Positioning method and apparatus, storage medium, and program product

Abstract

A positioning method and apparatus, a storage medium, and a program product. The method comprises: acquiring data output by a sensor mounted on a mobile device (S101), the data comprising global navigation satellite system (GNSS) data output by a GNSS chip and inertial measurement unit (IMU) data output by an inertial sensor; on the basis of the IMU data, determining the state of the mobile device (S102); if the state is that the mobile device is not fixed to a vehicle, inputting the data output by the sensor into a vehicle-unconstrained filtering model for filtering calculation to obtain positioning data of the mobile device (S103); and if the state is that the mobile device is fixed to a vehicle, inputting the data output by the sensor into a vehicle-constrained filtering model for filtering calculation to obtain positioning data of the mobile device (S104). On the basis of the state of a mobile device, the method uses different filtering models to position the mobile device, improving the positioning accuracy.

Description

Positioning methods, devices, storage media and program products

[0001] This disclosure claims priority to Chinese Patent Application No. 202410970184.8, filed on July 18, 2024, entitled “Positioning Method, Apparatus, Storage Medium and Program Product”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of positioning technology, and in particular to a positioning method, device, storage medium, and program product. Background Technology

[0003] Currently, navigation services provided by mobile applications have become a widespread choice for users traveling by car. Navigation services rely on the user's real-time location. To achieve more accurate location determination in driving scenarios, data from the mobile device's inertial sensors and satellite positioning chips are typically combined to determine the device's real-time location, which is equivalent to the user's real-time location. This positioning method is applicable to a wider range of scenarios, especially in outdoor areas where satellite positioning signals are blocked, such as tunnels and canyons, effectively improving positioning accuracy and reliability. However, this method depends on the mobile device's orientation, which can change due to the user's actions while using the device. Therefore, ensuring positioning accuracy based on the mobile device's orientation is a crucial problem to solve in driving scenarios. Summary of the Invention

[0004] This disclosure provides a positioning method, apparatus, storage medium, and program product that can improve positioning accuracy.

[0005] Firstly, this disclosure provides a positioning method, the method comprising:

[0006] Acquire data output from sensors mounted on a mobile device, including GNSS data output from a Global Navigation Satellite System (GNSS) chip and IMU data output from an inertial sensor;

[0007] Based on the IMU data, the state of the mobile device is determined;

[0008] If the state is that the mobile device is not fixed to the vehicle, the data output by the sensor is input into a filtering model without vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0009] If the mobile device is fixed on the vehicle, the data output by the sensor is input into a filtering model with vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0010] Secondly, this disclosure provides a positioning device, the device comprising:

[0011] The acquisition module is used to acquire data output by sensors mounted on the mobile device, including GNSS data output by the Global Navigation Satellite System (GNSS) chip and IMU data output by the inertial sensor.

[0012] The processing module is used to determine the state of the mobile device based on the IMU data. If the state is that the mobile device is not fixed to the vehicle, the first control module is triggered. If the state is that the mobile device is fixed to the vehicle, the second control module is triggered.

[0013] The first control module is used to input the data output by the sensor into a filtering model without vehicle constraints for filtering calculation, so as to obtain the positioning data of the mobile device.

[0014] The second control module is used to input the data output by the sensor into a filtering model with vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0015] Thirdly, this disclosure provides an electronic device, including: a processor and a memory; the processor and the memory are communicatively connected.

[0016] The memory stores computer-executed instructions;

[0017] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0018] Fourthly, this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0019] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first aspects.

[0020] The technical solution provided in this disclosure determines the state of a mobile device using IMU data output from sensors mounted on the device. If the mobile device is not fixed to a vehicle, the GNSS and IMU data output by the sensors are input into a filtering model without vehicle constraints. If the mobile device is fixed to a vehicle, the GNSS and IMU data output by the sensors are input into a filtering model with vehicle constraints. Since the attitude of the mobile device relative to the vehicle is uncertain when it is not fixed, filtering the GNSS and IMU data using a filtering model without vehicle constraints avoids incorrect constraints imposed by the vehicle on the mobile device's attitude, reducing the probability of positioning data errors. However, when the mobile device is fixed to a vehicle, its attitude relative to the vehicle is determined, and the constraints imposed by the vehicle on the mobile device's attitude are also determined. Filtering the GNSS and IMU data using a filtering model with vehicle constraints incorporates constraints on the vehicle's position estimation, improving the accuracy of the positioning data obtained by the filtering model. The technical solution of this disclosure improves the accuracy of positioning data obtained by mobile devices in different states. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a flowchart illustrating a positioning method provided in an embodiment of this disclosure;

[0023] Figure 2 is a flowchart illustrating another positioning method provided in an embodiment of this disclosure;

[0024] Figure 3 is a flowchart illustrating another positioning method provided in an embodiment of this disclosure;

[0025] Figure 4 is a flowchart illustrating another positioning method provided in an embodiment of this disclosure;

[0026] Figure 5 is a schematic diagram of a positioning device provided in an embodiment of this disclosure;

[0027] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0028] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0030] To facilitate understanding, the terminology used in this disclosure will be introduced first:

[0031] Inertial Navigation System (INS): The basic working principle is based on Newton's laws of motion. By measuring the acceleration of the vehicle in the inertial reference frame, integrating it over time, and transforming it into the navigation coordinate system, information such as the vehicle's velocity, yaw angle, and position in the navigation coordinate system can be obtained.

[0032] Inertial Measurement Unit (IMU): Used to measure the three-axis attitude angles (or angular rates) and acceleration of the carrier, it is the core unit of the inertial navigation system.

