Positioning method and apparatus, electronic device, and storage medium

Abstract

The present application provides a positioning method and apparatus, an electronic device, and a storage medium, capable of being applied to the field of positioning, and used for improving positioning accuracy and stability. The method comprises: acquiring observation data for each satellite among multiple satellites, wherein the observation data for each satellite comprises a Doppler observation quantity; on the basis of the Doppler observation quantities of the multiple satellites, determining a set of inter-satellite single-difference Doppler residuals and the movement speed of a mobile terminal; on the basis of the observation data for the multiple satellites, a first filter determining a first positioning result at a first positioning moment; on the basis of at least one of the set of inter-satellite single-difference Doppler residuals and the movement speed, determining whether the mobile terminal is in a low-speed motion state at the first positioning moment; and when the mobile terminal is in the low-speed motion state at the first positioning moment, a second filter using the first positioning result to determine a second positioning result at the first positioning moment.

Description

Positioning method, device, electronic device and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 22, 2024, with application number 202410340135.6 and application name “Mobile terminal positioning method, device, electronic device and storage medium”. The entire contents of the application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of satellite navigation technology, and in particular to a positioning method, device, electronic device and storage medium. Background Art

[0003] The Global Navigation Satellite System (GNSS) is an airborne radio navigation and positioning system that can provide all-weather three-dimensional coordinates, velocity, and time information for objects anywhere on the Earth's surface or in near-Earth space. It is widely used in navigation, communications, personnel detection, consumer entertainment, surveying and mapping, timing, vehicle management, and automotive navigation and information services.

[0004] In GNSS-based positioning, real-time kinematic (RTK) differential technology is typically used for positioning. This technology calculates the mobile terminal's positioning results in real time based on carrier phase observation data received by satellite positioning equipment installed on mobile terminals (such as mobile phones and vehicle-mounted terminals). However, traditional RTK differential technology often uses a Kalman filter with a relatively simplified state model (such as a uniform linear motion model) when performing positioning solutions. As a result, when the mobile terminal accelerates from a stationary state to a low-speed state, or decelerates from a non-low-speed state to a low-speed state, the RTK-calculated positioning results are easily unstable due to inaccurate state models, making it difficult to meet actual positioning needs. Summary of the Invention

[0005] Embodiments of the present application provide a positioning method, apparatus, electronic device, and storage medium for improving the positioning accuracy and stability of a mobile terminal.

[0006] In one aspect, an embodiment of the present application provides a positioning method, including:

[0007] Acquiring observation data for each of the plurality of satellites, wherein the observation data for each satellite includes a Doppler observation;

[0008] Based on the Doppler observations of the plurality of satellites, a set of inter-satellite single-difference Doppler residuals and a motion speed of the mobile terminal are determined, wherein each inter-satellite single-difference Doppler residual represents: a residual between Doppler observation equations of two different satellites;

[0009] Determining a first positioning result at a first positioning moment based on the observation data of the plurality of satellites by a first filter, wherein the first filter is a filter using a first state transfer matrix representing a non-low-speed motion state;

[0010] determining, based on at least one of the set of inter-satellite single-difference Doppler residuals and the movement speed, whether the mobile terminal is in a low-speed movement state at the first positioning moment;

[0011] When the mobile terminal is in a low-speed motion state at the first positioning moment, a second filter uses the first positioning result to determine a second positioning result at the first positioning moment. The second filter is a filter that uses a second state transfer matrix that characterizes the low-speed motion state.

[0012] On the other hand, an embodiment of the present application provides a positioning device, including:

[0013] an acquisition module, configured to acquire observation data for each of a plurality of satellites, wherein the observation data for each satellite includes a Doppler observation; and determine a set of inter-satellite single-difference Doppler residuals and a motion speed of the mobile terminal based on the Doppler observations for the plurality of satellites, wherein each inter-satellite single-difference Doppler residual represents: a residual between Doppler observation equations for two different satellites;

[0014] a positioning module, which determines a first positioning result at a first positioning moment by using a first filter based on the observation data of the plurality of satellites, wherein the first filter is a filter using a first state transfer matrix representing a non-low-speed motion state;

[0015] a detection module, configured to determine whether the mobile terminal is in a low-speed motion state at the first positioning moment based on at least one of the set of inter-satellite single-difference Doppler residuals and the motion speed;

[0016] Among them, the positioning module is also used to, when the mobile terminal is in a low-speed motion state at the first positioning moment, use the first positioning result by the second filter to determine the second positioning result at the first positioning moment, and the second filter is a filter that uses a second state transfer matrix that characterizes the low-speed motion state.

[0017] On the other hand, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of any of the above-mentioned mobile terminal positioning methods are implemented.

[0018] On the other hand, an embodiment of the present application provides a computer-readable storage medium having computer-executable instructions stored thereon. When the computer-executable instructions are executed by an electronic device, the steps of any of the above-mentioned mobile terminal positioning methods are implemented.

[0019] On the other hand, an embodiment of the present application provides a computer program product, comprising a computer program, which implements the steps of any of the above-mentioned mobile terminal positioning methods when executed by an electronic device. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] FIG1 is a schematic diagram of a positioning result provided in an embodiment of the present application;

[0022] FIG2 is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0023] FIG3 is a logic diagram of an overall solution of a positioning method provided in an embodiment of the present application;

[0024] FIG4 is a flowchart of an implementation of a positioning method provided in an embodiment of the present application;

[0025] FIG5 is a flow chart of a method for determining a set of Doppler residuals provided in an embodiment of the present application;

[0026] FIG6 is a flow chart of a method for determining the movement speed of a mobile terminal provided in an embodiment of the present application;

[0027] FIG7 is a schematic diagram of a positioning process of a dual filter after entering a low-speed motion according to an embodiment of the present application;

[0028] FIG8 is a flow chart of a method for positioning a mobile terminal after entering low-speed motion according to an embodiment of the present application;

[0029] FIG9 is a schematic diagram of a positioning process of a dual filter after exiting low-speed motion according to an embodiment of the present application;

[0030] FIG10 is a flow chart of a method for positioning a mobile terminal after exiting low-speed motion according to an embodiment of the present application;

[0031] FIG11 is a comparison diagram of positioning results of the present application and related technologies provided in an embodiment of the present application;

[0032] FIG12A is a schematic diagram of an application of a lane-level navigation scenario provided by an embodiment of the present application;

[0033] FIG12B is a schematic diagram of another lane-level navigation application scenario provided by an embodiment of the present application;

[0034] FIG12C is a schematic diagram of an application of a green wave navigation scenario provided by an embodiment of the present application;

[0035] FIG13 is a structural diagram of a mobile terminal positioning device provided in an embodiment of the present application;

[0036] FIG14 is a structural diagram of an electronic device provided in an embodiment of the present application;

[0037] FIG15 is a structural diagram of a mobile terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of the technical solutions of this application, but not all of them. Based on the embodiments described in this application document, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the technical solutions of this application.

[0039] For ease of understanding, the terms involved in the embodiments of this application are explained below.

[0040] Global Navigation Satellite System (GNSS): A space-based radio navigation and positioning system that provides all-weather, three-dimensional coordinate, velocity, and time information for any location on Earth's surface or in near-Earth space. GNSS includes satellite navigation systems such as the Global Positioning System (GPS), the Beidou Navigation Satellite System (BDS), the Global Orbiting Navigation Satellite System (GLONASS), and the Galileo Navigation Satellite System (GALILEO). GNSS is widely used in navigation, communications, personnel monitoring, consumer entertainment, surveying and mapping, timing, vehicle management, and automotive navigation and information services. Its overall development trend is to provide high-precision positioning and navigation services for real-time applications.

[0041] The Continuously Operating Reference Stations (CORS) system is a product of the comprehensive and in-depth integration of satellite positioning technology, computer network technology, digital communication technology, and other advanced technologies. The CORS system consists of five components: a base station network, a data processing center, a data transmission system, a positioning and navigation data broadcast system, and a user application system. Each base station is connected to the detection and analysis center through a data transmission system, forming a dedicated network.

