Positioning method and device, storage medium and program product

Abstract

The invention provides a positioning method and device, a storage medium and a program product, and the method comprises the steps: obtaining data outputted by a sensor carried by a mobile device, the data comprising GNSS data outputted by a GNSS chip and inertial measurement unit IMU data outputted by an inertial sensor; determining the state of the mobile device based on the IMU data; if the state is that the mobile equipment is not fixed on the carrier, inputting data output by the sensor into a filtering model without vehicle-mounted constraint for filtering calculation so as to obtain positioning data of the mobile equipment; and if the state is that the mobile equipment is fixed on the carrier, inputting data output by the sensor into a filtering model with vehicle-mounted constraints for filtering calculation so as to obtain positioning data of the mobile equipment. According to the technical scheme provided by the invention, based on the state of the mobile equipment, different filtering models are adopted to position the mobile equipment, so that the positioning accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the positioning technical field, and in particular to a positioning method and device, a storage medium and a program product. BACKGROUND

[0002] At present, navigation by using a navigation service provided by an application installed on a mobile device has become a widespread choice for users when driving. The implementation of the navigation service relies on the real-time position of the user. In order to achieve more accurate determination of the real-time position of the user, in the driving scenario, the data output by the inertial sensor and the satellite positioning chip carried by the mobile device are usually fused to position the mobile device, so as to obtain the real-time position of the mobile device, which is equivalent to the real-time position of the user. This positioning method is suitable for a wider range of scenarios, and can effectively improve the accuracy and reliability of positioning, especially in some outdoor areas where satellite positioning signals are shielded, such as tunnels and canyons. However, this positioning method relies on the attitude of the mobile device, and in the driving scenario, the attitude of the mobile device may change due to the user's behavior of using the mobile device. Therefore, in the driving scenario, how to ensure the accuracy of positioning based on the attitude of the mobile device is a problem to be solved. SUMMARY

[0003] The present application provides a positioning method, device, storage medium and program product, which can improve the accuracy of positioning.

[0004] In a first aspect, the present application provides a positioning method, which comprises:

[0005] obtaining data output by a sensor carried by a mobile device, wherein the data comprises global navigation satellite system (GNSS) data output by a GNSS chip and inertial measurement unit (IMU) data output by an inertial sensor;

[0006] determining the state of the mobile device based on the IMU data;

[0007] if the state is that the mobile device is not fixed on a vehicle, inputting the data output by the sensor into a filter model without vehicle constraints for filter calculation to obtain positioning data of the mobile device;

[0008] if the state is that the mobile device is fixed on a vehicle, inputting the data output by the sensor into a filter model with vehicle constraints for filter calculation to obtain positioning data of the mobile device.

[0009] In a second aspect, the present application provides a positioning device, which comprises:

[0010] An acquisition module is configured to acquire data output by sensors carried by a mobile device, the data including GNSS data output by a GNSS chip and IMU data output by an inertial sensor;

[0011] A processing module is configured to determine a state of the mobile device based on the IMU data, and trigger a first control module if the state is that the mobile device is not fixed on a carrier, and trigger a second control module if the state is that the mobile device is fixed on a carrier;

[0012] The first control module is configured to input the data output by the sensors into a filter model without vehicle constraints for filter calculation, to obtain positioning data of the mobile device;

[0013] The second control module is configured to input the data output by the sensors into a filter model with vehicle constraints for filter calculation, to obtain positioning data of the mobile device.

[0014] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the processor is communicatively connected with the memory;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes the computer-executed instructions stored in the memory, to implement the method according to any one of the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by a processor to implement the method according to any one of the first aspect.

[0018] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the method according to any one of the first aspect.

[0019] The technical scheme provided in the application determines the state of the mobile device through the IMU data output by the sensor carried by the mobile device, inputs the GNSS data and the IMU data output by the sensor into a filter model without vehicle constraints if the state of the mobile device is not fixed on the vehicle, and inputs the GNSS data and the IMU data output by the sensor into a filter model with vehicle constraints if the state of the mobile device is fixed on the vehicle. Since the state of the mobile device is not fixed on the vehicle, the posture of the mobile device relative to the vehicle is uncertain, at this time, the GNSS data and the IMU data are filtered and calculated through the filter model without vehicle constraints, so that incorrect constraints of the vehicle on the posture of the mobile device can be avoided, and the probability of errors in the positioning data can be reduced. Since the state of the mobile device is fixed on the vehicle, the posture of the mobile device relative to the vehicle is determined, at this time, the constraint of the vehicle on the posture of the mobile device is also determined, the GNSS data and the IMU data are filtered and calculated through the filter model with vehicle constraints, the constraint of the vehicle on the position calculation of the mobile device is added, and the accuracy of the positioning data obtained by the filter model can be improved. The technical scheme of the application improves the accuracy of the positioning data obtained by the mobile device in different states. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical scheme in the application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flowchart of a positioning method provided by an embodiment of the application is shown in the figure.

[0022] Figure 2 A flowchart of another positioning method provided by an embodiment of the application is shown in the figure.

[0023] Figure 3 A flowchart of another positioning method provided by an embodiment of the application is shown in the figure.

[0024] Figure 4 A flowchart of another positioning method provided by an embodiment of the application is shown in the figure.

[0025] Figure 5 A structural diagram of a positioning device provided by an embodiment of the application is shown in the figure.

[0026] Figure 6 A structural diagram of an electronic device provided by an embodiment of the application is shown in the figure.

[0027] The specific embodiments of the present application have been shown and described in the above drawings, and will be described in more detail in the following. These drawings and the written description are not intended to restrict the scope of the present application in any way, but to explain the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0028] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely in the following by combining the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative work fall within the protection scope of the present application.