[0033] Global Navigation Satellite System (GNSS): This refers to all satellite navigation systems, including global, regional, and augmented systems, such as the US GPS (Global Positioning System), Russia's GLONASS, Europe's Galileo, and China's BeiDou Navigation Satellite System, as well as related augmentation systems, such as the US WAAS (Wide Area Augmentation System), Europe's EGNOS (European Geostationary Navigation Overlay Service), and Japan's MSAS (Multi-Functional Satellite Augmentation System).

[0034] With technological advancements, an increasing number of applications offer location-based services (LBS). Therefore, accurately determining the user's location is crucial for ensuring the quality of LBS services. Navigation is a typical LBS service. Taking navigation as an example, when driving, users utilize navigation applications installed on their mobile devices (such as smartphones and tablets). Providing accurate navigation services hinges on accurately determining the location of the user's vehicle. Since the mobile device, user, and vehicle coexist in the same space when driving, the location of the mobile device can be used as the location of the user and their vehicle. Therefore, accurately determining the mobile device's location is paramount for navigation services. When driving, the quality of GNSS signals varies in different areas. In tunnels, mountains, or areas with tall buildings, GNSS signals are degraded due to obstruction and reflection, making it impossible to locate mobile devices solely using GNSS signals. To ensure the location of mobile devices in these areas, existing technologies typically rely on IMU data measured by the inertial sensors on the mobile device and GNSS data output by the GNSS chip to determine the device's position. However, this scenario presents several unresolved issues. For example, the mobile device's posture within the vehicle changes due to the user picking it up or using it, and these changes significantly affect the location of the device. The accuracy of location estimation based on IMU data is crucial. Furthermore, even when the mobile device is relatively stationary relative to the vehicle (i.e., the device is fixed to the vehicle, such as by attaching it to a bracket or placing it in a position that won't cause movement, or the user is holding the device stably), meaning the device's posture relative to the vehicle is fixed, the mounting angle of the device relative to the vehicle and the distance vector corresponding to the lever arm (i.e., the distance vector between the IMU mounting position and the vehicle reference point (such as the vehicle's center of gravity), including information such as the lever arm's length and direction) are difficult to predict. This can lead to errors in location estimation based on IMU data, reducing the accuracy of positioning. Therefore, in driving scenarios, how to more accurately determine the location of mobile devices—that is, ensuring the accuracy of mobile device positioning—is a problem that needs to be solved.

[0035] In view of this, this disclosure provides a positioning method that improves positioning accuracy by detecting the state of the mobile device and the vehicle, and inputting IMU data and GNSS data into different filtering models for filtering calculations. The executing entity of this positioning method can be an electronic device, such as a smartphone, tablet, or other mobile device not fixed to a vehicle, and the vehicle can be a vehicle, electric vehicle, or other transportation vehicle. The electronic device can deploy software or program code to execute or trigger the positioning method. Alternatively, the executing entity can be a server, which processes the GNSS data and IMU data acquired from the mobile device to generate positioning data for the mobile device and feeds this positioning data back to the mobile device. Whether the mobile device or the server is the executing entity, it does not affect the implementation process of the application.

[0036] The technical solutions of this disclosure are described in detail below with reference to specific embodiments. The specific embodiments of this disclosure can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0037] Figure 1 is a flowchart illustrating a positioning method provided in an embodiment of this disclosure. As shown in Figure 1, the method may include the following steps:

[0038] S101. Acquire data output by sensors mounted on the mobile device, including GNSS data output by the GNSS chip of the Global Navigation Satellite System and IMU data output by the inertial sensor.

[0039] Mobile devices can acquire GNSS data in real time through GNSS chips deployed on the device, and can also acquire IMU data in real time through inertial sensors deployed on the device. These inertial sensors may include, for example, accelerometers and gyroscopes. Correspondingly, the IMU data may include gravity axis data output by the accelerometer and data output by the gyroscope.

[0040] If the execution entity of this method is a server, the GNSS data and IMU data can be actively uploaded to the server by the mobile device on a periodic basis.

[0041] S102. Based on IMU data, determine the status of the mobile device. If the status of the mobile device is that the mobile device is not fixed to the vehicle, then proceed to step S103; if the status of the mobile device is that the mobile device is fixed to the vehicle, then proceed to step S104.

[0042] The state of the mobile device can be determined using IMU data. When the data fluctuation amplitude of the IMU data within a certain observation period is less than or equal to the corresponding data fluctuation threshold, the mobile device can be considered to be relatively stationary with respect to the vehicle, meaning the mobile device is fixed to the vehicle. This can be understood as the mobile device being stably held by the user, preventing any movement relative to the vehicle, or the mobile device being physically fixed to the vehicle. For example, the mobile device could be fixed to a bracket on the vehicle or to a specific area of ​​the vehicle. Conversely, if the fluctuation amplitude exceeds the threshold, the mobile device is considered not relatively stationary with respect to the vehicle, meaning it is not fixed to the vehicle. This could be because the mobile device, originally fixed to the vehicle, is picked up and used by the user, or it is placed in a specific area of ​​the vehicle where the area cannot completely secure it. As the vehicle moves (e.g., a sharp turn or sudden deceleration), the mobile device's orientation changes (e.g., the top of the mobile device, originally pointing forward, now points backward). The observation period and data fluctuation threshold can be set according to actual needs, and this disclosure does not impose any restrictions on them.

[0043] Regardless of whether the execution subject of this disclosed method is a server or a mobile device, when actually implementing this disclosed method, a filtering model with vehicle constraints and a filtering model without vehicle constraints can be pre-configured on the execution subject, or a filtering model can be pre-configured, and during the execution of this disclosed method, the parameters of the model can be adjusted according to the state of the mobile device to generate a model that matches the state of the mobile device.

[0044] S103. Input the data output by the sensor into a filter model without vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0045] The location data of the mobile device obtained by the present disclosure through filtering calculation may include: the position, speed, heading, zero offset and other data of the mobile device. As mentioned above, the position of the mobile device can also be regarded as the position of the vehicle on which the mobile device is located, that is, the positioning of the vehicle is realized.

