Method for determining satellite signal characteristics, positioning method, and electronic device

Abstract

Embodiments of the present disclosure disclose a method for determining satellite signal characteristics, a positioning method, and an electronic device. The method includes: obtaining GNSS observation data and position correction data received by a target object to be positioned within a target area; the position correction data includes sensor data; determining corrected position data of the target object to be positioned according to the GNSS observation data, the position correction data, and map data; determining a measurement error of the GNSS observation data according to the position data and the GNSS observation data; and determining satellite signal characteristics corresponding to the GNSS observation data based on the measurement error. This technical solution can analyze satellite signal characteristics within a target area, and these satellite signal characteristics can assist the target object to be positioned entering the target area to obtain a more accurate real-time positioning position, and can improve the accuracy of satellite positioning.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular, to a method for determining satellite signal characteristics, a positioning method, and an electronic device. Background Art

[0002] With the progress of Internet technology, the application of positioning technology has become more and more extensive, and people's travel and other aspects of life have become more and more dependent on positioning technology. For example, in a navigation scenario, it is necessary to continuously obtain the position information of a device so as to continuously provide navigation services for the device.

[0003] However, in areas with many high-rise buildings in cities, mountainous areas, canyons, etc., environmental factors such as buildings or mountains in these areas will block, reflect, or scatter GNSS satellite signals, which will cause the GNSS receiver chip carried on the device entering these areas to be unable to correctly identify problems such as multipath caused by environmental factors, and further cause problems such as jumps or drifts in the GNSS positioning position, resulting in a decrease in positioning accuracy. Therefore, improving the positioning accuracy of devices in these areas is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] Embodiments of the present disclosure provide a method for determining satellite signal characteristics, a positioning method, and an electronic device.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for determining satellite signal characteristics, which includes:

[0006] Obtain GNSS observation data and position correction data received by a target object to be positioned in a target area; the position correction data includes sensor data;

[0007] Determine the corrected position data of the object to be positioned according to the GNSS observation data, the position correction data, and map data;

[0008] Determine the measurement error of the GNSS observation data according to the position data and the GNSS observation data;

[0009] Determine the satellite signal characteristics corresponding to the GNSS observation data based on the measurement error.

[0010] Further, the sensor data includes the GNSS positioning position of the object to be positioned and other sensing data; determining the corrected position data of the object to be positioned according to the GNSS observation data, the position correction data, and map data includes:

[0011] Correct the GNSS positioning position based on the map data and / or the other sensing data to obtain the corrected position data.

[0012] Further, determining satellite signal characteristics corresponding to the GNSS observation data based on the measurement error includes:

[0013] Rasterize the target area to form multiple local areas;

[0014] For the located object whose positioning data is within the local area, perform statistical analysis on the measurement error corresponding to the observed GNSS observation data to obtain the satellite signal characteristics of each satellite within the local area.

[0015] Further, for the located object whose positioning data is within the local area, performing statistical analysis on the measurement error corresponding to the observed GNSS observation data to obtain the satellite signal characteristics of each satellite within the local area includes:

[0016] Use the measurement error and the ephemeris of the satellite to train an algorithm model so that the trained algorithm model can identify the satellite signal characteristics of each satellite within the local area.

[0017] Further, the algorithm model includes at least one of the following:

[0018] A satellite selection model, which is a first algorithm model for screening out satellites that can be selected during the current positioning process from multiple candidate satellites;

[0019] A weight determination model for determining the weights of multiple candidate satellites during the current positioning process.

[0020] In a second aspect, an embodiment of the present disclosure provides a positioning method, which includes:

[0021] Obtain GNSS observation data observed on the located object and satellite signal characteristics corresponding to the GNSS observation data; wherein, the satellite signal characteristics are obtained based on the method described in the first aspect;

[0022] Obtain the target positioning position of the located object based on the GNSS observation data and the satellite signal characteristics.

[0023] Further, obtaining the target positioning position of the located object based on the GNSS observation data and the satellite signal characteristics includes:

[0024] Obtain the GNSS positioning position output by the GNSS receiver chip of the located object;

[0025] Use a first preset positioning algorithm to correct the GNSS positioning position based on the satellite signal characteristics to obtain the target positioning position.

[0026] Further, obtaining the target positioning location of the object to be positioned based on the GNSS observation data and the satellite signal characteristics includes:

[0027] Obtaining the GNSS positioning location of the object to be positioned based on the GNSS observation data and the satellite signal characteristics by using a second preset positioning algorithm.