[0029] In order to facilitate understanding, the terms involved in the present application are introduced first:

[0030] Inertial Navigation System (INS): The basic working principle is based on Newton's law of mechanics, by measuring the acceleration of the carrier in the inertial reference frame, integrating it with respect to time, and transforming it into the navigation coordinate system, to obtain the velocity, yaw angle and position of the carrier in the navigation coordinate system.

[0031] Inertial Measurement Unit (IMU): used to measure the three-axis attitude angle (or angular rate) and acceleration of the carrier, and is the core unit of the inertial navigation system.

[0032] Global Navigation Satellite System (GNSS): refers to all satellite navigation systems, including global, regional and enhanced, such as GPS of the United States, Glonass of Russia, Galileo of Europe, Beidou satellite navigation system of China, and related enhanced systems, such as WAAS (Wide Area Augmentation System) of the United States, EGNOS (European Geostationary Navigation Overlay Service) of Europe and MSAS (Multi-functional Satellite Augmentation System) of Japan, etc.

[0033] With the development of technology, more and more application software can provide location-based services (LBS) for users, therefore, accurately determining the location of the user becomes a guarantee for the quality of LBS services. Navigation service is one of the typical services of LBS, for example, when driving, the user will use the application program installed on the mobile device (such as mobile phone, tablet computer and other mobile devices) with navigation function to navigate, and the premise of providing accurate navigation service for the user is to accurately determine the location of the user driving the vehicle. Because when driving, the mobile device, the user and the vehicle are in the same space, the position of the mobile device can be used as the position of the user and the vehicle driven by the user, so accurately determining the position of the mobile device is crucial for navigation service. However, when driving, due to the different quality of GNSS signals in different areas, in areas such as tunnels, high mountains or high-rise buildings, GNSS signals will be reduced in quality due to shielding, reflection and other factors, and it is impossible to position the mobile device only through GNSS signals. In order to ensure that the position of the mobile device can still be determined in these areas, the existing technology usually determines the position of the mobile device based on the IMU data measured by the inertial sensor carried by the mobile device and the GNSS data output by the GNSS chip, but in this scenario, there are some problems to be solved, for example, the posture of the mobile device in the vehicle will change when the user picks up or uses it, and the change of the posture of the mobile device will obviously affect the accuracy of the position calculated based on the IMU data; further, even if the mobile device is relatively static with the vehicle (that is, the mobile device is fixed on the vehicle, for example, fixed on the support of the vehicle or placed in a position of the vehicle that will not cause the mobile device to move, or the user stably holds the mobile device), that is, the posture of the mobile device relative to the vehicle is fixed, but at this time, the installation angle of the mobile device relative to the vehicle and the distance vector corresponding to the lever arm (that is, the distance vector between the IMU installation position and the reference point of the vehicle (such as the center of mass of the vehicle), which includes the length and direction of the lever arm and other information) are difficult to predict, which will also cause errors in the position calculated based on the IMU data and reduce the accuracy of positioning. Therefore, in the driving scenario, how to more accurately determine the position of the mobile device, that is, to ensure the accuracy of the positioning of the mobile device, is a problem to be solved.

[0034] Therefore, the application provides a positioning method. The IMU data and the GNSS data are input into different filtering models for filtering calculation by detecting the state of the mobile device and the vehicle, so as to improve the positioning accuracy. The execution subject of the positioning method can be an electronic device, for example, a mobile device such as a smart phone, a tablet computer, and the like, which is not fixed on a vehicle. The vehicle can be a traffic vehicle such as a vehicle and an electric vehicle. The electronic device can deploy software or program code to execute or trigger the positioning method. The execution subject of the positioning method can also be a server. The server generates positioning data of the mobile device by processing the GNSS data and the IMU data obtained from the mobile device, and feeds back the positioning data to the mobile device. Whether the mobile device or the server is the execution subject does not affect the implementation process of the application.

[0035] The technical solutions of the application will be described in detail below with reference to specific embodiments. The specific embodiments of the application can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments.

[0036] Figure 1 A flowchart of a positioning method provided by an embodiment of the application is shown in FIG. 1. As shown in FIG. 1, the method can include the following steps: Figure 1

[0037] S101, acquiring data output by a sensor carried by a mobile device, wherein the data includes GNSS data output by a global navigation satellite system (GNSS) chip and inertial measurement unit (IMU) data output by an inertial sensor.

[0038] The mobile device can collect GNSS data in real time through the GNSS chip deployed on the mobile device, and collect IMU data in real time through the inertial sensor deployed on the mobile device. The inertial sensor can include an accelerometer, a gyroscope, and the like. Correspondingly, the IMU data can include gravity axis data output by the accelerometer and data output by the gyroscope.

[0039] If the execution subject of the method is a server, the GNSS data and the IMU data can be actively uploaded to the server by the mobile device in a period.

[0040] S102, determining the state of the mobile device based on the IMU data. If the state of the mobile device is that the mobile device is not fixed on a vehicle, step S103 is performed. If the state of the mobile device is that the mobile device is fixed on a vehicle, step S104 is performed.

[0041] ​The state of the mobile device can be determined by the IMU data. When the fluctuation range of the IMU data within a certain observation time length is less than or equal to a corresponding data fluctuation threshold, it can be considered that the mobile device is relatively stationary with the vehicle, i.e., the state of the mobile device is fixed on the vehicle. The mobile device fixed on the vehicle can be understood as that the mobile device is stably held in the hand of the user, so that the mobile device does not move relative to the vehicle or the mobile device is actually fixed on the vehicle. The mobile device fixed on the vehicle may, for example, be fixed on a support of the vehicle or fixed in a specific area of the vehicle. Conversely, it can be considered that the mobile device is not relatively stationary relative to the vehicle, i.e., the state of the mobile device is not fixed on the vehicle. The reason why the mobile device is not fixed on the vehicle may be that the mobile device originally fixed on the vehicle is taken by the user or placed in a specific area of the vehicle, but the specific area cannot completely fix the mobile device, and with the movement of the vehicle (such as sharp turning or sharp deceleration), the attitude of the mobile device changes (such as the upper end of the mobile device originally pointing to the front of the vehicle changes to pointing to the rear of the vehicle). The observation time length and the data fluctuation threshold can be set according to actual needs, and the present application does not limit this.