[0042] Virtual Reference Station (VRS) differential data: This data is generated using virtual reference station technology and multiple CORS systems. It is provided to mobile terminals for differential positioning. Using VRS differential data, mobile terminals can leverage existing reference stations for high-precision positioning without having to establish their own base stations.

[0043] A satellite positioning device (also known as a satellite signal receiver) is an electronic device used to receive and process satellite signals and measure the geometric distance between a mobile terminal and a satellite (pseudorange observations) and the Doppler effect of satellite signals (Doppler observations). A satellite positioning device typically includes modules such as an antenna, a satellite signal reception loop, and baseband signal processing. A mobile terminal integrated with a satellite positioning device calculates the current location coordinates of the mobile terminal based on the pseudorange and Doppler observations. Satellite positioning devices are widely used in map navigation, surveying and mapping, location-based services, and deep space exploration, such as smartphone map navigation and high-precision geodesy. The observation data determined by a satellite positioning device includes pseudorange, pseudorange rate, and accumulated delta range (ADR). Pseudorange is the geometric distance from the satellite to the satellite positioning device, pseudorange rate is the Doppler effect caused by the relative motion between the satellite positioning device and the satellite, and ADR is the change in the geometric distance from the satellite to the satellite positioning device.

[0044] Pseudorange: This is the approximate distance between a ground receiver (e.g., a satellite positioning device installed on a mobile terminal) and a satellite during satellite positioning. Assuming the satellite clock and receiver clock are strictly synchronized, the propagation time of the signal can be calculated by multiplying the time it is transmitted by the satellite signal and the time it is received by the receiver. The distance between the satellite and the ground can then be calculated by multiplying the propagation speed. However, due to the inevitable clock error between the two clocks, and the signal propagation is also affected by factors such as atmospheric refraction, the distance directly measured using this method is not the true distance from the satellite to the ground receiver. Therefore, this distance is called a pseudorange.

[0045] Differential GNSS: This method uses two or more GNSS receivers to simultaneously receive satellite signals and improve positioning accuracy by calculating the pseudorange differences between the receivers.

[0046] Real-Time Kinematic (RTK) differential positioning technology: It can be called real-time dynamic differential positioning technology, also known as carrier phase differential positioning technology. It is a technology that uses the observation data of the carrier phase of satellite signals received by the GNSS receiver for high-precision positioning. RTK differential positioning technology requires a base station with known precise coordinates and a mobile station. The base station calculates and sends correction numbers to the mobile station in real time. The mobile station calculates its own position in real time after receiving the correction numbers. RTK differential positioning technology is widely used in surveying, remote sensing, drones and other fields. Compared with pseudo-range differential positioning, RTK differential positioning can achieve centimeter-level positioning accuracy. In the embodiment of the present application, the mobile station refers to a mobile terminal.

[0047] The Extended Kalman Filter (EKF) is a nonlinear optimal estimation algorithm used to estimate the state of nonlinear systems. The EKF is an extension of the Kalman filter for nonlinear systems. The Kalman filter is a linear optimal estimation algorithm suitable for linear systems. When the system model is nonlinear, the EKF uses linearization techniques to approximate the nonlinear system to a linear system, then applies the Kalman filter for state estimation. The EKF has a wide range of applications in navigation, positioning, target detection, and other fields.

[0048] Weighted Least Squares (WLS): is a mathematical optimization method used to solve linear or nonlinear systems of equations containing noise and uncertainty. Compared to ordinary least squares, the WLS method assigns a weight to each observation during the solution process to indicate its reliability or accuracy. Observations with larger weights have a greater influence on the solution, while observations with smaller weights have a lower influence. The WLS method is widely used in measurement, data fitting, statistical analysis, and other fields. In GNSS positioning, the WLS method can be used to process the combination of multiple satellite signals to improve positioning accuracy and reliability.

[0049] Pseudorange observation: The approximate distance between a satellite signal receiver (such as a satellite positioning device) and a satellite during satellite positioning.

[0050] Doppler observation: The Doppler measurement value or Doppler count value of the radio signal sent by the satellite measured by the satellite signal receiver.

[0051] The Doppler observation equation is a mathematical model that describes the frequency change (Doppler shift) caused by the relative motion between the receiver (such as a mobile terminal) and the signal source (such as a satellite).

[0052] Carrier phase observation: the phase difference between the carrier signal or subcarrier signal transmitted by the satellite and the local oscillator signal of the satellite signal receiver.

[0053] Satellite-to-Earth distance: refers to the distance between the satellite and the Earth's surface, also known as satellite altitude.

[0054] Clock drift: the rate of change of clock error. The clock drift in the embodiments of the present application specifically relates to mobile device clock drift and satellite clock drift. Mobile terminal clock drift refers to the rate of change of satellite signal receiver clock error, and satellite clock drift refers to the rate of change of satellite clock error.

[0055] Epoch: The starting point of a period or event, or a reference date for a measurement system. To compare observations made at different times, it is necessary to specify the time at which the observations were made. This time is called an epoch.

[0056] Single difference: There are two types of single difference: inter-satellite single difference and inter-station single difference. Inter-satellite single difference refers to the difference between the observation equation of a single station and the satellites; inter-station single difference refers to the difference between the observation equations of the mobile station and the reference station for the same satellite.

[0057] Double difference: first perform inter-station single difference, then perform inter-satellite single difference.

[0058] The following is an overview of the design concepts of the embodiments of the present application.

[0059] GNSS-based mobile terminal positioning is increasingly used in daily life. For example, in map navigation scenarios, real-time positioning of the vehicle terminal is required to determine the vehicle's location; in food delivery scenarios, real-time positioning of the deliveryman's mobile phone is required to obtain the delivery time of the food.

[0060] At present, when related technologies use PKT differential positioning technology to solve the positioning of mobile terminals, the filters used mostly adopt a relatively simplified state model (such as a uniform linear motion model). In this way, when the mobile terminal enters or exits low-speed motion, it is easy to cause the positioning results calculated by RTK to be unstable due to inaccurate state models.

[0061] Take the low-speed driving scenario of the vehicle terminal in the map navigation scenario as an example, as shown in Figure 1, the black line is the road boundary, the light blue line is the road centerline, the vehicle travels from left to right and changes lanes to the outermost lane to drive slowly. The triangle is the solution result of the traditional RTK differential positioning technology for the positioning of the vehicle terminal on the vehicle. When the vehicle terminal is in low-speed motion, the positioning point is prone to drift in position, speed and heading, and the positioning stability is poor.

[0062] In view of this, the embodiments of the present application provide a mobile terminal positioning method, device, electronic device and storage medium for improving the accuracy and stability of RTK differential positioning. The method is based on the Doppler observations of multiple satellites respectively, and adopts the inter-satellite single difference Doppler residual method and the weighted least squares Doppler velocity measurement method to calculate a set of Doppler residuals and motion speeds respectively, so as to detect the current motion state together through a set of Doppler residuals and motion speeds, thereby improving the accuracy of the current motion state judgment. When it is detected that the current motion state meets the preset conditions for entering low-speed motion, the first filter and the second filter are used to determine the final positioning result in the process of solving the positioning result by the RTK differential positioning technology. Since the state transfer matrices used by the first filter and the second filter represent different motion models respectively, in this way, the two filters can be combined for positioning according to the low-speed motion state of the mobile terminal, thereby outputting a smooth and stable positioning result, effectively improving the positioning accuracy and stability of the mobile terminal in the RTK differential decomposition.

[0063] It should be noted that the low-speed motion in the embodiments of the present application has different definitions for different types of mobile terminals and can be flexibly adjusted according to actual needs in practical applications.

[0064] For example, when the mobile terminal is on an electric vehicle, low-speed movement can be defined as a speed less than 80m / min; when the mobile terminal is on a car, low-speed movement can be defined as a speed of 400m / min; when the mobile terminal is on a high-speed rail, low-speed movement can be defined as a speed less than 1km / min.