[0046] Taking the unconstrained filtering model of INS / GNSS integrated navigation as an example, the model parameters of this unconstrained filtering model include a first state vector and a first covariance matrix. The first state vector includes a mobile device attitude state vector (where the attitude of the mobile device is its own attitude in the geodetic coordinate system), a mobile device velocity state vector, a mobile device position state vector, a gyroscope zero-bias state vector, and an accelerator zero-bias state vector, where each state vector is a three-dimensional state vector. The first covariance matrix is ​​a covariance matrix generated based on the covariances corresponding to each state vector in the first state vector. Based on the first state vector and the first covariance matrix, the unconstrained filtering model is constructed. By performing filtering calculations on GNSS data and IMU data using this unconstrained filtering model, the positioning data of the mobile device can be obtained.

[0047] The filtering calculation can be, for example, based on a Kalman filter, an extended Kalman filter method, or other filtering methods such as particle filtering. The specific calculation method for the filtering model without vehicle constraints can be found in existing technologies and will not be elaborated here.

[0048] S104. Input the data output by the sensor into a filtering model with vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0049] The location data is the same as the location data of the mobile device obtained by filtering calculation in S103 above, and will not be described again here.

[0050] Taking the INS / GNSS integrated navigation loose combination model with vehicle constraints as an example, the model parameters of this filtering model with vehicle constraints include a first state vector, a vehicle constraint vector, a first covariance matrix, and a vehicle constraint covariance matrix. The first state vector and the first covariance matrix are the same as those in the aforementioned filtering model without vehicle constraints, and will not be repeated here.

[0051] For the vehicle-mounted constraint vector and vehicle-mounted constraint covariance matrix, since the installation angle state vector and lever state vector are also needed to construct the loosely coupled INS / GNSS integrated navigation model when the mobile device is fixed on the vehicle, the vehicle-mounted constraint vector and vehicle-mounted constraint covariance matrix need to be included in the model parameters. The vehicle-mounted constraint vector includes the installation angle state vector (the installation angle represents the angular deviation between the measurement axis of the mobile device's IMU and the vehicle coordinate system when the IMU is installed on the vehicle) and the lever state vector (the lever represents the distance vector between the IMU installation position and the vehicle reference point (such as the vehicle's center of mass), which includes information such as the length and direction of the lever). Each of the above state vectors is a three-dimensional state vector. Correspondingly, the vehicle-mounted constraint covariance matrix includes the initial covariance of the installation angle and the initial covariance of the lever. The initial covariance of the installation angle is determined based on the covariance of the installation angle state vector, and the initial covariance of the lever is determined based on the covariance of the lever state vector. A vehicle-constrained filtering model is constructed based on the first state vector, the vehicle constraint vector, the first covariance matrix, and the vehicle constraint covariance matrix. By performing filtering calculations on GNSS data and IMU data using this vehicle-constrained filtering model, the positioning data of the mobile device fixed on the vehicle can be obtained.

[0052] The filtering calculation can be, for example, based on a Kalman filter, an extended Kalman filter method, or other filtering methods such as particle filtering. The specific calculation method for the filtering model without vehicle constraints can be found in existing technologies and will not be elaborated here.

[0053] Because the mobile device's posture relative to the car varies depending on its state, using the same filtering model for filtering calculations can lead to inaccurate parameters and poor accuracy in the filtering results, resulting in poor mobile device positioning. Therefore, it is necessary to use different filtering models corresponding to different mobile device states to improve positioning accuracy. The method provided in this disclosure determines the state of a mobile device using IMU data output from sensors mounted on the device. If the mobile device is not fixed to a vehicle, the GNSS and IMU data output by the sensors are input into a filtering model without vehicle constraints. If the mobile device is fixed to a vehicle, the GNSS and IMU data output by the sensors are input into a filtering model with vehicle constraints. When the mobile device is not fixed to a vehicle, its attitude relative to the vehicle is uncertain. In this case, filtering the GNSS and IMU data using a filtering model without vehicle constraints avoids incorrect constraints imposed by the vehicle on the mobile device's attitude, reducing the probability of positioning data errors. When the mobile device is fixed to a vehicle, its attitude relative to the vehicle is determined, and the constraints imposed by the vehicle on the mobile device's attitude are also determined. Filtering the GNSS and IMU data using a filtering model with vehicle constraints incorporates constraints on the vehicle's position estimation of the mobile device, improving the accuracy of the positioning data obtained by the filtering model. The technical solution of this disclosure improves the accuracy of positioning data obtained by mobile devices in different states.

[0054] As mentioned earlier, the attitude of the mobile device relative to the vehicle is fixed. However, the installation angle of the mobile device relative to the vehicle and the distance vector corresponding to the lever (i.e., the distance vector between the IMU installation position and the vehicle reference point (such as the vehicle's center of gravity, which includes information such as the length and direction of the lever) are difficult to predict. This can lead to errors in the position calculated based on IMU data, reducing the accuracy of positioning.

[0055] To address this problem, this disclosure provides another positioning method, the flowchart of which is shown in Figure 2. The difference between this method and the method shown in Figure 1 is that, if the mobile device is fixed to the vehicle, before inputting the sensor output data into the vehicle-constrained filtering model for filtering calculation, this method, based on the method shown in Figure 1, may further include the following steps:

[0056] S201. Buffer the data output by the sensor until the buffering time reaches a preset time threshold, wherein the preset time threshold is used to determine whether the heading of the mobile device is stable within the time period corresponding to the preset time threshold.

[0057] By continuously acquiring and caching data output from the mobile device's sensors, when the caching time reaches a preset threshold, the changes in the data within that caching time are assessed to determine whether the mobile device's heading is stable during that period. This preset threshold can be determined based on actual needs, and this disclosure does not impose any restrictions on it.

