminal, and the satellite ephemeris is downloaded using FTP mode.

[0031] Optionally, the step of using a precise single-point positioning model, based on the pedestrian's motion state, the integrated GNSS observations, and the satellite ephemeris, iteratively processes the initial coordinates of the smart terminal using a Kalman filter algorithm to output the current position coordinates of the pedestrian, and then further includes:

[0032] The location coordinates are displayed on the smart terminal.

[0033] A smart terminal single-point positioning system, comprising:

[0034] A feature vector generation module is used to acquire the acceleration of the smart terminal and generate a feature vector based on the acceleration; the feature vector includes frequency domain feature values ​​and time domain feature values; the frequency domain feature values ​​include the main frequency and the amplitude corresponding to the main frequency; the time domain feature values ​​include variance, mean, and slope;

[0035] The pedestrian motion state determination module is used to train a motion pattern classifier based on feature vectors, and to use the trained motion pattern classifier to identify the current pedestrian motion pattern and determine the pedestrian motion state; the pedestrian motion state includes stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0036] The satellite ephemeris calculation module is used to obtain the RTCM ephemeris data and raw GNSS observations received by the smart terminal when the smart terminal sends a positioning request, and to calculate the satellite ephemeris using the RTCM ephemeris data; the raw GNSS observations include pseudorange observations and carrier-to-noise ratio.

[0037] A smoothing module is used to smooth the raw GNSS observations and generate smoothed GNSS observations.

[0038] An integration module is used to perform quality control on the smoothed GNSS observations using the RTCM ephemeris data, and to determine the integrated GNSS observations.

[0039] The current pedestrian position coordinate output module is used to output the current pedestrian position coordinates based on the precise single-point positioning model, according to the pedestrian's motion state, the integrated GNSS observation value, and the satellite ephemeris, by using the Kalman filter algorithm to iteratively process the initial coordinates of the smart terminal.

[0040] An electronic device includes a memory and a processor, wherein the memory stores a computer program and the processor runs the computer program to enable the electronic device to perform the above-described smart terminal single-point positioning method.

[0041] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described single-point positioning method for a smart terminal.

[0042] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides a single-point positioning method, system and product for intelligent terminals, which constrains pedestrian trajectories by pedestrian movement patterns, determines pedestrian movement states, effectively improves the convergence accuracy and time of Kalman filtering, and optimizes the raw GNSS observations of intelligent terminals (mobile phones) through a dedicated mobile phone quality control scheme, thereby improving the positioning accuracy of intelligent terminals. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of the single-point positioning method for intelligent terminals provided by the present invention;

[0045] Figure 2 This is a structural diagram of the intelligent terminal single-point positioning system provided by the present invention;

[0046] Figure 3 This is a flowchart of a single-point positioning method using a mobile phone as an example, provided by the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The purpose of this invention is to provide a single-point positioning method, system, and product for smart terminals, which can improve the positioning accuracy of smart terminals.

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] The Chinese names and full English names of some of the English abbreviations involved in this invention are as follows:

[0051] NTRIP: Networked Transport of RTCM via Internet Protocol;

[0052] RTCM: Radio Technology Committee of Marine, an international standards organization.

[0053] FTP: File Transfer Protocol;

[0054] API: Application Programming Interface;

[0055] GPS: Global Positioning System;

[0056] BDS: BeiDou Navigation Satellite System.

[0057] Example 1

[0058] Figure 1 The flowchart of the single-point positioning method for smart terminals provided by the present invention is as follows: Figure 1 As shown, a single-point positioning method for a smart terminal includes:

[0059] Step 101: Obtain the acceleration of the smart terminal and generate a feature vector based on the acceleration; the feature vector includes frequency domain feature values ​​and time domain feature values; the frequency domain feature values ​​include the main frequency and the amplitude corresponding to the main frequency; the time domain feature values ​​include variance, mean and slope.

[0060] In practical applications, step 101 specifically includes: separating gravitational acceleration from the acceleration; determining linear acceleration based on the gravitational acceleration; extracting the horizontal and vertical components of the linear acceleration; and performing feature extraction and selection based on the horizontal and vertical components to generate a feature vector.