[0028] In a third aspect, an embodiment of the present disclosure provides a location-based service providing method, wherein the method uses the positioning method described in the second aspect to position the location of the object to be served, and the location-based service includes one or more of navigation, map rendering, and route planning.

[0029] In a fourth aspect, an embodiment of the present disclosure provides a determination of satellite signal characteristics, including:

[0030] A first acquisition module, configured to acquire GNSS observation data and position correction data received by an object to be positioned in a target area; the position correction data includes sensor data;

[0031] A first determination module, configured to determine the corrected position data of the object to be positioned according to the GNSS observation data, the position correction data, and map data;

[0032] A reverse inference module, configured to determine the measurement error of the GNSS observation data according to the position data and the GNSS observation data;

[0033] A second determination module, configured to determine the satellite signal characteristics corresponding to the GNSS observation data based on the measurement error.

[0034] The above functions can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

[0035] In a possible design, the structure of the above device includes a memory and a processor. The memory is used to store one or more computer instructions supporting the above device to execute the corresponding method, and the processor is configured to execute the computer instructions stored in the memory. The above device may further include a communication interface for communicating between the above device and other devices or communication networks.

[0036] In a fifth aspect, an embodiment of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method described in any of the above aspects.

[0037] Sixth aspect, embodiments of the present disclosure provide a computer-readable storage medium for storing computer instructions used by any of the above devices. When the computer instructions are executed by a processor, they are used to implement the method described in any of the above aspects.

[0038] Seventh aspect, embodiments of the present disclosure provide a computer program product that includes computer instructions. When the computer instructions are executed by a processor, they are used to implement the method described in any of the above aspects.

[0039] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

[0040] In the embodiments of the present disclosure, for a target area with a relatively harsh environment, GNSS observation data and position correction data of a positioned object entering the target area can be obtained. The position correction data may include, but is not limited to, sensor data and / or map data of the target area; the GNSS observation data and the position correction data are used to obtain relatively accurate position data of the positioned object in the target area, and then the measurement error of the GNSS observation data is deduced from the relatively accurate position data, and the satellite signal characteristics corresponding to the GNSS observation data are determined using the measurement error. In this way, the satellite signal characteristics in the target area can be analyzed. The satellite signal characteristics can reflect in real time the influence of environmental changes, weather changes, and satellite changes in the target area on the satellite signal, and thus can assist the positioned object entering the target area to obtain a more accurate real-time positioning position, and can improve the accuracy of satellite positioning.

[0041] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In combination with the drawings, through the following detailed description of non-limiting embodiments, other features, objectives, and advantages of the present disclosure will become more obvious. In the drawings:

[0043] Figure 1 Show a flowchart of a method for determining satellite signal characteristics according to an embodiment of the present disclosure;

[0044] Figure 2 Show a schematic diagram of the application process of satellite signal determination according to an embodiment of the present disclosure;

[0045] Figure 3 Show a flowchart of a positioning method according to an embodiment of the present disclosure;

[0046] Figure 4 Show a schematic diagram of a vehicle navigation application scenario according to an embodiment of the present disclosure;

[0047] Figure 5It is a structural diagram of an electronic device suitable for implementing a satellite signal characteristic determination method, a positioning method and / or a location-based service provision method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0049] In the present disclosure, it should be understood that terms such as "including" or "having" are intended to indicate the presence of features, numbers, steps, behaviors, components, parts, or combinations thereof disclosed in the present specification, and do not exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof exist or are added.

[0050] It should also be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] The inventors of this disclosure have discovered that the impact of satellite signals in areas with high-rise buildings, mountainous areas, and canyons is correlated with the surrounding environment of the positioning device, and this correlation remains largely stable over time. Therefore, this disclosure proposes a method for determining satellite signal characteristics, which is used to mine satellite signal characteristics within a local area and then use these satellite signal characteristics to assist positioning devices within that area in performing position determination.

[0052] The details of the embodiments of the present disclosure are described in detail below through specific examples.

[0053] Figure 1 FIG. 1 is a flow chart showing a method for determining satellite signal characteristics according to an embodiment of the present disclosure. Figure 1 As shown, the method for determining the satellite signal characteristics includes the following steps:

[0054] In step S101, GNSS observation data and position correction data received by the positioned object in the target area are obtained; the position correction data includes sensor data;

[0055] In step S102, the corrected position data of the located object is determined based on the GNSS observation data, the position correction data and the map data;

[0056] In step S103, a measurement error of the GNSS observation data is determined based on the position data and the GNSS observation data;

[0057] In step S104, based on the measurement error and the GNSS observation data, determine the satellite signal characteristics corresponding to the GNSS observation data.