[0042] Regardless of whether the execution subject of the method of the present application is a server or a mobile device, in the actual implementation of the method of the present application, the vehicle-constrained filtering model and the non-vehicle-constrained filtering model can be pre-configured on the execution subject, or a filtering model is pre-configured, and in the process of executing the method of the present application, the parameters of the model are adjusted according to the state of the mobile device to generate a model matched with the state of the mobile device.

[0043] S103, inputting the data output by the sensor into the non-vehicle-constrained filtering model for filtering calculation to obtain positioning data of the mobile device.

[0044] The positioning data of the mobile device obtained by the filtering calculation can include the position, speed, heading, and zero offset of the mobile device. As described above, the position of the mobile device can also be regarded as the position of the vehicle on which the mobile device is located, i.e., the positioning of the vehicle is achieved.

[0045] The loose combination model of the INS / GNSS combination navigation is taken as an example of the vehicle-free constraint filter model. The model parameters of the vehicle-free constraint filter 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 the attitude of the mobile device itself in a geodetic coordinate system), a mobile device velocity state vector, a mobile device position state vector, a gyro zero offset state vector, and an accelerometer zero offset state vector. Each state vector is a three-dimensional state vector. The first covariance matrix is a covariance matrix generated according to the covariances of the state vectors in the first state vector. The vehicle-free constraint filter model is constructed based on the first state vector and the first covariance matrix. The GNSS data and the IMU data are filtered and calculated through the vehicle-free constraint filter model, and the positioning data of the mobile device can be obtained.

[0046] The filter calculation may be, for example, a filter calculation based on a Kalman filter, or a filter calculation based on an extended filter method of the Kalman filter, or a filter calculation based on a particle filter or other filter method. The specific calculation method of the filter calculation according to the vehicle-free constraint filter model can refer to the prior art, and will not be described here.

[0047] In S104, the data output by the sensor is input into the vehicle-constrained filter model for filter calculation to obtain the positioning data of the mobile device.

[0048] The positioning data is the same as the positioning data of the mobile device obtained by the filter calculation in the foregoing S103, and will not be described here.

[0049] The loose combination model of the INS / GNSS combination navigation is taken as an example of the vehicle-free constraint filter model. The model parameters of the vehicle-free constraint filter 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 the attitude of the mobile device itself in a geodetic coordinate system), a mobile device velocity state vector, a mobile device position state vector, a gyro zero offset state vector, and an accelerometer zero offset state vector. Each state vector is a three-dimensional state vector. The first covariance matrix is a covariance matrix generated according to the covariances of the state vectors in the first state vector. The vehicle-free constraint filter model is constructed based on the first state vector and the first covariance matrix. The GNSS data and the IMU data are filtered and calculated through the vehicle-free constraint filter model, and the positioning data of the mobile device can be obtained.

[0050] For the vehicle constraint vector and the vehicle constraint covariance matrix, since the INS / GNSS integrated navigation loose combination model also needs to introduce the installation angle state vector and the lever arm state vector for the case that the mobile device is fixed on the vehicle, the vehicle constraint vector needs to be introduced in the model parameters, and the vehicle constraint vector includes the installation angle state vector (the installation angle represents the angle deviation between the measurement axis of the IMU of the mobile device and the vehicle coordinate system when the IMU is installed on the vehicle) and the lever arm state vector (the lever arm represents the distance vector between the IMU installation position and the vehicle reference point (such as the vehicle center of mass), which includes the length and direction of the lever arm, and other information), and each of the above state vectors is a three-dimensional state vector. Correspondingly, the vehicle constraint covariance matrix includes the installation angle initial covariance and the lever arm initial covariance, the installation angle initial covariance is determined according to the covariance of the installation angle state vector, and the lever arm initial covariance is determined according to the covariance of the lever arm state vector. The first state vector, the vehicle constraint vector, the first covariance matrix and the vehicle constraint covariance matrix are used to construct the filtering model with vehicle constraints. Through the filtering calculation of the GNSS data and the IMU data by the filtering model with vehicle constraints, the positioning data of the mobile device fixed on the vehicle can be obtained.

[0051] The filtering calculation may be, for example, a filtering calculation based on a Kalman filter, or a filtering calculation based on an extended filtering method of the Kalman filter, or a filtering calculation based on a particle filter or other filtering methods. The specific calculation method of the filtering calculation according to the filtering model without vehicle constraints can refer to the prior art, and will not be described here.