[0061] In practical applications, the raw data (i.e., the raw acceleration) is processed using a low-pass filter to reduce noise; the processed data is denoted as A1. To obtain the linear acceleration of the human body to visually represent its motion state, gravitational acceleration should be separated from the linear acceleration obtained from the inertial sensor. Some smartphones already contain gravity sensors; if the phone does not have a gravity sensor, it can be obtained using the following methods:

[0062] At the start of data acquisition, hold the smartphone still for several seconds and take the average of the three-axis acceleration values ​​acquired during this period as the gravity vector.

[0063] Considering the phone's posture, the horizontal and vertical components of the linear acceleration in A1 are extracted and denoted as data A2. The specific calculation method is as follows:

[0064] remember For linear acceleration, If the acceleration is due to gravity, then the vertical component of the linear acceleration in A2 can be expressed as: The horizontal component can be represented as

[0065] Based on the processed A2 data, an appropriate time length is selected for feature extraction and selection. Common time-domain features include variance, mean, and slope. Common frequency-domain features include the dominant frequency and its corresponding amplitude.

[0066] Step 102: Train a motion pattern classifier based on feature vectors, and use the trained motion pattern classifier to identify the current pedestrian motion pattern and determine the pedestrian motion state; the pedestrian motion state includes stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0067] In practical applications, after obtaining the location service request, real-time parameters are obtained through the sensors of the mobile phone, and the current pedestrian movement pattern is judged based on the trained motion pattern classifier, and the pedestrian movement status is output, including stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0068] Step 103: When the smart terminal sends a positioning request, the system obtains the RTCM ephemeris data and GNSS raw observations received by the smart terminal, and calculates the satellite ephemeris using the RTCM ephemeris data; the GNSS raw observations include pseudorange observations and carrier-to-noise ratio.

[0069] In practical applications, step 103 specifically includes: acquiring the raw GNSS observation values ​​received by the smart terminal; acquiring RTCM ephemeris data through the NTRIP protocol within the smart terminal; and downloading the satellite ephemeris using FTP mode.

[0070] In practical applications, sensor parameters and raw GNSS observations are obtained through the mobile phone's built-in API interface to calculate pseudorange, carrier wave, and Doppler values. Sensor parameters are input to a motion pattern classifier, which, based on a pre-trained classifier, determines the pedestrian's motion state for the current epoch. Sensor parameters are feature vectors generated based on acceleration. Ephemeris parameters, pedestrian motion parameters, and raw GNSS observations are integrated and stored. If the number of satellites involved in the calculation is insufficient, approximate coordinates obtained from the mobile phone API will be output. If RTCM ephemeris data cannot be obtained via the NTRIP protocol, precise satellite ephemeris data will be downloaded using FTP. If no precise ephemeris data is available, pseudorange point positioning results will be calculated using broadcast ephemeris and output. The satellite systems involved in the calculation include GPS and BDS; the user can choose which system to use, and inter-system biases will be considered.

[0071] Step 104: Smooth the raw GNSS observations to generate smoothed GNSS observations.

[0072] In practical applications, step 104 specifically includes: acquiring Doppler observations; smoothing the pseudorange observations using the Doppler observations to generate smoothed pseudorange observations; removing gross errors from the original GNSS observations; and generating smoothed GNSS observations from the removed original GNSS observations and the smoothed pseudorange observations.

[0073] The process of smoothing the pseudorange observations using the Doppler observations to generate smoothed pseudorange observations further includes: calculating the residuals of the smoothed pseudorange observations and verifying the residuals.

[0074] Step 105: Use the RTCM ephemeris data to perform quality control on the smoothed GNSS observations and determine the integrated GNSS observations.

[0075] In practical applications, step 105 specifically includes: acquiring the ionosphere using the RTCM ephemeris data, accumulating errors in the ionosphere, performing cycle slip detection, and establishing a threshold judgment mechanism; when the smoothed pseudorange observation does not meet the threshold judgment mechanism, resetting the smoothing window, generating new smoothed pseudorange observations, and integrating the new smoothed pseudorange observations with the discarded original GNSS observations to generate integrated GNSS observations; when the smoothed pseudorange observation meets the threshold judgment mechanism, using the smoothed GNSS observation as the integrated GNSS observation.