[0058] If the method is executed by a mobile device, the preset duration threshold can be pre-configured in the mobile device or determined according to instructions from an external device (e.g., a cloud server). If the method is executed by a cloud server, the cloud server can continuously acquire data output from the sensor on the mobile device. This data can be acquired actively by the cloud server or uploaded actively by the mobile device to the cloud server.

[0059] S202. Based on the GNSS data in the cached data, determine whether the heading of the mobile device is stable. If the heading of the mobile device is stable, proceed to step S203. If the heading of the mobile device is unstable, continue to determine whether the heading of the mobile device is stable based on subsequent GNSS data.

[0060] When there are errors in the installation angle and lever arm of the mobile device relative to the vehicle compared to the actual installation situation, these errors will gradually accumulate during the positioning process of the mobile device, resulting in a decrease in positioning accuracy. Therefore, it is necessary to improve the accuracy of the installation angle and lever arm of the mobile device relative to the vehicle in order to improve the positioning accuracy.

[0061] However, when the mobile device's heading is unstable, its measurement data is significantly affected, leading to increased measurement errors in information such as the mounting angle and lever arm, thus impacting positioning accuracy. Furthermore, in dynamic environments, the vehicle's motion constantly changes, causing fluctuations in the data output of navigation devices such as IMUs and GNSS. This fluctuation increases the complexity and difficulty of data processing, making it more challenging to obtain accurate information such as the mounting angle and lever arm. Therefore, it is necessary to determine whether the mobile device's heading is stable based on GNSS data. Once the heading is stable, further information on the mounting angle and lever arm, including the vehicle constraint vector and vehicle constraint covariance matrix, should be obtained to improve positioning accuracy and reduce the difficulty of obtaining this information.

[0062] Specifically, the instantaneous speed and heading of the mobile device can be calculated based on the location information (such as latitude, longitude, and altitude) and timestamps in the GNSS data. If the mobile device can quickly recover and maintain its predetermined heading after being disturbed by external forces, it is considered to have a stable heading. For example, if the headway calculated from the cached GNSS data fluctuates within a preset fluctuation range within a preset time threshold, and can recover to the predetermined heading within the first time interval after being disturbed by external forces, the mobile device is considered to have a stable heading. If the headway calculated from the cached GNSS data fluctuates beyond the preset fluctuation range within a preset time threshold, or cannot recover to the predetermined heading within the first time interval after being disturbed by external forces, the mobile device is considered to have an unstable heading.

[0063] S203. Input the cached data into at least two filters to obtain the initial values ​​of the mounting angle, lever arm, mounting angle, and lever arm.

[0064] The initial values ​​of the mounting angle and the lever arm can be obtained through at least two filters in the mobile device. An initial covariance of the mounting angle is calculated based on the initial mounting angle value, and an initial covariance of the lever arm is generated based on the initial lever arm value. The initial mounting angle value of this filter is equal to a set value, and the initial mounting angle values ​​of any two filters are different. This filter is a filter in the filtering model, such as a filter implemented based on the Kalman filter algorithm. It should be understood that the filters, target filters, etc., mentioned in the following content of this disclosure are all filters in this filtering model, and will not be elaborated further thereafter.

[0065] Specifically, taking a cloud server as the execution entity of this disclosure as an example, the method provided in step S203 can be implemented through the following sub-steps:

[0066] S2031. Input the cached data into at least two filters to obtain the information for each filter.

[0067] In this configuration, at least two filters have different initial mounting angles. For example, a mobile device may include four filters, which are initialized with different initial mounting angles, and all other parameters of the four filters are initialized to the same value in their initial state. It should be understood that the number of filters included in the mobile device can be determined according to actual needs, and this disclosure does not impose any limitations on this.

[0068] Optionally, the initial mounting angles between the at least two filters can be equal or unequal. For example, the initial mounting angle of filter 1 differs from that of filter 2 by 30 degrees, the initial mounting angles of filter 2 and filter 3 differ by 60 degrees, the initial mounting angles of filter 3 and filter 4 differ by 90 degrees, and the initial mounting angles of filter 4 and filter 1 differ by 180 degrees. Alternatively, the initial mounting angles between each filter can all differ by 90 degrees. Both of these cases can be set according to actual needs, and this disclosure does not impose any restrictions on them.

[0069] During the target acquisition period, at least two initialized filters are run starting from the initial moment of the target acquisition period, and the observation updates within these filters (i.e., the difference between the initial value and the converged value) are statistically analyzed during the target acquisition period. For example, the sum of the absolute values ​​of these updates can be calculated.

[0070] S2032. Determine the target filter from the filters whose innovation is less than or equal to the preset innovation threshold.

[0071] The magnitude relationship between the news feeds of at least two filters is determined by summing the absolute values ​​of the news feeds of each filter. For example, the magnitude relationship between the news feeds of at least two filters can be determined by sorting the sums of the absolute values ​​of the news feeds of at least two filters.

[0072] While theoretically all filters may eventually converge to the actual installation angle of the mobile device on the vehicle, in practice, due to various factors (such as noise, initial error distribution, and data fluctuations), the convergence speed and stability of these filters may vary. By setting a preset innovation threshold, selecting filters whose sum of absolute innovation values ​​is less than the preset threshold means that the difference between the observed and predicted values ​​of filters whose innovation is less than or equal to the preset threshold is smaller. This indicates that their estimation of the actual installation angle is more accurate and reliable, and can better reflect the situation closest to the actual installation angle under the current state. Therefore, a target filter can be determined from at least one filter whose innovation is less than or equal to the preset innovation threshold, thereby improving the accuracy of predicting the actual installation angle.