[0052] Since the posture of the mobile device relative to the vehicle is different when the state of the mobile device is different, if the same filtering model is used for filtering calculation, the parameters used for filtering calculation are inaccurate, and the accuracy of the filtering calculation result and the positioning accuracy of the mobile device are poor. Therefore, different filtering models corresponding to different states need to be used for filtering calculation according to the state of the mobile device to improve the positioning accuracy. The method provided in the embodiments of the present application determines the state of the mobile device through the IMU data output by the sensor carried by the mobile device. If the state of the mobile device is not fixed on the vehicle, the GNSS data and the IMU data output by the sensor are input into a filtering model without vehicle constraints. If the state of the mobile device is fixed on the vehicle, the GNSS data and the IMU data output by the sensor are input into a filtering model with vehicle constraints. Since the posture of the mobile device relative to the vehicle is uncertain when the state of the mobile device is not fixed on the vehicle, at this time, the GNSS data and the IMU data are filtered and calculated through the filtering model without vehicle constraints, which can avoid incorrect constraints of the vehicle on the posture of the mobile device and reduce the probability of errors in the positioning data. When the state of the mobile device is fixed on the vehicle, the posture of the mobile device relative to the vehicle is determined, at this time, the constraint of the vehicle on the posture of the mobile device is also determined, and the GNSS data and the IMU data are filtered and calculated through the filtering model with vehicle constraints, which adds the constraint of the vehicle on the position calculation of the mobile device and can improve the accuracy of the positioning data obtained by the filtering model. The technical scheme of the present application improves the accuracy of the positioning data obtained by the mobile device in different states.

[0053] As described above, the posture of the mobile device relative to the vehicle is fixed, but at this time, the installation angle of the mobile device relative to the vehicle and the distance vector corresponding to the rod arm (that is, the distance vector between the IMU installation position and the vehicle reference point (such as the vehicle center of mass), which includes the length and direction of the rod arm and other information) are difficult to predict, which also causes errors in the position calculated based on the IMU data and reduces the positioning accuracy.

[0054] To solve this problem, the present application provides another positioning method, and a flowchart of the method is shown in Figure 2 The difference between the method and the method shown in Figure 1 is that, if the state is that the mobile device is fixed on the vehicle, before the data output by the sensor is input into the filtering model with vehicle constraints for filtering calculation, the method can further include the following steps based on the method shown in Figure 1 .

[0055] S201, cache the data output by the sensor until the cache time length reaches a preset time threshold, wherein the preset time threshold is used to determine whether the heading of the mobile device is stable in a time period corresponding to the preset time threshold.

[0056] By continuously acquiring and caching the data of the sensor output of the mobile device, when the cache duration of the cached data reaches a preset duration threshold, the change of the data included in the cache duration is judged to determine whether the heading of the mobile device is stable in the time period. The preset duration threshold can be determined according to actual needs, and the present application does not limit this.

[0057] If the execution subject of the method is a mobile device, the preset duration threshold can be pre-configured in the mobile device, or determined according to the indication of an external device (such as a cloud server). If the execution subject of the method is a cloud server, the cloud server can continuously acquire the data of the sensor output from the mobile device, which can be actively acquired by the cloud server or uploaded to the cloud server by the mobile device.

[0058] 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, execute step S203; if the heading of the mobile device is not stable, continue to judge whether the heading of the mobile device is stable based on subsequent GNSS data.

[0059] When the installation angle and the lever arm of the mobile device relative to the vehicle have errors with the actual installation situation, these errors will gradually accumulate in the positioning process of the mobile device, resulting in a decrease in the accuracy of positioning, so it is necessary to improve the accuracy of the installation angle and the lever arm of the mobile device relative to the vehicle to improve the accuracy of positioning.

[0060] However, when the heading of the mobile device is unstable, the measurement data of the mobile device will be greatly disturbed, resulting in an increase in the measurement error of the installation angle, the lever arm and other information, thereby affecting the accuracy of positioning. In addition, in a dynamic environment, the motion state of the vehicle is constantly changing, resulting in fluctuations in the data output of the navigation devices such as IMU and GNSS, which increases the complexity and difficulty of data processing, making it more difficult to obtain accurate installation angle, lever arm and other information. Therefore, it is necessary to determine whether the heading of the mobile device is stable based on GNSS data, and further obtain the information of the installation angle and the lever arm of the vehicle constraint vector and the vehicle constraint covariance matrix when the heading of the mobile device is stable, to improve the accuracy of positioning and reduce the difficulty of obtaining the installation angle, the lever arm and other information.

[0061] Specifically, the instantaneous speed and heading of the mobile device can be calculated according to the position information (e.g. latitude, longitude, height, etc.) and time stamp in the GNSS data. If the mobile device can quickly recover and maintain the predetermined heading after being disturbed by the external environment, the heading of the mobile device is stable. For example, if the calculated heading of the GNSS data in the buffered data fluctuates within the preset fluctuation range within the preset time threshold, and can recover to the predetermined heading within the first time after being disturbed by the external environment, it is considered that the heading of the mobile device is stable; if the calculated heading of the GNSS data in the buffered data fluctuates beyond the preset fluctuation range within the preset time threshold, or cannot recover to the predetermined heading within the first time after being disturbed by the external environment, it is considered that the heading of the mobile device is unstable.

[0062] S203, input the buffered data into at least two filters to obtain the initial installation angle value, the initial lever arm value, the initial installation angle covariance and the initial lever arm covariance of the mobile device.

[0063] The initial installation angle value and the initial lever arm value can be obtained by at least two filters in the mobile device, and the initial installation angle covariance is calculated according to the initial installation angle value, and the initial lever arm covariance is calculated according to the initial lever arm value. The angle value of the initial installation angle of the filter is equal to the set value, and the angle values of the initial installation angles of any two filters are different. The filter is a filter in a filter model, for example, a filter implemented based on Kalman filtering algorithm. It should be understood that the filters, target filters, etc. involved in the subsequent content of the present application are all filters in the filter model, which will not be described hereinafter.

[0064] Specifically, taking the cloud server as an example, the method provided in step S203 can be implemented through the following sub-steps:

[0065] S2031, input the buffered data into at least two filters to obtain the innovation of each filter.

[0066] The initial installation angles of the at least two filters are different. For example, the mobile device includes four filters, which are set to different initial installation angles when initialized, and the initial state of the four filters has the same initial value of other parameters. It should be understood that the number of filters included in the mobile device can be determined according to actual needs, which is not limited in the present application.