[0076] Step 106: Based on the precise single-point positioning model, according to the pedestrian's motion state, the integrated GNSS observation values, and the satellite ephemeris, the Kalman filter algorithm is used to iteratively process the initial coordinates of the smart terminal and output the current position coordinates of the pedestrian.

[0077] In practical applications, based on the Kalman filter module, the smoothed pseudorange value and related parameters are called, and the precise satellite data calculated by RTCM ephemeris is used. The approximate coordinates called by the mobile API are used as the initial iteration coordinates for iteration. The precise satellite data is the satellite ephemeris. The related parameters include pseudorange value, carrier phase value and ionospheric value, etc.

[0078] Among them, the precise single-point positioning model adopts a non-differential and non-combined model, and sets pseudorange clock error and carrier clock error separately. In the precise single-point positioning model with non-differential and non-combined parameters, the carrier and pseudorange clock error are set separately due to the mismatch.

[0079] In practical applications, step 106 is followed by displaying the location coordinates on the smart terminal.

[0080] In practical applications, a residual control module is used to verify the residuals of the smoothed pseudorange observations, thereby improving the final accuracy. Specifically, the residual control module is set up after the Karl filter calculation, and the residual value is the difference between the pseudorange obtained by substituting the coordinates into the filtered calculation and the pseudorange observation.

[0081] In practical applications, mobile phone ambiguity will be directly solved using floating-point ambiguity.

[0082] In practical applications, the coordinate data calculated based on the interactive module will be displayed on the mobile phone interface, and users can choose the map visualization display themselves. Multiple parameters, including pedestrian movement detection, positioning calculation methods, and the NTRIP protocol interface, can be visually adjusted.

[0083] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a smart terminal single-point positioning system is provided below.

[0084] Example 2

[0085] Figure 2 The following is a structural diagram of the intelligent terminal single-point positioning system provided by the present invention, as shown in the figure. Figure 2 As shown, a smart terminal single-point positioning system includes:

[0086] The feature vector generation module 201 is used to acquire the acceleration of the smart terminal and generate a feature vector based on the acceleration; the feature vector includes frequency domain feature values ​​and time domain feature values; the frequency domain feature values ​​include the main frequency and the amplitude corresponding to the main frequency; the time domain feature values ​​include variance, mean and slope.

[0087] The pedestrian motion state determination module 202 is used to train a motion pattern classifier based on feature vectors, and to use the trained motion pattern classifier to identify the current pedestrian motion pattern and determine the pedestrian motion state; the pedestrian motion state includes stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0088] The satellite ephemeris calculation module 203 is used to obtain the RTCM ephemeris data and raw GNSS observations received by the smart terminal when the smart terminal sends a positioning request, and to calculate the satellite ephemeris using the RTCM ephemeris data; the raw GNSS observations include pseudorange observations and carrier-to-noise ratio.

[0089] The smoothing module 204 is used to smooth the raw GNSS observations to generate smoothed GNSS observations.

[0090] The integration module 205 is used to perform quality control on the smoothed GNSS observations using the RTCM ephemeris data, and to determine the integrated GNSS observations.

[0091] The current pedestrian position coordinate output module 206 is used to output the current pedestrian position coordinates based on the precise single-point positioning model, according to the pedestrian's motion state, the integrated GNSS observation value, and the satellite ephemeris, by using the Kalman filter algorithm to iteratively process the initial coordinates of the smart terminal.

[0092] Example 3

[0093] Figure 3 The flowchart of the single-point positioning method using a mobile phone as an example provided by the present invention is as follows: Figure 3 As shown.

[0094] S1. Data acquisition is performed based on the phone's built-in sensors. Since the parameters acquired by the smartphone's MEMS inertial sensor contain some noise, noise reduction is performed using a low-pass filter.

[0095] Based on the mobile phone sensor parameters collected over a certain period of time, feature extraction and feature selection are performed. The acceleration values ​​acquired by the sensors include gravitational acceleration; the linear acceleration is calculated from the gravitational acceleration value obtained by the gravity sensor. Due to the phone's orientation, only the vertical and horizontal components of the extracted linear acceleration are used as parameters. Based on these parameters, frequency domain feature values ​​and time domain feature values ​​are calculated and used as feature vectors.