[0065] It is understandable that in the specific implementation of this application, the positioning of the mobile terminal involved, when applied to the methods or products of the following embodiments of this application, has obtained the permission or consent of the mobile terminal owner, and the collection, use and processing of relevant data comply with the laws, regulations and standards of relevant countries and regions.

[0066] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0067] As shown in FIG2 , which is a schematic diagram of an application scenario in an embodiment of the present application, the application scenario diagram includes a mobile terminal 210 , a reference station 220 , a GNSS 230 , and a server 240 .

[0068] In the embodiments of the present application, the mobile terminal 210 includes but is not limited to mobile phones, tablet computers, laptop computers, desktop computers, e-book readers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals and other devices, but is not limited thereto; the mobile terminal 210 installs and runs an application that supports positioning services, such as a navigation application; a satellite positioning device is installed on the mobile terminal 210, and the mobile terminal 210 can communicate with the GNSS230 through the satellite positioning device to obtain observation data (such as Doppler observations, carrier phase observations and pseudorange observations, etc.). The server 240 is the background server corresponding to the application of the positioning service. The server 240 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing services such as big data and artificial intelligence platforms.

[0069] Optionally, reference station 220 is a CORS system. Reference station 220 can communicate with GNSS 230 to obtain raw observation data corresponding to each satellite in GNSS 230. Reference station 220 can also communicate with server 240, transmitting the received raw observation data to server 240. Server 240 uses this raw observation data to calculate observation data between mobile terminal 210 and the satellites in real time and transmits it to mobile terminal 210.

[0070] In some embodiments, the mobile terminal 210 receives satellite signals through a receiving module and determines observation data for each satellite by measuring satellite signals from multiple satellites. The position, velocity, and other parameters of the mobile terminal are then estimated based on the observation data.

[0071] In some embodiments, server 240 can communicate with mobile terminal 210 and provide positioning-related computing services (e.g., differential service broadcasting, location reporting, etc.). In some embodiments, because server 240 can achieve centimeter-level positioning accuracy, server 240 is also referred to as a high-precision positioning server platform.

[0072] Optionally, the mobile terminal 210 and the server 240 may communicate via a communication network, which may be a wired network or a wireless network.

[0073] It should be noted that the mobile terminal positioning method in each embodiment of the present application can be executed by an electronic device, which can be a mobile terminal 210 or a server 240, that is, the method can be executed by the mobile terminal 210 or the server 240 alone, or can be executed jointly by the mobile terminal 210 and the server 240.

[0074] It should be noted that the above-mentioned mobile terminal 210, reference station 220, GNSS230 and server 240 are only examples. Other existing or future mobile terminals, reference stations, satellite clusters or servers that are applicable to this application should also be included in the scope of protection of this application and are included here by reference.

[0075] In an embodiment of the present application, when there are multiple servers, the multiple servers can be combined into a blockchain, and the servers are nodes on the blockchain; as disclosed in the embodiment of the present application, the mobile terminal positioning method, the relevant data involved can be stored on the blockchain, for example, Doppler observations, carrier phase observations, pseudoranges, etc.

[0076] In addition, the embodiments of the present application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, assisted driving, map navigation and other scenarios.

[0077] The following describes the mobile terminal positioning method provided by the exemplary embodiment of the present application in combination with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation of the present application is not limited in this respect.

[0078] As shown in Figure 3, it is a logic diagram of the overall solution of a mobile terminal positioning method provided by an embodiment of the present application. First, based on the Doppler observations received from multiple satellites respectively, the motion state of the mobile terminal is detected; then, different filters are used for positioning solution according to different motion states. Specifically, when the mobile terminal enters low-speed motion, the first filter and the second filter are used to jointly output the positioning result of the mobile terminal; when the mobile terminal exits low-speed motion, the positioning result of the mobile terminal is adaptively selected based on the comparison of the positioning results of the first filter and the second filter; when the mobile terminal maintains continuous non-low-speed motion, the first filter is directly used to output the positioning result. Among them, the state transfer matrices used by the first filter and the second filter respectively represent different motion models. In this way, a smooth and stable positioning result can be output according to the motion state of the mobile terminal, which effectively improves the positioning accuracy and stability of the mobile terminal in RTK differential decomposition.

[0079] It should be noted that the embodiments of the present application do not impose any restrictive requirements on the types of the first filter and the second filter, including but not limited to Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter, robust Kalman filter, etc.

[0080] 4 shows an implementation flow of a mobile terminal positioning method provided in an embodiment of the present application. Taking a mobile terminal as an example, the process mainly includes the following steps:

[0081] S401: Acquire observation data for each of a plurality of satellites, wherein the observation data for each satellite includes a Doppler observation. In some embodiments, the mobile terminal receives satellite signals via a receiving module and determines the observation data for each satellite by measuring the satellite signals from the plurality of satellites.

[0082] S402: Based on the Doppler observations of the multiple satellites, determine a set of inter-satellite single-difference Doppler residuals and a velocity of the mobile terminal, where each inter-satellite single-difference Doppler residual represents a residual between Doppler observation equations for two different satellites. In one embodiment, the set of inter-satellite single-difference Doppler residuals can be obtained using an inter-satellite single-difference Doppler residual method.

[0083] As shown in Figure 5, the process of obtaining a set of inter-satellite single-difference Doppler residuals includes:

[0084] S4021: Select a reference satellite from multiple satellites.

[0085] In practical applications, a reference satellite may be selected randomly or based on a preset rule.

[0086] For example, to avoid the impact of Doppler frequency shift on satellite signals, the satellite's movement speed needs to remain stable, and the circular orbit is a factor that ensures the constant satellite speed. Therefore, a satellite on a circular orbit can be selected as a reference satellite.

[0087] It should be noted that the embodiments of the present application do not impose any restrictive requirements on the selection rules of reference satellites. In addition to orbit-based selection, selection can also be based on the stability of satellite signals, the distance of the satellite from the earth, etc.

[0088] S4022: Determine a set of inter-satellite single-difference observation equations based on the Doppler observation equation for the reference satellite and the Doppler observation equations for the other satellites in the plurality of satellites, wherein each inter-satellite single-difference observation equation represents a difference between the Doppler observation equation for the reference satellite and the Doppler observation equation for one of the other satellites.

[0089] In practical applications, the Doppler observation equation between a reference satellite and a mobile terminal is subtracted from the Doppler observation equations between other satellites and the mobile terminal to obtain the corresponding inter-satellite single-difference observation equations. Each Doppler observation equation mathematically describes the frequency change (Doppler shift) caused by the relative motion between the mobile terminal and the satellite. Alternatively, the Doppler observation equation can be understood as an equation for calculating the mobile terminal's position and clock drift based on Doppler observations and satellite state parameters (such as carrier phase wavelength, satellite clock drift, and the speed of light in vacuum).

[0090] Specifically, the Doppler observation equations between the mobile terminal and the reference satellite and other satellites are as follows:

[0091] Where t represents the reference satellite, s represents other satellites, r represents the mobile terminal, f represents the frequency of the carrier phase, λ represents the wavelength of the carrier phase, ρ represents the rate of change of the satellite-to-earth distance, δ represents the clock drift, c represents the speed of light, ε represents the random error, and D represents the Doppler observation.

[0092] Subtracting Formula 1 from Formula 2 yields the inter-satellite single-difference observation equation, where each inter-satellite single-difference observation equation represents the difference in satellite state parameters between the reference satellite and other satellites. The equation is as follows:

[0093] in, represents the intersatellite single difference operator, denoted as It represents the rate of change of satellite-to-ground distance under the assumption that the mobile terminal speed is 0.

[0094] S4023: Determine a set of Doppler residual equations based on the set of inter-satellite single-difference observation equations.