[0067] Optionally, the intervals between the initial installation angles of the at least two filters can be equal or unequal. For example, the initial installation angle of filter 1 differs from that of filter 2 by 30 degrees, the initial installation angle of filter 2 differs from that of filter 3 by 60 degrees, the initial installation angle of filter 3 differs from that of filter 4 by 90 degrees, and the initial installation angle of filter 4 differs from that of filter 1 by 180 degrees. Alternatively, the initial installation angle of each filter can differ from that of the next filter by 90 degrees. The above two cases can be set according to actual needs, and the present application does not limit this.

[0068] During the target acquisition time period, the initialized at least two filters are run from the initial time of the target acquisition time period, and the innovations (i.e., the difference information between the initial value and the converged value) of the observation updates inside these filters are counted during the target acquisition time period. For example, the sum of the absolute values of these innovations can be counted.

[0069] S2032, determining a target filter from the filters whose innovations are less than or equal to the preset innovation threshold.

[0070] According to the sum of the absolute values of the innovations of the obtained filters, the size relationship between the innovations of the at least two filters is determined. For example, the sum of the absolute values of the innovations of the at least two filters can be sorted to determine the size relationship between the innovations of the at least two filters.

[0071] Although theoretically all filters can eventually converge to the value of the actual installation angle of the mobile device on the vehicle, in actual process, due to the influence of various factors (such as noise, distribution of initial error, fluctuation of data, etc.), the convergence speed and stability of these filters can be different. By presetting the innovation threshold, selecting the filter whose innovation absolute value is less than the preset innovation threshold means that the difference between the observation value and the prediction value of the filter whose innovation is less than or equal to the preset innovation threshold is smaller, which represents that its 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, the target filter can be determined from the at least one filter whose innovation is less than or equal to the preset innovation threshold, so as to improve the accuracy of predicting the actual installation angle.

[0072] Optionally, the filter with the smallest innovation among the filters whose innovations are less than or equal to the preset innovation threshold can be taken as the target filter. The difference between the observation value and the prediction value of the filter with the smallest innovation is the smallest, which represents that the estimation of the actual installation angle of this filter is the most accurate and reliable among these filters. Taking the filter with the smallest innovation as the target filter can better reflect the situation closest to the actual installation angle under the current state, further improving the accuracy of predicting the actual installation angle.

[0073] S2033, determining the initial installation angle value, the initial lever arm value, the initial installation angle covariance, and the initial lever arm covariance of the mobile device according to the installation angle information, the lever arm information, and the covariance matrix estimated by the target filter.

[0074] According to the installation angle information and the lever arm information estimated by the target filter, the initial installation angle value and the initial lever arm value of the mobile device are determined. According to the installation angle information and the lever arm information estimated by the target filter, the corresponding covariance matrix is determined, and according to the covariance matrix, the initial installation angle covariance and the initial lever arm covariance are determined.

[0075] Alternatively, according to the installation angle information and the lever arm information estimated by the target filter, the initial installation angle value and the initial lever arm value of the mobile device are determined. According to the initial installation angle value, the initial installation angle covariance is determined, and according to the initial lever arm value, the initial lever arm covariance is determined.

[0076] Through the method of the above sub-steps, the innovation generated in the running process 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, and the estimation result of the target filter can be used to determine the initial installation angle value, the initial lever arm value, the initial installation angle covariance, and the initial lever arm covariance of the mobile device, thereby improving the accuracy of obtaining the distance vector corresponding to the installation angle and the lever arm of the mobile device relative to the vehicle, reducing the situation that the position calculated based on the IMU data appears to be erroneous due to the difficulty in predicting the distance vector corresponding to the installation angle and the lever arm of the mobile device relative to the vehicle or the low accuracy of the obtained distance vector corresponding to the installation angle and the lever arm of the mobile device relative to the vehicle, and further improving the accuracy of positioning.

[0077] As mentioned above, in order to improve the accuracy of the distance vector corresponding to the installation angle and the lever arm of the mobile device relative to the vehicle, at least two filters (such as filters implemented by Kalman filtering algorithm) in the filtering model need to be configured with different initial installation angles so as to determine the target filter according to the innovation of each filter. The interval between the initial installation angles of the at least two filters can be equal or unequal. However, how to configure different initial installation angles for at least two filters on the mobile device to improve the efficiency of obtaining the distance vector corresponding to the installation angle and the lever arm of the mobile device relative to the vehicle is a problem to be solved.

[0078] To solve this problem, the application further provides a method for initializing the initial installation angle of a filter. Based on the method shown in the foregoing Figure 2 Before the steps S2031 to S2033, the initial installation angle of at least two filters on the mobile device can also be initialized by the following method.

[0079] The initial installation angles of the at least two filters are set according to equal angle intervals. The filters are initialized when GNSS observation is good. The initial installation angles of the at least two filters are determined according to an installation angle interval when the mobile device is fixed on the carrier and the number of filters of the mobile device. Generally, the installation angle interval is 360 degrees, i.e., covers the value range of all possible installation angles of the mobile device installed on the carrier. The distance between the initial installation angles of each filter is determined by dividing the installation angle interval by the number of filters. And the filters corresponding to the initial installation angles are initialized according to the initial installation angles.

[0080] For example, in a filter model including 12 filters, the difference between the values of the initial installation angles of every two adjacent filters can be 30 degrees, i.e., an initial installation angle value is set every 30 degrees, and the values are respectively assigned to different filters. In this way, the initial installation angle of at least one filter in the 12 filters can be closer to the actual installation angle value, so as to converge to the actual installation angle faster, thereby improving the efficiency of determining the target filter.