[0073] Optionally, the filter with the smallest innovation among those filters whose innovation is less than or equal to a preset innovation threshold can be selected as the target filter. The filter with the smallest innovation exhibits the smallest difference between the observed and predicted values, indicating that its estimate of the actual installation angle is the most accurate and reliable among these filters. Using this filter with the smallest innovation as the target filter better reflects the situation closest to the actual installation angle under the current condition, further improving the accuracy of predicting the actual installation angle.

[0074] S2033. Based on the installation angle information, arm information, and covariance matrix estimated by the target filter, determine the initial values ​​of the installation angle, arm, installation angle, and arm.

[0075] Based on the installation angle and lever information estimated by the target filter, the initial values ​​of the installation angle and lever for the mobile device are determined. Then, based on the installation angle and lever information estimated by the target filter, the corresponding covariance matrix is ​​determined, and based on this covariance matrix, the initial covariance of the installation angle and the initial covariance of the lever are determined.

[0076] Alternatively, the initial values ​​of the installation angle and the lever arm can be determined based on the installation angle and lever arm information estimated by the target filter. Then, the initial covariance of the installation angle and the initial covariance of the lever arm can be determined based on the initial installation angle and the initial value of the lever arm, respectively.

[0077] By utilizing the above sub-steps, the information generated during the operation of multiple filters with different initial installation angle values ​​can be used to determine the target filter with the highest accuracy in estimating the actual installation angle. Based on the estimation results of this target filter, the initial installation angle, initial lever value, initial installation angle covariance, and initial lever covariance of the mobile device can be determined. This improves the accuracy of obtaining the distance vectors corresponding to the installation angle and lever of the mobile device relative to the vehicle, and reduces the errors in position calculation based on IMU data caused by the difficulty in predicting the distance vectors corresponding to the installation angle and lever of the mobile device relative to the vehicle, or the low accuracy of obtaining the distance vectors corresponding to the installation angle and lever of the mobile device relative to the vehicle. This, in turn, improves the accuracy of positioning.

[0078] As mentioned earlier, to improve the accuracy of the mounting angle of the mobile device relative to the vehicle and the distance vector corresponding to the lever arm, it is necessary to configure different initial mounting angles for at least two filters in the filtering model (such as filters implemented using the Kalman filter algorithm) to facilitate the determination of the target filter based on the information of each filter. The interval between the initial mounting angles of these at least two filters can be equal or unequal. However, how to configure different initial mounting angles for at least two filters on the mobile device to improve the efficiency of obtaining the distance vector corresponding to the mounting angle of the mobile device relative to the vehicle and the lever arm is a problem that urgently needs to be solved.

[0079] To address this problem, this disclosure also provides a method for initializing the initial mounting angle of a filter. Based on the method shown in Figure 2 above, prior to steps S2031 to S2033, the initial mounting angles of at least two filters on the mobile device can also be initialized using the following method.

[0080] The initial installation angles of at least two filters are set at equal angular intervals. The filters are initialized when GNSS observations are favorable. The initial installation angles of the at least two filters are determined based on the installation angle range when the mobile device is fixed to the vehicle, and the number of filters on the mobile device. Typically, this installation angle range is 360 degrees, covering all possible installation angles for mounting the mobile device on the vehicle. The distance between the initial installation angles of each filter is determined by dividing this installation angle range by the number of filters. The filters corresponding to each initial installation angle are then initialized based on these initial installation angles.

[0081] For example, taking a filtering model comprising 12 filters, the difference in the initial mounting angle between any two adjacent filters can be 30 degrees. That is, an initial mounting angle value is set every 30 degrees, and these values ​​are assigned to different filters. This allows at least one of the 12 filters to have an initial mounting angle closer to the actual mounting angle, converging more quickly and thus improving the efficiency of determining the target filter.

[0082] The method provided in this disclosure initializes the initial installation angles of multiple filters in a filtering model at equal angular intervals. Based on the information generated by the initialized filters, a target filter with higher accuracy is determined. The initial values ​​of the installation angle, the lever arm, the initial covariance of the installation angle, and the initial covariance of the lever arm are obtained from the estimation results of the target filter, representing the state of the mobile device fixed on the carrier. Setting the initial installation angles of multiple filters at equal angular intervals allows the initial installation angle of at least one filter to be closer to the actual installation angle, converging to the actual installation angle more quickly, thereby improving the efficiency of determining the target filter. This further improves the efficiency of determining the initial installation angle and lever arm values ​​based on the target filter, enhancing both positioning accuracy and efficiency.

[0083] As mentioned earlier, in real-world scenarios where users use mobile devices for location tracking while driving, the user may have the device fixed to the vehicle (i.e., the device is fixed to the vehicle), or the user may pick up or move the device during the location process (i.e., the device is not fixed to the vehicle). This results in the device's state constantly switching between these two states. In scenarios where the device's state is switching, if a filtering model corresponding to the device's state cannot be used in real time, it will lead to significant location errors.

[0084] To address this issue, this disclosure provides another positioning method. The following describes the positioning method based on two implementation methods: a filtering model with pre-configured vehicle constraints and a filtering model without vehicle constraints on the executing entity; or, a filtering model is pre-configured, and during the execution of the method of this disclosure, the parameters of the model are adjusted according to the state of the mobile device to generate a model that matches the state of the mobile device.

[0085] Implementation method 1: Pre-configure a filtering model with vehicle constraints and a filtering model without vehicle constraints.

[0086] The system acquires data from sensors deployed on mobile devices and determines the mobile device's state based on IMU data within this data. When a change in the mobile device's state is detected, the filtering model used to process the sensor output data is switched. For example, model switching can be achieved by changing the interface that calls the filtering model, between calling a filtering model with vehicle constraints and one without. Alternatively, the input location of the sensor output data can be changed to switch the data to another filtering model for processing.