[0095] In some embodiments, term transposition transformation is performed on each of the multiple inter-satellite single-difference observation equations obtained to obtain corresponding Doppler residual equations.

[0096] Specifically, the Doppler residual equation is:

[0097] in, represents the Doppler residual, which is used to characterize the deviation between Doppler observations from two different satellites.

[0098] S4024: Input the multiple Doppler observations into corresponding Doppler residual equations respectively to obtain the set of inter-satellite single-difference Doppler residuals.

[0099] By inputting the Doppler observation quantity into the Doppler residual equation, multiple inter-satellite single-difference Doppler residuals can be solved. These inter-satellite single-difference Doppler residuals constitute a group of inter-satellite single-difference Doppler residuals.

[0100] In one embodiment, the speed of the mobile terminal can be obtained by weighted least squares Doppler velocity measurement method. As shown in FIG6 , the process of obtaining the speed of the mobile terminal includes:

[0101] S4025: Establishing a Doppler observation equation group based on the multiple Doppler observation equations for the multiple satellites.

[0102] The Doppler observation equation group includes the Doppler observation equation between each satellite and the mobile terminal.

[0103] S4026: Based on the credibility of each of the multiple Doppler observations, set weights for corresponding Doppler observations respectively.

[0104] Among them, the higher the credibility of the Doppler observation, the greater the corresponding weight.

[0105] In practical applications, the embodiments of the present application do not impose any restrictive requirements on the measurement indicators of the Doppler observation reliability, and can be flexibly adjusted according to actual needs.

[0106] Optionally, the credibility of the Doppler observation can be measured according to the data variance. Specifically, the smaller the deviation between the Doppler observation and the mean of the Doppler observation, the higher the credibility.

[0107] Optionally, the credibility can also be measured based on the stability of the Doppler observation itself. Specifically, the smaller the error between the Doppler observation and other Doppler observations, the more stable the observation and the higher the credibility.

[0108] S4027: Solve the Doppler observation equations based on the weights of the multiple Doppler observation quantities to obtain the satellite-to-earth distance change rate.

[0109] By combining the Doppler observation equations between the mobile terminal and multiple satellites and using the weighted least squares method, the satellite-to-earth distance change rate can be solved.

[0110] S4028: Determine the movement speed based on the satellite-to-ground distance change rate.

[0111] The formula for the satellite-to-ground distance change rate is:

[0112] Among them, X, Y, and Z represent the distance of the mobile terminal in the direct coordinate system. By taking the derivative of the distance, the speed of the mobile terminal in the three axes can be obtained. The formula is expressed as follows:

[0113] It should be noted that the embodiment of the present application does not impose any restrictive requirements on the order of calculating a set of Doppler residuals and the movement speed of the mobile terminal. For example, the movement speed can be calculated first, and then a set of Doppler residuals can be calculated, or the two can be calculated in parallel.

[0114] S403: Based on the observation data of the multiple satellites, a first positioning result at a first positioning moment is determined by a first filter. The first filter is a filter that uses a first state transition matrix that represents a non-low-speed motion state. Here, the mobile terminal may perform positioning periodically. The first positioning moment is, for example, a moment at which a satellite signal is received. Here, the first filter is, for example, a Kalman filter.

[0115] S404: Based on at least one of the set of inter-satellite single-difference Doppler residuals and the movement speed, determine whether the mobile terminal is in a low-speed movement state at the first positioning moment; if so, execute S405; otherwise, execute S406.

[0116] After obtaining a set of inter-satellite single-difference Doppler residuals and movement speeds, by determining whether the value of at least one of the two is within a preset range, it is possible to detect whether the mobile terminal is in a low-speed movement state at the first positioning moment.

[0117] For example, when the circular error probable (CEP) of the set of inter-satellite single-difference Doppler residuals is less than a preset error threshold or the moving speed is less than a preset speed threshold, it is determined that the mobile terminal is in a low-speed motion state. Additionally, when the circular error probable of the set of inter-satellite single-difference Doppler residuals reaches the preset error threshold and the moving speed reaches the preset speed threshold, it is determined that the mobile terminal is in a non-low-speed motion state.

[0118] In one embodiment, the process of detecting the current motion state of a mobile terminal based on a set of inter-satellite single-difference Doppler residuals includes: calculating the circular error probable (CEP) of the set of inter-satellite single-difference Doppler residuals and comparing it with a preset error threshold. When the circular error probable is less than the preset error threshold, it is determined that the current motion state of the mobile terminal meets the preset condition for entering low-speed motion.

[0119] Optionally, the circular error probable can use the 68th percentile operator, and the formula is expressed as: m 68 |V| < T1 Formula 8

[0120] Where, V represents a set of Doppler residuals, T1 represents the preset error threshold, and m 68 means that more than 68% of the Doppler residuals are within the circle.

[0121] Optionally, the value range of T1 is 0.1 - 0.5 m / s.

[0122] In another embodiment, the process of detecting the current motion state of a mobile terminal based on the moving speed includes: if the moving speed of the mobile terminal is less than a preset speed threshold, it is determined that the mobile terminal is in low-speed motion, and the formula is expressed as: |v| < T2 Formula 9

[0123] Where, |v| represents the magnitude of the speed of the mobile terminal on the X, Y, and Z axes, and T2 represents the preset speed threshold.

[0124] Optionally, the value range of T2 is 0.2 - 0.5 m / s.

[0125] In an embodiment of the present application, Doppler observations of multiple satellites are utilized, employing the inter-satellite single-difference Doppler residual method and the weighted least squares Doppler velocity method to calculate a set of inter-satellite single-difference Doppler residuals and motion velocities, respectively. These are then compared with corresponding thresholds. When at least one of the inter-satellite single-difference Doppler residuals and motion velocities falls within a corresponding preset range, it is determined that the mobile terminal has entered a low-speed motion state. By jointly detecting a set of inter-satellite single-difference Doppler residuals and motion velocities, the current motion state of the mobile terminal can be accurately determined, allowing the use of corresponding filters for positioning and solution, improving positioning accuracy and stability.

[0126] S405: When the mobile terminal is in a low-speed motion state at the first positioning moment, a second filter uses the first positioning result to determine a second positioning result at the first positioning moment. In this way, the second positioning result can be used as the positioning result of the mobile terminal. The second filter uses a second state transition matrix that represents the low-speed motion state. The second filter is, for example, a Kalman filter.

[0127] When the circular error probability of a group of inter-satellite single-difference Doppler residuals is less than a preset error threshold, or the movement speed is less than a preset speed threshold, it is determined that the mobile terminal is currently entering low-speed movement, that is, the current movement state of the mobile terminal is a low-speed movement state.

[0128] As shown in Figure 7, assuming that the mobile terminal was in a non-low-speed motion state at the previous positioning moment before the first positioning moment, and entered low-speed motion at the first positioning moment, at this time, the mobile terminal starts the second filter based on the first filter, and the second filter performs state prediction and update based on the first positioning result of the first filter at the first positioning moment to obtain the positioning result of the mobile terminal at the first positioning moment, thereby realizing the joint positioning of the first filter and the second filter, and improving the positioning accuracy in the process of entering low-speed motion from non-low-speed motion.

[0129] In one embodiment, the joint positioning process of the first filter and the second filter is shown in FIG8 , and mainly includes the following steps:

[0130] S4051: Determine a first measurement value and a first state quantity of the second filter at the first positioning moment according to a first positioning result at the first positioning moment. For example, the first positioning result may include position and movement speed.

[0131] Taking the extended Kalman filter as an example, the first filter and the second filter include an observation model and a state model. The observation model mainly uses the first positioning result of the first filter at the first positioning moment, and uses the first positioning result of the first filter at the first positioning moment as the first measurement value of the second filter at the first positioning moment. The equation is expressed as follows: r = r1, v = v1 Formula 10

[0132] Wherein, r represents the position of the mobile terminal located by the first filter at the first positioning moment, and v1 represents the moving speed of the mobile terminal located by the first filter at the first positioning moment.