[0081] The method provided by the embodiments of the present application initializes the initial installation angles of a plurality of filters in a filter model according to equal angle intervals, determines a target filter with higher accuracy according to the innovations generated by the plurality of filters after initialization, and obtains the installation angle initial value, the lever arm initial value, the installation angle initial covariance, and the lever arm initial covariance of the state of the mobile device fixed on the carrier from the estimation result of the target filter. Setting the initial installation angles of the plurality of filters according to equal angle intervals can make the initial installation angle of at least one filter in the plurality of filters closer to the actual installation angle value, so as to converge to the actual installation angle faster, thereby improving the efficiency of determining the target filter. Further improving the efficiency of determining the installation angle initial value and the lever arm initial value according to the target filter improves the positioning efficiency on the basis of improving the positioning accuracy.

[0082] As described above, in the actual scenario that the user uses the mobile device for positioning when driving, the user may fix the mobile device on the carrier (i.e., the mobile device is in the state of being fixed on the carrier) or may hold up or move the mobile device during positioning (i.e., the mobile device is in the state of not being fixed on the carrier), so that the state of the mobile device is switched between the above two states. In the scenario that the mobile device is switched, if the filter model corresponding to the state of the mobile device cannot be used in time, the positioning error will be large.

[0083] To solve the problem, the application provides another positioning method. The following describes the positioning method based on two implementation manners: one is that a filter model with vehicle constraints and a filter model without vehicle constraints are pre-configured on an execution subject, or one filter model is pre-configured, and in the process of executing the method of the application, parameters of the model are adjusted according to the state of the mobile device to generate a model matching the state of the mobile device.

[0084] Implementation manner 1: The filter model with vehicle constraints and the filter model without vehicle constraints are pre-configured.

[0085] Data of sensor output deployed on the mobile device is acquired, and the state of the mobile device is determined according to IMU data in the data. When it is detected that the state of the mobile device changes, the filter model processing the data of the sensor output is switched. For example, the model can be switched by switching an interface calling the filter model, and the model is switched between calling the filter model with vehicle constraints and calling the filter model without vehicle constraints. Alternatively, the data of the sensor output can be switched to be input into another filter model for processing by changing an input position of the data of the sensor output.

[0086] For example, when the state of the mobile device changes from being fixed on the vehicle to not being fixed on the vehicle, the data of the sensor output deployed on the mobile device is input into the filter model without vehicle constraints for filter calculation to obtain the positioning data of the mobile device; when the state of the mobile device changes from not being fixed on the vehicle to being fixed on the vehicle, the data of the sensor output deployed on the mobile device is input into the filter model with vehicle constraints for filter calculation to obtain the positioning data of the mobile device.

[0087] Implementation manner 2: One dynamic filter model that can be converted into the filter model with vehicle constraints or the filter model without vehicle constraints is pre-configured.

[0088] Data of sensor output deployed on the mobile device is acquired, and the state of the mobile device is determined according to IMU data in the data. When it is detected that the state of the mobile device changes, the model parameters of the dynamic filter model are controlled to change, so that the dynamic filter model is converted into a filter model corresponding to the state of the mobile device. The data of the sensor output is processed by the filter model converted from the dynamic filter model to obtain the positioning data of the mobile device.

[0089] In a possible implementation, the dynamic model can include a first state vector, a vehicle constraint vector, a first covariance matrix, and a vehicle constraint covariance matrix, where the vehicle constraint vector includes an installation angle state vector and a lever arm state vector, and the vehicle constraint covariance matrix includes an installation angle initial covariance and a lever arm initial covariance. The cloud server can realize the transition between the filtering model with vehicle constraints and the filtering model without vehicle constraints by controlling the values of the vehicle constraint vector and the vehicle constraint covariance matrix.

[0090] In the following, the specific content of the implementation mode 2 will be described in detail taking the state switching scenario of the mobile device as an example.

[0091] Scenario 1: The mobile device switches from the state of being fixed on the carrier to the state of not being fixed on the carrier.

[0092] Figure 3 Another flowchart of a positioning method provided by the embodiment of the application is shown in FIG. 6. As shown in the figure, the method can further include the following steps: Figure 3

[0093] S301: Obtain a filtering model with vehicle constraints corresponding to the state of the mobile device being fixed on the carrier.

[0094] Obtain the mobile device attitude state vector, the mobile device velocity state vector, the mobile device position state vector, the gyro zero bias state vector, the accelerometer zero bias state vector, the installation angle state vector, and the lever arm state vector through the IMU data and the GNSS data during the state of the mobile device being fixed on the carrier, and calculate the respective covariances corresponding to the 21-dimensional state vector to generate the first covariance matrix and the vehicle constraint covariance matrix. Alternatively, the 21-dimensional state vector calculated by the mobile device can also be directly obtained, and the respective covariances corresponding to the 21-dimensional state vector are calculated to generate the first covariance matrix and the vehicle constraint covariance matrix. Alternatively, the 21-dimensional state vector calculated by the mobile device, the first covariance matrix, and the vehicle constraint covariance matrix can be directly obtained.

[0095] Determine the filtering model with vehicle constraints according to the 21-dimensional state vector, the first covariance matrix, and the vehicle constraint covariance matrix.

[0096] S302: Modify the values of the vehicle constraint vector and the vehicle constraint covariance matrix in the filtering model with vehicle constraints to the first values, so as to convert the filtering model with vehicle constraints into a filtering model without vehicle constraints.

[0097] The installation angle state vector is related to the installation angle initial value, and the lever arm state vector is related to the lever arm initial value.