[0087] For example, when the state of the mobile device changes from being fixed to a vehicle to being unfixed to a vehicle, the data output by the sensors deployed on the mobile device is input into a filtering model without vehicle constraints for filtering calculation to obtain the positioning data of the mobile device; when the state of the mobile device changes from being unfixed to being fixed to a vehicle, the data output by the sensors deployed on the mobile device is input into a filtering model with vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0088] Implementation Method 2: Pre-configure a dynamic filtering model that can be transformed into a filtering model with vehicle constraints or a filtering model without vehicle constraints.

[0089] The system acquires data from sensors deployed on the mobile device and determines the device's state based on IMU data within this data. When a change in the mobile device's state is detected, the model parameters of the dynamic filtering model are adjusted to transform it into a filtering model corresponding to the mobile device's state. The sensor output data is then processed by the transformed filtering model to obtain the mobile device's location data.

[0090] One possible implementation is that the dynamic model may include a first state vector, an on-board constraint vector, a first covariance matrix, and an on-board constraint covariance matrix. The on-board constraint vector includes an installation angle state vector and a lever arm state vector; the on-board constraint covariance matrix includes the initial covariance of the installation angle and the initial covariance of the lever arm. The cloud server can control the values ​​of the on-board constraint vector and the on-board constraint covariance matrix to achieve the transition between a filtering model with on-board constraints and a filtering model without on-board constraints.

[0091] Below, we will take the mobile device state switching scenario as an example to introduce the specific details of implementation method 2.

[0092] Scenario 1: The mobile device switches from being fixed to a vehicle to being unfixed to a vehicle.

[0093] Figure 3 is a flowchart illustrating another positioning method provided in this embodiment of the present disclosure. As shown in Figure 3, the method may further include the following steps:

[0094] S301. Obtain the filtering model with vehicle constraints corresponding to the state of the mobile device fixed on the vehicle.

[0095] By utilizing IMU and GNSS data collected during the mobile device's fixed-on-vehicle state, the following 21-dimensional state vectors are acquired: mobile device attitude state vector, mobile device velocity state vector, mobile device position state vector, gyroscope zero-bias state vector, accelerator zero-bias state vector, mounting angle state vector, and lever state vector. The covariance of each vector is then calculated to generate a first covariance matrix and a vehicle-mounted constraint covariance matrix. Alternatively, the 21-dimensional state vectors generated by the mobile device can be directly obtained, and their corresponding covariances calculated to generate the first covariance matrix and the vehicle-mounted constraint covariance matrix.

[0096] The filtering model with vehicle constraints is determined based on the 21-dimensional state vector, the first covariance matrix, and the vehicle constraint covariance matrix.

[0097] S302. Modify the values ​​of the vehicle constraint vector and vehicle constraint covariance matrix in the filtering model with vehicle constraints to the first value, so as to convert the filtering model with vehicle constraints into a filtering model without vehicle constraints.

[0098] The installation angle state vector is related to the initial value of the installation angle, and the lever arm state vector is related to the initial value of the lever arm.

[0099] Since the mobile device is no longer fixed to the vehicle after switching to the unfixed state, there are no mounting angles or levers. Therefore, the unconstrained filtering model does not need to rely on the mounting angle state vector and lever state vector, as well as their corresponding covariance, to perform filtering calculations on IMU and GNSS data. Therefore, the values ​​of the vehicle constraint vector and vehicle constraint covariance matrix in the vehicle-constrained filtering model need to be modified to the first value so that the vehicle constraint vector and vehicle constraint covariance matrix do not participate in the filtering calculation. At this point, only the first state vector and the first covariance matrix participate in the filtering calculation in the model parameters, meaning the filtering model has been transformed from a vehicle-constrained filtering model to an unconstrained filtering model.

[0100] The first value indicates that the model parameters containing the first value are not used in the filtering calculation. This first value can be, for example, 0, or a preset specific value, such as a letter. When the model parameters are set to the first value, these model parameters do not participate in the filtering calculation when using the filtering model. This first value can be set according to actual needs, and this disclosure does not impose any restrictions on it.

[0101] Scenario 2: The mobile device switches from a state where it is not fixed to the vehicle to a state where it is fixed to the vehicle.

[0102] Figure 4 is a flowchart illustrating another positioning method provided in an embodiment of this disclosure. As shown in Figure 4, the method may include the following steps:

[0103] S401. Obtain the unconstrained filtering model corresponding to the state where the mobile device is not fixed to the vehicle, and obtain the initial values ​​of the mounting angle, the initial value of the lever arm, the initial covariance of the mounting angle, and the initial covariance of the lever arm when the mobile device is fixed to the vehicle.

[0104] The unconstrained filtering model is the aforementioned dynamic filtering model. The model parameters of this dynamic filtering model include not only the first state vector and first covariance matrix of the unconstrained filtering model, but also the vehicle-constrained state vector and vehicle-constrained covariance matrix. In this case, the values ​​of the vehicle-constrained state vector and vehicle-constrained covariance matrix in the dynamic filtering model are both the first values ​​mentioned in step S302.

[0105] By utilizing IMU and GNSS data from when the mobile device is not fixed to the vehicle, we acquire the mobile device's attitude state vector, velocity state vector, position state vector, gyroscope zero-bias state vector, and accelerator zero-bias state vector. We then calculate the covariance of each of these 15-dimensional state vectors to determine the values ​​in the first covariance matrix. Alternatively, we can directly obtain the 15-dimensional state vectors generated by the mobile device and calculate their respective covariances to determine the values ​​in the first covariance matrix. Or, we can directly obtain the 15-dimensional state vectors and the first covariance matrix generated by the mobile device. A vehicle-unconstrained filtering model is then constructed based on these 15-dimensional state vectors and the first covariance matrix.

[0106] S402. Based on the initial values ​​of the mounting angle, the lever arm, the initial covariance of the mounting angle, and the initial covariance of the lever arm in the state of the mobile device fixed on the vehicle, modify the vehicle constraint vector and the vehicle constraint covariance matrix with the first value in the filter model without vehicle constraints, so as to convert the filter model without vehicle constraints into a filter model with vehicle constraints.