[0133] Based on the observation model of the second filter shown in Formula 10, the observation matrix of the second filter can be set to a unit matrix.

[0134] S4052: Based on the first state quantity, use the second state transfer matrix to perform state prediction to obtain a first predicted state quantity of the second filter at the first positioning moment; wherein the second state transfer matrix is ​​a unit matrix representing that the mobile terminal is stationary.

[0135] Taking the second filter as an extended Kalman filter as an example, its state model is set to a static model, that is, the first state transfer matrix is ​​a unit matrix that characterizes the stillness of the mobile terminal, denoted as F=I6, and the setting of the noise covariance matrix of the second filter is much smaller than the setting of the noise covariance matrix of the first filter (for example, the setting value of the elements on the diagonal of the matrix), such as 0.001 to 0.01 times the noise covariance matrix of the first filter. Here, the size of the noise covariance matrix is ​​proportional to the uncertainty of the noise. The operation of increasing the noise covariance matrix, for example, includes directly increasing the elements on the diagonal (that is, the variance of the noise in each dimension).

[0136] At the first positioning moment of entering low-speed motion, the second filter is started. At this time, the second filter itself has no historical positioning results. Therefore, the first positioning result of the first filter at the first positioning moment can be used as the first state quantity of the second filter, and the first state transfer matrix is ​​used for state prediction to obtain the first predicted state quantity of the second filter at the first positioning moment.

[0137] The first predicted state quantity includes the predicted position and predicted movement speed of the mobile terminal at the first positioning moment determined by the second filter.

[0138] S4053: Determine a second positioning result at the first positioning moment based on the first measurement value, the first predicted state quantity and the Kalman gain of the second filter.

[0139] Taking the second filter as the extended Kalman filter as an example, the Kalman gain formula is: K k+1 =P k+1 H T (HP k+1 H T +R) -1 Formula 11 P k+1 =FP k F T +Q Formula 12

[0140] Where P is the prediction covariance matrix, Q is the noise covariance matrix, F is the second state transition matrix, R is the noise matrix, H is the observation matrix, and K is the Kalman gain. P and K are updated as the second filter positioning duration changes.

[0141] After obtaining the Kalman gain of the second filter at the first positioning moment, the first predicted state quantity is updated based on the first measurement value. The update formula is as follows: k+1 =x k+1 +K k+1 (z-Hx k+1 ) Formula 13

[0142] Where x′ k+1 represents the target positioning result at the first positioning moment, x k+1 represents the first predicted state quantity, z represents the first measured value, H represents the observation matrix, K k+1 represents the Kalman filter gain.

[0143] S406: Use the first positioning result of the first filter as the positioning result of the mobile terminal.

[0144] When the mobile terminal is in a non-low-speed motion state, the first positioning result of the first filter is used as the positioning result of the mobile terminal. In other words, when the mobile terminal remains in a non-low-speed motion state and the second filter is never activated, the mobile terminal can directly use the current first positioning result of the first filter as the current positioning result of the mobile terminal.

[0145] In an embodiment of the present application, the state transition matrices used by the first filter and the second filter respectively characterize different motion models (i.e., low-speed and non-low-speed motion models). On this basis, when it is detected that the mobile terminal has entered a low-speed motion state, the first filter and the second filter are superimposed on the basis of the first filter to achieve joint positioning of the first filter and the second filter, and when the mobile terminal is in a non-low-speed motion state, the positioning result of the first filter is used as the positioning result of the mobile terminal. In summary, the embodiment of the present application can automatically adjust the positioning method according to the motion state of the mobile terminal, thereby improving the positioning accuracy.

[0146] In one embodiment, after the mobile terminal enters low-speed motion, it can also exit low-speed motion by accelerating, that is, the latest motion state of the mobile terminal is no longer low-speed motion. As shown in Figure 9, assuming that the mobile terminal is no longer in a low-speed motion state at the second positioning moment, at this time, there is no need to immediately terminate the second filter. Instead, the positioning results of the first filter and the second filter at the second positioning moment are compared, and the first filter and the second filter are adaptively switched based on the comparison results to determine the positioning result of the mobile terminal at the second positioning moment.

[0147] In one embodiment, the positioning process of adaptive switching between the first filter and the second filter is shown in FIG10 , and mainly includes the following steps:

[0148] S407: When it is determined that the mobile terminal has entered a non-low-speed motion state at a second positioning moment, the positioning results of the first filter and the second filter at the second positioning moment are compared to obtain a positioning difference between the first filter and the second filter. The second positioning moment can be understood as a switching moment from the low-speed motion state to the non-low-speed motion state.

[0149] When the mobile terminal enters low-speed motion, the second filter is activated. Therefore, between the first positioning moment and the second positioning moment, the second filter will continuously output positioning results. After the first positioning moment, the second filter has its own positioning result. Thus, when the second filter predicts the state of the second positioning moment, it can use the positioning result of the first filter at the second positioning moment as the measurement value of the second filter, combined with its own positioning result at the previous positioning moment, that is, using the positioning result of the second filter at the previous positioning moment as the state quantity, and using the first state transfer matrix for prediction. After the second positioning moment, the mobile terminal has exited low-speed motion and entered non-low-speed motion. To achieve smoothness of the positioning result of the mobile terminal from low-speed motion to non-low-speed motion, the positioning result of the second filter should gradually converge to the positioning result of the first filter.

[0150] After the mobile terminal exits the low-speed motion state, the positioning process of the second filter at a positioning moment is described:

[0151] S4071: Using the positioning result of the first filter at the current positioning time as the second measurement value of the second filter at the current positioning time. The current positioning time is one of the second positioning time and the subsequent positioning time.

[0152] S4072: Based on the positioning result of the second filter at the previous positioning moment, the second state transfer matrix is ​​used to perform state prediction to obtain a second predicted state value of the second filter at the current positioning moment.

[0153] Since the second filter has been started before the second positioning moment, when the second positioning moment arrives, the second filter has the positioning result of the previous positioning moment. Therefore, the positioning result of the previous positioning moment can be used as the initial value of the state quantity, combined with the second state transfer matrix to realize the prediction of the state quantity at the second positioning moment.

[0154] S4073: Increase the noise covariance matrix of the second filter at the current positioning moment to obtain the Kalman gain of the second filter.

[0155] It can be seen from Formula 11 and Formula 12 that by increasing the noise covariance matrix Q of the second filter, the Kalman gain of the second filter can be increased, so that the positioning result of the second filter gradually converges to the positioning result of the first filter.

[0156] In order to achieve a smooth transition, after the second positioning moment, the noise covariance matrix of the second filter gradually increases with time until it is consistent with the noise covariance matrix of the first filter or is greater than the noise covariance matrix of the first filter.

[0157] It should be noted that the embodiment of the present application does not impose any restrictive requirements on the amplitude of each increase in the noise covariance matrix of the second filter. For example, a linear increase method or a nonlinear increase method can be used.

[0158] S4074: Determine a positioning result of the second filter at the current positioning moment based on the second measurement value, the second predicted state quantity and the Kalman gain of the second filter.

[0159] The calculation formula for the positioning result of the second filter at the current positioning moment is shown in Formula 13, which will not be repeated here.

[0160] Unlike the second filter, the mobile terminal needs positioning services during movement. Therefore, the first filter is always in the starting state after the mobile terminal starts moving, that is, it will always output positioning results at different positioning moments (including the first positioning moment and the second positioning moment).

[0161] The positioning process of the first filter at different times mainly includes the following steps:

[0162] S4075: Based on the pseudorange observation quantity, carrier phase observation quantity and Doppler observation quantity at the current positioning moment, obtain a third measurement value of the first filter at the current positioning moment.