[0098] ​Since the mobile device is not fixed on the carrier after switching to the state of not being fixed on the carrier, there is no installation angle and lever arm. Therefore, the filter model without the carrier constraint does not need to rely on the installation angle state vector and the lever arm state vector, and the covariance corresponding to the two, to perform filter calculation on the IMU data and the GNSS data. Therefore, the value of the carrier constraint vector and the carrier constraint covariance matrix in the filter model with the carrier constraint needs to be modified to the first value, so that the carrier constraint vector and the carrier constraint covariance matrix do not participate in the filter calculation when the filter model performs filter calculation. At this time, only the first state vector and the first covariance matrix in the model parameters of the filter model participate in the filter calculation, that is, the filter model is converted from the filter model with the carrier constraint to the filter model without the carrier constraint.

[0099] The first value is used to represent that the model parameters where the first value is located are not called to participate in calculation in the filter calculation. The first value may be a 0 value, or may be a preset specific value, for example, may be represented by a letter, etc. When the value of the model parameter is set to the first value, the model parameters do not participate in the filter calculation when the filter model is used to perform filter calculation. The first value may be set according to actual needs, which is not limited in the present application.

[0100] Scenario 2: The mobile device switches from the state of not being fixed on the carrier to the state of being fixed on the carrier.

[0101] Figure 4 Another flowchart of a positioning method provided by an embodiment of the present application is shown in FIG. 6. As shown in the figure, the method may include the following steps: Figure 4

[0102] S401, obtaining a filter model without a carrier constraint corresponding to a state of a mobile device not being fixed on a carrier, and obtaining an installation angle initial value, a lever arm initial value, an installation angle initial covariance, and a lever arm initial covariance of a state of the mobile device being fixed on the carrier.

[0103] The filter model without the carrier constraint is the dynamic filter model mentioned above, and the model parameters of the dynamic filter model include not only the first state vector and the first covariance matrix in the filter model without the carrier constraint, but also the carrier constraint state vector and the carrier constraint covariance matrix. At this time, the values of the carrier constraint state vector and the carrier constraint covariance matrix in the dynamic filter model are the first values mentioned in step S302.

[0104] ​The IMU data and the GNSS data during the state of the mobile device not being fixed on the carrier are used to obtain a mobile device attitude state vector, a mobile device velocity state vector, a mobile device position state vector, a gyro zero bias state vector, and a scale factor zero bias state vector, and to calculate the respective covariance corresponding to the 15-dimensional state vector to determine the values in the first covariance matrix. Alternatively, the 15-dimensional state vector calculated by the mobile device can be directly obtained, and the respective covariance corresponding to the 15-dimensional state vector is calculated to determine the values in the first covariance matrix. Alternatively, the 15-dimensional state vector calculated by the mobile device and the first covariance matrix can be directly obtained. The 15-dimensional state vector and the first covariance matrix are used to construct a filter model without carrier constraints.

[0105] S402, the installation angle initial value, the lever arm initial value, the installation angle initial covariance, and the lever arm initial covariance of the state of the mobile device being fixed on the carrier are used to modify the values of the carrier constraint vector and the carrier constraint covariance matrix in the filter model without carrier constraints to convert the filter model without carrier constraints into a filter model with carrier constraints.

[0106] The installation angle initial value and the lever arm initial value of the state of the mobile device being fixed on the carrier are used to determine the values of the carrier constraint vector, and the values are used to replace the first values originally used in the carrier constraint vector. The installation angle initial covariance and the lever arm initial covariance of the state of the mobile device being fixed on the carrier are used to determine the values of the carrier constraint covariance matrix, and the values are used to replace the first values originally used in the carrier constraint covariance matrix, so as to convert the filter model without carrier constraints into the filter model with carrier constraints.

[0107] The method provided in the embodiments of the present application can obtain the state of the mobile device being fixed on the carrier or not, dynamically adjust the values of the model parameters of the dynamic model according to the different states, and adaptively convert the dynamic model into a corresponding filter model according to the state of the mobile device. When the state of the mobile device is not fixed on the carrier, the GNSS data and the IMU data are filtered and calculated by using the filter model without carrier constraints, so that incorrect constraints of the carrier on the attitude of the mobile device can be avoided, and the probability of errors in the positioning data can be reduced. When the state of the mobile device is fixed on the carrier, the GNSS data and the IMU data are filtered and calculated by using the filter model with carrier constraints, so that the constraints of the carrier on the position calculation of the mobile device are added, and the accuracy of the positioning data obtained by the filter model can be improved. Therefore, the accuracy of the filter calculation of the IMU data and the GNSS data by the mobile device when the state of the mobile device on the carrier changes is improved, and the accuracy and efficiency of the positioning are improved.

[0108] Figure 5A structural schematic diagram of a positioning device is provided for an embodiment of the present application. As shown in the figure, the device can include an acquisition module 11, a processing module 12, a first control module 13, and a second control module 14. Figure 5

[0109] The acquisition module 11 is configured to acquire sensor output data of a mobile device, which includes GNSS data output by a GNSS chip and IMU data output by an inertial sensor.

[0110] The processing module 12 is configured to determine a state of the mobile device based on the IMU data, trigger the first control module if the state is that the mobile device is not fixed on a carrier, and trigger the second control module if the state is that the mobile device is fixed on the carrier.

[0111] The first control module 13 is configured to input the sensor output data into a filter model without carrier constraints for filter calculation to obtain positioning data of the mobile device.

[0112] The second control module 14 is configured to input the sensor output data into a filter model with carrier constraints for filter calculation to obtain the positioning data of the mobile device.

[0113] In a possible implementation, model parameters of the filter model without carrier constraints include a first state vector and a first covariance matrix, the first state vector including a mobile device attitude state vector, a mobile device velocity state vector, a mobile device position state vector, a gyro zero bias state vector, and an accelerometer zero bias state vector. Model parameters of the filter model with carrier constraints include the first state vector, a carrier constraint vector, the first covariance matrix, and a carrier constraint covariance matrix, wherein the carrier constraint vector includes an installation angle state vector and a lever arm state vector, and the carrier constraint covariance matrix includes an installation angle initial covariance and a lever arm initial covariance.