[0096] After obtaining the location service request, the system acquires real-time parameters through the sensors on the mobile phone, and judges the current pedestrian movement pattern based on the trained motion pattern classifier, outputting the pedestrian movement status, including stationary state, walking state, turning state, going upstairs state, going downstairs state, going up elevator state, and going down elevator state.

[0097] S2. Real-time acquisition of RTCM ephemeris data via the NTRIP protocol to calculate precise satellite data.

[0098] Obtain raw GNSS observations through the phone's built-in API interface.

[0099] To address the characteristics of raw observations from smart terminals, a specialized data quality control method is designed. At the quality control end, Doppler observations acquired by the mobile phone are used to smooth pseudorange, and ionospheric cumulative error, cycle slip, and gross errors are detected. A threshold judgment mechanism is established to filter qualified observation data. When the threshold judgment mechanism is not met, the smoothing window is reset, and the smoothed pseudorange observations and related parameters are integrated and stored. These related parameters include pseudorange values, carrier phase values, and ionospheric values.

[0100] S3. Based on traditional PPP technology, Kalman filtering is used to iteratively update the input initial coordinates. The parameters of the Kalman state update matrix are constrained by the pre-input pedestrian motion state, and a random weighting model based on signal-to-noise ratio is used for formula calculation.

[0101] Example 4

[0102] This invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the smart terminal single-point positioning method of Embodiment 1.

[0103] In practical applications, the aforementioned electronic devices can be servers.

[0104] In practical applications, electronic devices include: at least one processor, memory, bus, and communication interface.

[0105] The processor, communication interface, and memory communicate with each other via a communication bus.

[0106] A communication interface is used to communicate with other devices.

[0107] The processor is used to execute programs, specifically the methods described in the above embodiments.

[0108] Specifically, the program may include program code, which includes computer operation instructions.

[0109] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The electronic device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0110] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0111] Based on the description of the above embodiments, this application provides a storage medium storing computer program instructions thereon, which can be executed by a processor to implement the methods described in any embodiment.

[0112] The smart terminal point positioning system provided in this application exists in various forms, including but not limited to:

[0113] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include: smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones, etc.

[0114] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access capabilities. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.

[0115] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0116] (4) Other electronic devices with data interaction functions.

[0117] Specific embodiments of the subject matter have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0118] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0119] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0124] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0125] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, and CD-ROM.

[0126] Digital multifunction optical disc (DVD) or other optical storage, magnetic cassette tape, magnetic magnetic disk storage or other magnetic storage devices

[0127] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0129] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific transactions or implement specific abstract data types. This application can also be practiced in distributed computing environments where transactions are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0131] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0132] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for single point positioning of a smart terminal, characterized in that, The method comprises the following steps: obtaining the acceleration of the intelligent terminal and generating a feature vector according to the acceleration; the feature vector comprises frequency domain characteristic values and time domain characteristic values; the frequency domain characteristic values comprise a main frequency and an amplitude corresponding to the main frequency; the time domain characteristic values comprise a variance, a mean value and a slope; training a motion mode classifier according to the feature vector and identifying the current motion mode of the pedestrian by using the trained motion mode classifier to determine the motion state of the pedestrian; the motion state of the pedestrian comprises a stationary state, a walking state, a turning state, an up-stairs state, a down-stairs state, an up-elevator state and a down-elevator state; when the intelligent terminal sends a positioning request, obtaining RTCM ephemeris data and GNSS original observation values received by the intelligent terminal, and calculating satellite ephemeris by using the RTCM ephemeris data; the GNSS original observation values comprise pseudo-range observation values and carrier-to-noise ratio; performing smoothing processing on the GNSS original observation values to generate smoothed GNSS observation values; performing quality control on the smoothed GNSS observation values by using the RTCM ephemeris data to determine integrated GNSS observation values; based on a precise point positioning model, performing iterative processing on the initial coordinates of the intelligent terminal according to the motion state of the pedestrian, the integrated GNSS observation values and the satellite ephemeris by using a Kalman filtering algorithm to output the position coordinates of the current pedestrian.