[0163] Taking the first filter as an extended Kalman filter as an example, it also includes an observation model and a state model. Among them, the observation model mainly uses the double-difference pseudorange equation, double-difference carrier phase equation and inter-satellite single-difference Doppler equation corresponding to the pseudorange observation, carrier phase observation and Doppler observation. The formula is expressed as follows:

[0164] Where s represents the satellite, r represents the mobile terminal, f represents the frequency of the carrier phase, λ represents the wavelength of the carrier phase, ρ represents the rate of change of the satellite-to-earth distance, δ represents the clock drift, c represents the speed of light, ε represents the random error, and D represents the Doppler observation value. represents the carrier phase, P represents the pseudorange, represents the inter-satellite single difference operator, Δ represents the inter-station single difference operator, and N represents the ambiguity.

[0165] Based on Formula 14 and Formula 15, the observation matrix H of the first filter and the third measurement value z at the current positioning moment can be obtained.

[0166] S4076: Based on the positioning result of the first filter at the previous positioning moment, a first state transfer matrix of the first filter is used to perform state prediction to obtain a third predicted state quantity of the first filter at the current positioning moment. The first state transfer matrix is ​​a matrix determined based on an epoch time interval and represents the uniform linear motion of the mobile terminal.

[0167] Still taking the first filter as an extended Kalman filter as an example, assuming that its state model is a uniform motion model, the second state transfer matrix F of the first filter is a matrix representing the uniform linear motion of the mobile terminal determined based on the epoch time interval, and is expressed as:

[0168] Among them, t d Represents an epoch time interval.

[0169] Based on the first state transfer matrix of the first filter, the state quantity of the first filter can be predicted. The prediction formula is as follows: k+1 =Fx k +Bu k Formula 18

[0170] Among them, x k represents the last positioning result before the current positioning moment, B represents the motion control matrix of the mobile terminal, u k Indicates the motion control quantity of the mobile terminal, B and u k is a known quantity set according to actual conditions, x k+1 It represents the third predicted state quantity at the current positioning moment, and the matrix is ​​represented as x=[r T v T ΔN T ] T , r represents the position of the mobile terminal located by the first filter at the last moment before the current positioning moment, v represents the movement speed of the mobile terminal located by the first filter at the last positioning moment before the current positioning moment, and ΔN represents the single difference ambiguity between stations.

[0171] S4077: Based on the third measurement value, the third predicted state quantity and the Kalman gain of the first filter, obtain the positioning result of the first filter at the current positioning moment.

[0172] The calculation formula for the positioning result of the first filter at the current positioning moment is shown in Formula 13, which will not be repeated here.

[0173] After obtaining the current positioning results of the first filter and the second filter, the positioning difference between the first filter and the second filter can be obtained through the position gap in the positioning results of the two filters at the second positioning moment, or through the convergence time of the positioning results of the two filters.

[0174] S408: The mobile terminal determines whether the positioning difference is not less than a preset difference threshold. If so, execute S409; otherwise, execute S411.

[0175] S409: The mobile terminal uses the positioning result of the second positioning moment of the second filter as the positioning result of the mobile terminal at the second positioning moment.

[0176] When the positioning difference is no less than the preset difference threshold, it indicates that the positioning results of the first filter and the second filter at the second positioning moment differ significantly. Because the target positioning result is determined by the second filter before entering the second positioning moment of non-low-speed motion, to achieve a smooth transition of the positioning result from low-speed motion to non-low-speed motion, the positioning result of the second filter at the second positioning moment can be used as the positioning result of the mobile terminal at the second positioning moment, thereby avoiding drastic changes in the positioning result and improving positioning smoothness. In addition, when the positioning difference is no less than the preset difference threshold, the mobile terminal can execute step S410 at one or more subsequent positioning moments until the positioning difference is less than the preset difference threshold.

[0177] S410: The mobile terminal increases the noise covariance matrix of the second filter at the next positioning moment to recalculate the positioning result, and compares it with the positioning result of the first filter at the next positioning moment. Then, the positioning result of the mobile terminal is determined based on the positioning difference at the next positioning moment. For example, when the positioning difference at the next positioning moment is less than the preset difference threshold, it indicates that the current positioning results of the first filter and the second filter are slightly different. At this time, the second filter can be terminated and the positioning mode of the first filter can be automatically switched to be used. The current positioning result of the first filter is used as the current positioning result of the mobile terminal. In addition, when the positioning difference at the next positioning moment is not less than the preset difference threshold, the mobile terminal uses the current positioning result of the second filter as the current positioning result of the mobile terminal. The positioning process of the second filter at the next positioning moment refers to S4071 to S4074 and will not be repeated here.

[0178] S411: The mobile terminal uses the current positioning result of the first filter as the positioning result of the mobile terminal.

[0179] In an embodiment of the present application, when the mobile terminal enters low-speed motion, on the basis of the first filter using the uniform linear motion model, the second filter using the stationary model is started, and the positioning result of the first filter is used as the measurement value of the second filter, and the second filter determines the final positioning result of the mobile terminal, thereby achieving high-precision and stable positioning of the mobile terminal after entering low-speed motion from non-low-speed motion; when the mobile terminal exits low-speed motion, the noise covariance matrix of the second filter is gradually increased, so that the positioning result of the second filter gradually converges to the positioning result of the first filter, and when the positioning difference between the two is less than the preset difference threshold, the positioning result of the first filter is used as the final target positioning result of the mobile terminal, thereby achieving high-precision and stable positioning of the mobile terminal after entering non-low-speed motion from low-speed motion. Through the adaptive switching of the first filter and the second filter, the positioning accuracy and stability of the mobile terminal in RTK differential decomposition are effectively improved.

[0180] Still taking the slow driving scenario of the vehicle terminal in the map navigation scenario as an example, as shown in Figure 11, the black line is the road boundary, the light blue line is the road center line, the vehicle travels from left to right and changes lanes to the outermost lane to slow down on the way. The mobile terminal positioning method based on two filters provided in the embodiment of the present application is adopted, and the positioning results of the vehicle terminal are represented by dots using RTK differential positioning technology. Compared with the traditional RTK differential positioning technology represented by triangles, the method provided in the embodiment of the present application can effectively improve the positioning accuracy and stability of the vehicle terminal in the slow driving scenario.

[0181] The dual-filter-based mobile terminal positioning method provided in the embodiment of the present application can ensure the robust positioning of RTK differential positioning in low-speed motion scenarios and is applicable to a variety of positioning scenarios.

[0182] 1. Lane-Level Navigation Scenario

[0183] As shown in Figure 12A, the mobile phone used for navigation on the vehicle is equipped with a satellite positioning device. The vehicle needs to switch from the leftmost lane to the rightmost lane to enter Xuelian Road. During the lane switching process, the vehicle will slow down and enter a low-speed motion state. In this way, during the lane switching process, the second filter is superimposed on the positioning result of the first filter, and the positioning result of the first filter is used as the measurement value of the second filter. The second filter outputs the positioning result after the vehicle enters the low-speed motion; as shown in Figure 12B, after entering Xuelian Road, the vehicle exits the low-speed motion by accelerating. At this time, the second filter converges to the positioning result of the first filter by increasing the noise covariance matrix, and adopts the positioning result of the first filter when the preset conditions are met. By positioning the mobile phone on the vehicle using the method provided in the embodiment of the present application, the problem of lane-level matching errors during navigation caused by RTK positioning point drift can be prevented, the stability of the positioning result can be improved, and the guide line of lane-level navigation can be accurately drawn, thereby improving the accuracy of lane-level navigation.

[0184] 2. Green Wave Navigation Scenario

[0185] As shown in Figure 12C, a mobile phone equipped with a satellite positioning device is used for navigation during vehicle driving. During the navigation process, the dual-filter-based mobile terminal positioning method provided in the embodiment of the present application can be used to achieve stable positioning of the vehicle. In this way, speed guidance can be provided for the vehicle based on the number of seconds of the green light on the driving route, so that the vehicle can pass through the intersection on the driving route with the green light, reducing parking waiting time and improving vehicle traffic efficiency.

[0186] Based on the same technical concept, an embodiment of the present application provides a structural schematic diagram of a positioning device, which can implement the above-mentioned mobile terminal positioning method and achieve the same technical effect.