[0114] In the implementation, optionally, if the state is that the mobile device is fixed on the carrier, before the second control module 14 inputs the sensor output data into the filter model with carrier constraints for filter calculation, the processing module 12 is further configured to cache the sensor output data until a cache time length reaches a preset time threshold. Based on GNSS data in the cached data, it is determined whether a 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 an installation angle initial value, a lever arm initial value, an installation angle initial covariance, and a lever arm initial covariance of the mobile device, an angle value of an initial installation angle of the filter being equal to a set value and angle values of initial installation angles of any two filters being different.

[0115] ​Optionally, the processing module 12 is specifically configured to input the buffered data into at least two filters to obtain innovation of each filter. The target filter is determined from the filters whose innovation is less than or equal to a preset innovation threshold. The installation angle initial value, the lever arm initial value, the installation angle initial covariance and the lever arm initial covariance of the mobile device are determined according to the installation angle information estimated by the target filter, the lever arm information and the covariance matrix.

[0116] In a possible implementation, the processing module 12 is specifically configured to select the filter with the minimum innovation as the target filter from the filters whose innovation is less than or equal to the preset innovation threshold.

[0117] In any of the above implementation, the first control module 13 is further configured to, when the state is switched from the mobile device being fixed on the carrier to the mobile device not being fixed on the carrier, input the data output by the sensor into the filter model without the carrier constraint to perform filter calculation to obtain the positioning data of the mobile device. When the state is switched from the mobile device not being fixed on the carrier to the mobile device being fixed on the carrier, input the data output by the sensor into the filter model with the carrier constraint to perform filter calculation to obtain the positioning data of the mobile device.

[0118] In the state of the mobile device not being fixed on the carrier or the state being switched from the mobile device being fixed on the carrier to the mobile device not being fixed on the carrier, the processing module 12 is further configured to modify the carrier constraint vector and the carrier constraint covariance matrix value in the filter model with the carrier constraint to a first value to convert the filter model with the carrier constraint into the filter model without the carrier constraint.

[0119] The positioning apparatus provided by the embodiments of the present application can execute the positioning method in the method embodiments, and has similar implementation principles and technical effects, which will not be described here.

[0120] Figure 6 A structural schematic diagram of an electronic device is provided by the embodiments of the present application. The electronic device can be used to execute the positioning method as described above, for example. As shown in the figure, the electronic device 600 can include at least one processor 601 and a memory 602. In a possible implementation, the electronic device 600 can further include a communication interface 603. Figure 6

[0121] The memory 602 is used to store programs. Specifically, the programs can include program codes, and the program codes include computer operation instructions.

[0122] The memory 602 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory. ​

[0123] The processor 601 is configured to execute the computer-executable instructions stored in the memory 602 to implement the methods described in the foregoing method embodiments. The processor 601 can be a CPU, or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0124] The processor 601 can communicate with external devices through the communication interface 603. The external devices can be the mobile device described above. In a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are implemented independently, the communication interface 603, the memory 602 and the processor 601 can be connected through a bus and communicate with each other. The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0125] Optionally, in a specific implementation, if the communication interface 603, the memory 602 and the processor 601 are integrated on a chip, the communication interface 603, the memory 602 and the processor 601 can communicate through an internal interface.

[0126] The present application also provides a computer readable storage medium, which can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes. Specifically, the computer readable storage medium stores program instructions. The program instructions are used for the methods in the above embodiments.

[0127] The present application also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of an electronic device can read the execution instructions from the readable storage medium. The at least one processor executes the execution instructions to make the electronic device implement the positioning method provided by the various embodiments.

[0128] The term "plurality" in the present document refers to two or more. The term "and / or" in the present document is merely used to describe associated objects, and indicates that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present document generally indicates that the associated objects before and after are in an "or" relationship; in the formula, the character " / " indicates that the associated objects before and after are in a "division" relationship. In addition, it should be understood that in the description of the present application, the words "first", "second", etc. are only used for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor indicating or implying sequence.

[0129] It can be understood that various numerical numbers involved in the embodiments of the present application are only for the convenience of differentiation, and are not used to limit the scope of the embodiments of the present application.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A positioning method, characterized by, The method comprises: obtaining sensor output data of 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 to perform 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 to perform filtering calculation to obtain positioning data of the mobile device.

2. The method of claim 1, wherein, model parameters of the filter model without carrier constraints comprise a first state vector and a first covariance matrix, the first state vector comprising 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; 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 to perform filtering calculation, the method further comprises: buffering the sensor output data until a buffering time 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 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 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 according to any one of claims 1 to 5, characterized in that, the method further comprises: when the state is switched from the mobile device being fixed on the vehicle to the mobile device not being fixed on the vehicle, inputting the data output by the sensor into the filter model without the vehicle constraint for filter calculation to obtain the positioning data of the mobile device; when the state is switched from the mobile device not being fixed on the vehicle to the mobile device being fixed on the vehicle, inputting the data output by the sensor into the filter model with the vehicle constraint for filter calculation to obtain the positioning data of the mobile device.

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

8. A positioning device, characterized in that The apparatus 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, trigger a first control module if the state is the mobile device not being fixed on a vehicle, and 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 the vehicle constraint for filter calculation to obtain the 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 the vehicle constraint for filter calculation to obtain the positioning data of the mobile device.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any one of claims 1-7.

10. A computer program product, characterised in that, The computer program or instructions are executed by the processor to implement the method in any one of claims 1-7.

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