[0107] Based on the initial values ​​of the mounting angle and the lever arm when the mobile device is fixed to the vehicle, the values ​​of the vehicle constraint vector are determined, and these values ​​replace the original first value in the vehicle constraint vector. Based on the initial covariance of the mounting angle and the initial covariance of the lever arm when the mobile device is fixed to the vehicle, the values ​​of the vehicle constraint covariance matrix are determined, and these values ​​replace the original first value in the vehicle constraint covariance matrix. This transforms the filtering model without vehicle constraints into a filtering model with vehicle constraints.

[0108] The method provided in this disclosure obtains the state of whether the mobile device is fixed on a vehicle, and dynamically adjusts the values ​​of the model parameters of the dynamic model according to the different states, so that the dynamic model can adaptively transform into the corresponding filtering model according to the state of the mobile device. When the mobile device is not fixed on a vehicle, filtering calculations are performed on GNSS data and IMU data using a filtering model without vehicle constraints, which can avoid incorrect constraints on the attitude of the mobile device by the vehicle and reduce the probability of errors in positioning data. When the mobile device is fixed on a vehicle, filtering calculations are performed on GNSS data and IMU data using a filtering model with vehicle constraints, which incorporates the constraint of the vehicle on the position estimation of the mobile device, which can improve the accuracy of the filtering model in obtaining positioning data. Thus, the accuracy of filtering calculations on IMU data and GNSS data when the state of the mobile device on the vehicle changes is improved, thereby improving the accuracy and efficiency of positioning.

[0109] Figure 5 is a schematic diagram of a positioning device provided in an embodiment of this disclosure. As shown in Figure 5, the device may include: an acquisition module 11, a processing module 12, a first control module 13, and a second control module 14.

[0110] The acquisition module 11 is used to acquire data output by sensors mounted on the mobile device, including GNSS data output by the Global Navigation Satellite System (GNSS) chip and IMU data output by the inertial sensor.

[0111] The processing module 12 is used to determine the state of the mobile device based on the IMU data. If the state is that the mobile device is not fixed to the vehicle, the first control module is triggered. If the state is that the mobile device is fixed to the vehicle, the second control module is triggered.

[0112] The first control module 13 is used to input the data output by the sensor into a filtering model without vehicle constraints for filtering calculation, so as to obtain the positioning data of the mobile device;

[0113] The second control module 14 is used to input the data output by the sensor into a filtering model with vehicle constraints for filtering calculation to obtain the positioning data of the mobile device.

[0114] In one possible implementation, the model parameters of the unconstrained filtering model include a first state vector and a first covariance matrix. The first state vector includes a mobile device attitude state vector, a mobile device velocity state vector, a mobile device position state vector, a gyroscope zero-bias state vector, and an accelerator zero-bias state vector. The model parameters of the constrained filtering model include the first state vector, a vehicle constraint vector, a first covariance matrix, and a vehicle constraint covariance matrix. The vehicle constraint vector includes an installation angle state vector and a lever arm state vector. The vehicle constraint covariance matrix includes the initial covariance of the installation angle and the initial covariance of the lever arm.

[0115] In this implementation, optionally, if the mobile device is fixed on the vehicle, before the second control module 14 inputs the sensor output data into the vehicle-constrained filtering model for filtering calculation, the processing module 12 is also used to cache the sensor output data until the caching time reaches a preset time threshold. Based on the GNSS data in the cached data, it is determined whether the heading of the mobile device is stable. If the heading of the mobile device is stable, the cached data is input into at least two filters to obtain the initial value of the installation angle, the initial value of the boom arm, the initial covariance of the installation angle, and the initial covariance of the boom arm of the mobile device. The initial installation angle value of the filter is equal to the set value, and the initial installation angle values ​​of any two filters are different.

[0116] Optionally, the processing module 12 is specifically used to input the cached data into at least two filters to obtain the innovation of each filter. A target filter is determined from the filters whose innovation is less than or equal to a preset innovation threshold. Based on the installation angle information, lever information, and covariance matrix estimated by the target filter, the initial values ​​of the installation angle, lever, initial covariance of the installation angle, and initial covariance of the lever for the mobile device are determined.

[0117] In one possible implementation, the processing module 12 is specifically used to select the filter with the smallest innovation from the filters whose innovation is less than or equal to a preset innovation threshold as the target filter.

[0118] In any of the above implementations, the first control module 13 is further configured to, when the state changes from the mobile device being fixed to the vehicle to the mobile device not being fixed to the vehicle, input the data output by the sensor into the unconstrained filtering model for filtering calculation to obtain the positioning data of the mobile device. When the state changes from the mobile device not being fixed to the vehicle to the mobile device being fixed to the vehicle, input the data output by the sensor into the constrained filtering model for filtering calculation to obtain the positioning data of the mobile device.

[0119] If the state is that the mobile device is not fixed to the vehicle or the state is switched from the mobile device being fixed to the vehicle to the mobile device not being fixed to the vehicle, the processing module 12 is further configured to modify the vehicle constraint vector and the vehicle constraint covariance matrix value in the filtering model with vehicle constraints to the first value, so as to convert the filtering model with vehicle constraints into the filtering model without vehicle constraints.

[0120] The positioning device provided in this embodiment can execute the positioning method in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0121] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. This electronic device can, for example, be used to execute the aforementioned positioning method. As shown in Figure 6, the electronic device 600 may include at least one processor 601 and a memory 602. In one possible implementation, it may also include a communication interface 603.

[0122] The memory 602 is used to store programs. Specifically, the program may include program code, which includes computer operation instructions.