[0187] Referring to FIG13 , the positioning device includes an acquisition module 1301 , a detection module 1302 , and a positioning module 1303 ; wherein:

[0188] An acquisition module 1301 is configured to determine observation data for each satellite by measuring satellite signals from a plurality of satellites, wherein the observation data for each satellite includes a Doppler observation; and determine a set of inter-satellite single-difference Doppler residuals and a motion speed of the mobile terminal based on the Doppler observations for the plurality of satellites, wherein each inter-satellite single-difference Doppler residual represents a residual between Doppler observation equations for two different satellites.

[0189] The positioning module 1303 determines a first positioning result at a first positioning moment based on the observation data of the plurality of satellites using a first filter, where the first filter is a filter using a first state transfer matrix representing a non-low-speed motion state;

[0190] A detection module 1302 is configured to determine whether the mobile terminal is in a low-speed motion state at the first positioning moment based on at least one of the set of inter-satellite single-difference Doppler residuals and the motion speed;

[0191] The positioning module 1303 is also used to, when the mobile terminal is in a low-speed motion state at the first positioning moment, use the first positioning result by a second filter to determine a second positioning result at the first positioning moment, and the second filter is a filter that uses a second state transfer matrix that characterizes the low-speed motion state.

[0192] The mobile terminal positioning device provided in an embodiment of the present application utilizes Doppler observations of multiple satellites, employing the intersatellite single-difference Doppler residual method and the weighted least squares Doppler velocity method to calculate a set of intersatellite single-difference Doppler residuals and motion velocity, respectively. These are then compared with corresponding thresholds. When at least one of the intersatellite single-difference Doppler residuals and motion velocity falls within a corresponding preset range, it is determined that the mobile terminal has entered a low-speed motion state. By jointly detecting a set of intersatellite single-difference Doppler residuals and motion velocity, the current motion state of the mobile terminal can be accurately determined, allowing the use of corresponding filters for positioning and solution, thereby improving positioning accuracy and stability.

[0193] For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing this application, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

[0194] Those skilled in the art will appreciate that various aspects of the present application can be implemented as systems, methods, or program products. Therefore, various aspects of the present application can be specifically implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation that combines hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0195] After introducing the positioning method and positioning device according to an exemplary embodiment of the present application, next, an electronic device according to another exemplary embodiment of the present application is introduced.

[0196] In one embodiment, the electronic device may be the server in Figure 2. As shown in Figure 14 , the structure of the electronic device may include a memory 1401, a communication module 1403, and one or more processors 1402.

[0197] The memory 1401 is used to store computer programs executed by the processor 1402. The memory 1401 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an operating instruction set.

[0198] Memory 1401 may be a volatile memory, such as random-access memory (RAM); a non-volatile memory, such as read-only memory, flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or any other medium capable of carrying or storing a desired computer program in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 1401 may be a combination of the aforementioned memories.

[0199] The processor 1402 may include one or more central processing units (CPUs) or digital processing units, etc. The processor 1402 is configured to implement the above-mentioned mobile terminal positioning method when calling the computer program stored in the memory 1701 .

[0200] The communication module 1403 is used to communicate with mobile terminals and other devices.

[0201] The specific connection medium between the memory 1401, communication module 1403, and processor 1402 is not limited in the embodiments of the present application. In Figure 14, the embodiment of the present application shows that the memory 1401 and the processor 1402 are connected via a bus 1404. The bus 1404 is depicted as a bold line in Figure 14. The connection methods between other components are merely schematic and are not intended to be limiting. The bus 1404 can be divided into an address bus, a data bus, a control bus, etc. For ease of description, Figure 14 only uses a single bold line, but does not represent a single bus or a single type of bus.

[0202] The memory 1401 stores a computer storage medium, which stores computer executable instructions for implementing the mobile terminal positioning method of the embodiment of the present application. The processor 1402 is configured to execute the steps of the mobile terminal positioning method.

[0203] In another embodiment, the electronic device may be the mobile terminal shown in Figure 2. In this embodiment, the structure of the mobile terminal may be as shown in Figure 15, including: a communication component 1510, a memory 1520, a display unit 1530, a camera 1540, a sensor 1550, an audio circuit 1560, a Bluetooth module 1570, a processor 1580, and other components.

[0204] The communication component 1510 is used to communicate with the server. In some embodiments, it may include a wireless fidelity (WiFi) module. The WiFi module is a short-range wireless transmission technology. The electronic device can help the object send and receive information through the WiFi module.

[0205] The memory 1520 can be used to store software programs and data. The processor 1580 executes various functions and data processing of the mobile device by running the software programs or data stored in the memory 1520. The memory 1520 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The memory 1520 stores the operating system that enables the mobile device to operate. In the present application, the memory 1520 can store the operating system and various application programs, and may also store the computer program that executes the mobile terminal positioning method according to the embodiment of the present application.

[0206] The display unit 1530 can also be used to display information input by or provided to an object, as well as a graphical object interface of various menus of the vehicle-mounted terminal. Specifically, the display unit 1530 may include a display screen 1532 disposed on the front of the vehicle-mounted terminal. The display screen 1532 may be configured in the form of a liquid crystal display, a light-emitting diode, or the like. The display unit 1530 can be used to display the application operation interface in the embodiments of the present application.

[0207] The display unit 1530 can also be used to receive input digital or character information and generate signal input related to object settings and function control of the mobile device. Specifically, the display unit 1530 may include a touch screen 1531 set on the mobile device, which can collect touch operations of objects on or near it, such as clicking a button, dragging a scroll box, etc.

[0208] The touch screen 1531 can be covered on the display screen 1532, or the touch screen 1531 and the display screen 1532 can be integrated to realize the input and output functions of the mobile device, and the integrated display screen can be simply called a touch display screen. In this application, the display unit 1530 can display applications and corresponding operation steps.

[0209] Camera 1540 can be used to capture images. There can be one or more cameras 1540. The object is projected through the lens to generate an optical image, which is then projected onto a photosensitive element. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) phototransistor (CMOS). The photosensitive element converts the optical signal into an electrical signal, which is then transmitted to processor 1580 for conversion into a digital image signal.

[0210] The mobile device may further include at least one sensor 1550, such as an accelerometer 1551, a distance sensor 1552, a fingerprint sensor 1553, and a temperature sensor 1554. The mobile device may also be equipped with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, a light sensor, and a motion sensor.

[0211] The audio circuit 1560, the speaker 1561, and the microphone 1562 can provide an audio interface between the object and the mobile terminal. The audio circuit 1560 can transmit the electrical signal converted from the received audio data to the speaker 1561, which converts it into a sound signal for output. The mobile device can also be equipped with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 1562 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1560 and converted into audio data. The audio data is then output to the communication component 1510 for transmission to, for example, another mobile terminal, or the audio data is output to the memory 1520 for further processing.

[0212] The Bluetooth module 1570 is used to exchange information with other Bluetooth devices having a Bluetooth module through the Bluetooth protocol. For example, a mobile device can establish a Bluetooth connection with a wearable electronic device (such as a smart watch) that also has a Bluetooth module through the Bluetooth module 1570 to exchange data.

[0213] The processor 1580 is the control center of the mobile device, connecting the various parts of the entire terminal using various interfaces and lines. It performs various functions of the mobile device and processes data by running or executing software programs stored in the memory 1520 and calling data stored in the memory 1520. In some embodiments, the processor 1580 may include one or more processing units; the processor 1580 may also integrate an application processor and a baseband processor, wherein the application processor mainly processes the operating system, object interface, and application programs, and the baseband processor mainly processes wireless communications. It is understandable that the above-mentioned baseband processor may not be integrated into the processor 1580. In the present application, the processor 1580 can run the operating system, application programs, object interface display and touch response, as well as the mobile terminal positioning method of the embodiment of the present application. In addition, the processor 1580 is coupled to the display unit 1530.