2. The intelligent terminal single point positioning method according to claim 1, characterized in that, The method of generating a feature vector according to the acceleration comprises the following steps: separating gravity acceleration from the acceleration; determining linear acceleration according to the gravity acceleration; extracting the horizontal component and the vertical component of the linear acceleration; performing feature extraction and selection according to the horizontal component and the vertical component to generate a feature vector.

3. The intelligent terminal single point positioning method according to claim 1, wherein, The method of performing smoothing processing on the GNSS original observation values to generate smoothed GNSS observation values comprises the following steps: obtaining Doppler observation values; performing smoothing processing on the pseudo-range observation values by using the Doppler observation values to generate smoothed pseudo-range observation values; eliminating GNSS original observation values with gross errors in the GNSS original observation values; generating smoothed GNSS observation values from the eliminated GNSS original observation values and the smoothed pseudo-range observation values.

4. The intelligent terminal single point positioning method according to claim 3, wherein, The method of performing quality control on the smoothed GNSS observation values by using the RTCM ephemeris data to determine integrated GNSS observation values comprises the following steps: obtaining an ionosphere by using the RTCM ephemeris data, performing cumulative error on the ionosphere, performing cycle slip detection and setting up a threshold judgment mechanism; when the smoothed pseudo-range observation values do not satisfy the threshold judgment mechanism, resetting a smoothing window, generating new smoothed pseudo-range observation values, integrating the new smoothed pseudo-range observation values and the eliminated GNSS original observation values to generate integrated GNSS observation values; when the smoothed pseudo-range observation values satisfy the threshold judgment mechanism, taking the smoothed GNSS observation values as the integrated GNSS observation values.

5. The intelligent terminal single point positioning method according to claim 3, wherein, The method of performing smoothing processing on the pseudo-range observation values by using the Doppler observation values to generate smoothed pseudo-range observation values further comprises the following steps: Calculate the residual of the smoothed pseudo-range observation value, and check the residual.

6. The intelligent terminal single point positioning method according to claim 1, wherein, The method comprises the following steps: The GNSS raw observation value received by the intelligent terminal is acquired. The RTCM ephemeris data is acquired through the NTRIP protocol in the intelligent terminal, and the satellite ephemeris is downloaded in FTP mode.

7. The intelligent terminal single point positioning method according to claim 1, wherein, The initial coordinate of the intelligent terminal is iteratively processed based on the precise point positioning model, the motion state of the pedestrian, the integrated GNSS observation value and the satellite ephemeris, and the position coordinate of the current pedestrian is output. The position coordinate is displayed on the intelligent terminal.

8. An intelligent terminal single point positioning system, characterized by The method comprises the following steps: The feature vector generation module is used to acquire the acceleration of the intelligent terminal and generate a feature vector based on the acceleration; The feature vector comprises frequency domain characteristic values and time domain characteristic values; The frequency domain characteristic values comprise a main frequency and an amplitude corresponding to the main frequency; and the time domain characteristic values comprise a variance, a mean value and a slope; The motion state determination module is used to train a motion mode classifier based on the feature vector, and identify the motion mode of the current pedestrian by using the trained motion mode classifier to determine the motion state of the pedestrian; The motion state of the pedestrian comprises a stationary state, a walking state, a turning state, an up-stairs state, a down-stairs state, an up-elevator state and a down-elevator state; The satellite ephemeris calculation module is used to acquire the RTCM ephemeris data and the GNSS raw observation value received by the intelligent terminal when the intelligent terminal sends a positioning request, and calculate the satellite ephemeris by using the RTCM ephemeris data; the GNSS raw observation value comprises a pseudo-range observation value and a carrier-to-noise ratio; The smoothing module is used to perform smoothing processing on the GNSS raw observation value to generate a smoothed GNSS observation value; The integration module is used to perform quality control on the smoothed GNSS observation value by using the RTCM ephemeris data to determine an integrated GNSS observation value; The position coordinate output module is used to iteratively process the initial coordinate of the intelligent terminal based on the precise point positioning model, the motion state of the pedestrian, the integrated GNSS observation value and the satellite ephemeris by using a Kalman filtering algorithm, and output the position coordinate of the current pedestrian.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the intelligent terminal single point positioning method in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer program is stored in the memory and is executed by the processor to implement the intelligent terminal single point positioning method in any one of claims 1-6.

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