[0123] The memory 602 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0124] The processor 601 is used to execute computer execution instructions stored in the memory 602 to implement the method described in the foregoing method embodiments. The processor 601 may be a CPU (Central Processing Unit), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this disclosure.

[0125] The processor 601 can communicate and interact with external devices through the communication interface 603. These external devices can be, for example, the aforementioned mobile devices. In specific implementations, if the communication interface 603, memory 602, and processor 601 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0126] Optionally, in a specific implementation, if the communication interface 603, memory 602, and processor 601 are integrated on a single chip, then the communication interface 603, memory 602, and processor 601 can communicate through an internal interface.

[0127] This disclosure also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions that are used in the methods described in the above embodiments.

[0128] This disclosure also provides a program product including executable instructions stored in a readable storage medium. At least one processor of an electronic device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the electronic device to implement the positioning methods provided in the various embodiments described above.

[0129] The term "multiple" in this document refers to two or more. The term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects; in formulas, the character " / " indicates a "division" relationship between the preceding and following related objects. Additionally, it should be understood that in the descriptions of this disclosure, terms such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order.

[0130] It is understood that the various numerical designations used in the embodiments of this disclosure are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this disclosure.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A positioning method, wherein, The method comprises: obtaining sensor output data carried by a mobile device, the data comprising global navigation satellite system (GNSS) data output by a GNSS chip and inertial measurement unit (IMU) data output by an inertial sensor; determining a state of the mobile device based on the IMU data; if the state is that the mobile device is not fixed on a carrier, inputting the sensor output data into a filter model without carrier constraints for filtering calculation to obtain positioning data of the mobile device; if the state is that the mobile device is fixed on a carrier, inputting the sensor output data into a filter model with carrier constraints for filtering calculation to obtain positioning data of the mobile device.

2. The method of claim 1, wherein, The model parameters of the filter model without carrier constraints comprise a first state vector and a first covariance matrix, and the first state vector comprises a mobile device attitude state vector, a mobile device velocity state vector, a mobile device position state vector, a gyroscope bias state vector, and an accelerometer bias state vector. The model parameters of the filter model with carrier constraints comprise the first state vector, a carrier constraint vector, the first covariance matrix, and a carrier constraint covariance matrix, wherein the carrier constraint vector comprises a mounting angle state vector and a lever arm state vector, and the carrier constraint covariance matrix comprises a mounting angle initial covariance and a lever arm initial covariance.

3. The method of claim 2, wherein, If the state is that the mobile device is fixed on a carrier, before inputting the sensor output data into the filter model with carrier constraints for filtering calculation, the method further comprises: buffering the sensor output data until a buffering time length reaches a preset time threshold; determining whether a heading of the mobile device is stable based on GNSS data in the buffered data; if the heading of the mobile device is stable, inputting the buffered data into at least two filters to obtain a mounting angle initial value, a lever arm initial value, a mounting angle initial covariance, and a lever arm initial covariance of the mobile device, an angle value of an initial mounting angle of the filters being equal to a set value and angle values of initial mounting angles of any two filters being different.

4. The method of claim 3, wherein, The inputting of the buffered data into the at least two filters to obtain the mounting angle initial value, the lever arm initial value, the mounting angle initial covariance, and the lever arm initial covariance of the mobile device comprises: inputting the buffered data into the at least two filters to obtain innovation of each filter; determining a target filter from the filters with innovation less than or equal to a preset innovation threshold; determining the mounting angle initial value, the lever arm initial value, the mounting angle initial covariance, and the lever arm initial covariance of the mobile device according to mounting angle information, lever arm information, and a covariance matrix estimated by the target filter.

5. The method of claim 4, wherein, The determining of the target filter from the filters with innovation less than or equal to the preset innovation threshold comprises: selecting a filter with minimum innovation as the target filter from the filters with innovation less than or equal to the preset innovation threshold.

6. The method of any of claims 1-5, wherein, The method further comprises: when the state is switched from the mobile device being fixed on a vehicle to the mobile device not being fixed on a vehicle, inputting the data output by the sensor into the filter model without vehicle constraints for filter calculation to obtain positioning data of the mobile device; when the state is switched from the mobile device not being fixed on a vehicle to the mobile device being fixed on a vehicle, inputting the data output by the sensor into the filter model with vehicle constraints for filter calculation to obtain positioning data of the mobile device.

7. The method of claim 6, wherein, If the state is the mobile device not being fixed on a vehicle or the state is switched from the mobile device being fixed on a vehicle to the mobile device not being fixed on a vehicle, the method further comprises: modifying the vehicle constraint vector and the vehicle constraint covariance matrix value in the filter model with vehicle constraints to a first value to convert the filter model with vehicle constraints into the filter model without vehicle constraints.

8. A positioning device, wherein, The device comprises: an acquisition module configured to acquire data output by a sensor carried by a mobile device, the data comprising global navigation satellite system (GNSS) data output by a GNSS chip and inertial measurement unit (IMU) data output by an inertial sensor; a processing module configured to determine a state of the mobile device based on the IMU data, and trigger a first control module if the state is the mobile device not being fixed on a vehicle, or trigger a second control module if the state is the mobile device being fixed on a vehicle; the first control module is configured to input the data output by the sensor into a filter model without vehicle constraints for filter calculation to obtain positioning data of the mobile device; the second control module is configured to input the data output by the sensor into a filter model with vehicle constraints for filter calculation to obtain positioning data of the mobile device.

9. An electronic device, comprising: comprise: a processor and a memory; the processor is in communication connection with the memory; the memory stores computer-executed instructions; the processor executes the computer-executed instructions stored in the memory to implement the method according to any one of claims 1-7.

10. A computer readable storage medium, wherein, The computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by the processor to implement the method according to any one of claims 1-7.

11. A computer program product, wherein, comprise computer programs or instructions, and the computer programs or instructions are executed by the processor to implement the method according to any one of claims 1-7.

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