[0214] In some possible implementations, various aspects of the mobile terminal positioning method provided in the present application may also be implemented in the form of a program product, which includes a computer program. When the program product is run on an electronic device, the computer program is used to enable the electronic device to execute the steps of the mobile terminal positioning method according to various exemplary embodiments of the present application described above in this specification. For example, the electronic device may execute the steps shown in Figure 4.

[0215] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0216] The program product of the embodiment of the present application may be a portable compact disk read-only memory and include a computer program, and can be run on an electronic device. However, the program product of the present application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with a command execution system, apparatus, or device.

[0217] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with a command execution system, apparatus, or device.

[0218] The computer program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0219] The computer program for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The computer program can be executed entirely on the user computing device, partially on the user computing device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, including a local area network or a wide area network, or can be connected to an external computing device.

[0220] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain a computer-usable computer program.

[0221] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0222] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A positioning method, performed in a mobile terminal, comprising: Acquiring observation data for each of the plurality of satellites, wherein the observation data for each satellite includes a Doppler observation; Based on the Doppler observations of the plurality of satellites, a set of inter-satellite single-difference Doppler residuals and a motion speed of the mobile terminal are determined, wherein each inter-satellite single-difference Doppler residual represents: a residual between Doppler observation equations of two different satellites; Determining a first positioning result at a first positioning moment based on the observation data of the plurality of satellites by a first filter, wherein the first filter is a filter using a first state transfer matrix representing a non-low-speed motion state; determining, based on at least one of the set of inter-satellite single-difference Doppler residuals and the movement speed, whether the mobile terminal is in a low-speed movement state at the first positioning moment; When the mobile terminal is in a low-speed motion state at the first positioning moment, a second filter uses the first positioning result to determine a second positioning result at the first positioning moment. The second filter is a filter that uses a second state transfer matrix that characterizes the low-speed motion state.

2. The method according to claim 1, wherein determining a set of inter-satellite single-difference Doppler residuals and the motion speed of the mobile terminal based on Doppler observations of the plurality of satellites comprises: selecting a reference satellite from the plurality of satellites; Determining a set of inter-satellite single-difference observation equations based on the Doppler observation equation for the reference satellite and the Doppler observation equations for the other satellites in the plurality of satellites; wherein each inter-satellite single-difference observation equation represents: a difference between the Doppler observation equation for the reference satellite and the Doppler observation equation for one other satellite; determining a set of Doppler residual equations based on the set of inter-satellite single-difference observation equations; The plurality of Doppler observations are respectively input into corresponding Doppler residual equations to obtain the set of inter-satellite single-difference Doppler residuals.

3. The method according to claim 1 or 2, wherein determining a set of inter-satellite single-difference Doppler residuals and the motion speed of the mobile terminal based on Doppler observations of the plurality of satellites comprises: Establishing a Doppler observation equation group based on a plurality of Doppler observation equations for the plurality of satellites; Based on the credibility of each of the multiple Doppler observations, weights are set for the corresponding Doppler observations respectively; Solving the Doppler observation equations based on the weights of the plurality of Doppler observations to obtain a satellite-to-earth distance change rate; The movement speed is determined based on the satellite-to-ground distance change rate.

4. The method according to any one of claims 1 to 3, wherein determining whether the mobile terminal is in a low-speed motion state based on at least one of the set of inter-satellite single-difference Doppler residuals and the motion speed comprises: When the circular probable error of the group of inter-satellite single-difference Doppler residuals is less than a preset error threshold or the movement speed is less than a preset speed threshold, it is determined that the mobile terminal is in a low-speed movement state.

5. The method according to any one of claims 1 to 4, wherein determining, by a second filter, a second positioning result for the mobile terminal using the first positioning result comprises: determining, according to a first positioning result of the first positioning moment, a first measurement value and a first state quantity of the second filter at the first positioning moment; Based on the first state quantity, using the second state transfer matrix to perform state prediction to obtain a first predicted state quantity of the second filter at the first positioning moment; wherein the second state transfer matrix is ​​a unit matrix representing that the mobile terminal is stationary; A second positioning result at the first positioning moment is determined based on the first measurement value, the first predicted state quantity and the Kalman gain of the second filter.

6. The method of claim 5, further comprising: When it is determined that the mobile terminal enters a non-low-speed motion state at a second positioning moment, comparing the positioning results of the first filter and the second filter at the second positioning moment to obtain a positioning difference between the first filter and the second filter; wherein the second positioning moment is a switching moment from the low-speed motion state to the non-low-speed motion state; If the positioning difference is not less than a preset difference threshold, using the positioning result of the second filter at the second positioning moment as the positioning result of the mobile terminal at the second positioning moment; Increasing the noise covariance matrix of the second filter at the next positioning moment, recalculating the positioning result of the second filter at the next positioning moment, and comparing the recalculated positioning result with the positioning result of the first filter at the next positioning moment; When the positioning difference is smaller than a preset difference threshold, the current positioning result of the first filter is used as the positioning result of the mobile terminal at the current positioning moment.

7. The method according to claim 6, wherein the positioning results of the second filter at different positioning times are determined by: Using the positioning result of the first filter at the current positioning moment as the second measurement value of the second filter at the current positioning moment; Based on the positioning result of the second filter at the previous positioning moment, the second state transfer matrix is ​​used to perform state prediction to obtain a second predicted state quantity of the second filter at the current positioning moment; Increasing the noise covariance matrix of the second filter at the current positioning moment to obtain a Kalman gain of the second filter; Based on the second measurement value, the second predicted state quantity and the Kalman gain of the second filter, a positioning result of the second filter at the current positioning moment is determined.

8. The method according to any one of claims 1 to 4, 6 to 7, wherein the observation data further comprises: Pseudorange observations and carrier phase observations; the positioning results of the first filter at different positioning times are determined by the following methods: Obtaining a third measurement value of the first filter at the current positioning moment based on the pseudorange observation, the carrier phase observation, and the Doppler observation at the current positioning moment; Based on the positioning result of the first filter at the previous positioning moment, a first state transfer matrix of the first filter is used to perform state prediction to obtain a third predicted state quantity of the first filter at the current positioning moment; wherein the first state transfer matrix is ​​a matrix representing the uniform linear motion of the mobile terminal determined based on the epoch time interval; Based on the third measurement value, the third predicted state quantity and the Kalman gain of the first filter, a positioning result of the first filter at the current positioning moment is obtained.

9. The method according to any one of claims 1 to 8, further comprising: When the mobile terminal is in a non-low-speed motion state, the first positioning result of the first filter is used as the positioning result of the mobile terminal.

10. A positioning device comprising: an acquisition module, configured to acquire observation data of each of the plurality of satellites, wherein the observation data of each satellite includes a Doppler observation; Based on the Doppler observations of the plurality of satellites, a set of inter-satellite single-difference Doppler residuals and a motion speed of the mobile terminal are determined, wherein each inter-satellite single-difference Doppler residual represents: a residual between Doppler observation equations of two different satellites; a positioning module, which determines a first positioning result at a first positioning moment by using a first filter based on the observation data of the plurality of satellites, wherein the first filter is a filter using a first state transfer matrix representing a non-low-speed motion state; a detection module, configured to determine whether the mobile terminal is in a low-speed motion state at the first positioning moment based on at least one of the set of inter-satellite single-difference Doppler residuals and the motion speed; Among them, the positioning module is also used to, when the mobile terminal is in a low-speed motion state at the first positioning moment, use the first positioning result by the second filter to determine the second positioning result at the first positioning moment, and the second filter is a filter that uses a second state transfer matrix that characterizes the low-speed motion state.

11. An electronic device comprising a processor and a memory, wherein: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 9.

12. A computer-readable storage medium comprising a computer program, wherein when the computer program is run on an electronic device, the computer program is configured to cause the electronic device to execute the method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program, wherein the computer program is stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device executes any one of the methods described in claims 1-9.

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