[0058] In this embodiment, the method can be executed on a server. The server can collect GNSS observation data and position correction data of one or more objects to be located entering the target area; the position correction data can include, but is not limited to, map data and / or sensor data. The object to be located can be an object capable of obtaining its GNSS observation data and sensor data within the target area, such as a mobile phone, ipad, computer, smart watch, vehicle, robot, etc. of a crowdsourcing user. In some embodiments, the target area can be a pre-determined area where occlusion may exist; in other embodiments, the target area can also be any area.

[0059] The object to be located can receive satellite signals from multiple satellites. It can be understood that the satellite signals received by the object to be located at different times can come from different satellites, and the number of satellites corresponding to the satellite signals that can be received can also be different. The GNSS receiver provided on the object to be located can analyze GNSS observation data from the satellite signals of multiple satellites received at the current moment, and then calculate the positioning position of the object to be located at the current moment based on the GNSS observation data.

[0060] GNSS refers to the Global Navigation Satellite System, that is, a satellite system for autonomous geospatial positioning covering the globe; GNSS includes GPS (Global Positioning System), GLONASS (Global Navigation Satellite System), BDS (BeiDou Navigation Satellite System), Galileo (Galileo positioning system). GNSS observation data refers to the satellite observables obtained by the receiver, which can include, but is not limited to, pseudorange, pseudorange rate, carrier wave, signal-to-noise ratio, etc. After the receiver receives the satellite signal, it can directly or indirectly obtain GNSS observation data from the satellite signal.

[0061] Sensor data can be data collected by sensors provided on the object to be located. The sensors can include, but are not limited to, GNSS receivers, accelerometers, gyroscopes, magnetometers, vision sensors, etc. Sensor data can include, but is not limited to, the GNSS positioning position output by the GNSS receiver, the acceleration, angular velocity, azimuth of the object to be located, and surrounding image data, etc. Map data can be map data of the area passed by the object to be located, and the map data can be pre-stored in the server.

[0062] In the embodiments of the present disclosure, when the object to be located is currently in an area where satellite signals are greatly affected by the environment, due to the influence of the environment on GNSS observation data, there are certain errors in the GNSS observation data. As a result, the GNSS positioning position calculated in real time by the GNSS receiver on the object to be located based on this GNSS observation data is not accurate, or rather, the accuracy of this GNSS positioning position is relatively low. Therefore, in the embodiments of the present disclosure, map data and / or other sensing data such as the acceleration, azimuth, and angular velocity of the object to be located can also be used to calibrate this inaccurate GNSS positioning position, thereby obtaining position data with higher accuracy than the GNSS positioning position. In some embodiments, the above-mentioned position data with higher accuracy can be obtained by using the Kalman smoothing algorithm or the map matching algorithm, etc. In some embodiments, this position data can be the true trajectory data of the object to be located in the target area, which can be called the true value trajectory, and the accuracy of this true value trajectory is higher than the trajectory data obtained from the GNSS positioning position calculated in real time using GNSS observation data.

[0063] Therefore, the measurement error of the GNSS observation data can be deduced inversely based on the above-mentioned position data with higher accuracy. In some embodiments, the measurement error of the GNSS observation data may include, but is not limited to, pseudorange error, pseudorange rate error, and carrier error. In other embodiments, the measurement error of the GNSS observation data may also include signal-to-noise ratio error. However, since the signal-to-noise ratio error is related to the hardware, the signal-to-noise ratio of each hardware device, that is, the object to be located, is basically constant. Therefore, the signal-to-noise ratio can be directly used in the measurement error of the GNSS observation data.

[0064] After obtaining the measurement error of the GNSS observation data, the corresponding satellite signal characteristics can be determined according to this measurement error. For example, there is a viaduct in the target area. After the object to be located enters the vicinity of this viaduct, the measurement error of the GNSS observation data of the satellites blocked by this viaduct is relatively large. Therefore, after the object to be located enters the vicinity of the viaduct in this target area, it can be determined that the satellite signals received from the above azimuth have characteristics such as being blocked, reflected, or multipath.

[0065] In some embodiments, after the server determines the satellite signal characteristics based on the above method, it can broadcast the satellite signal characteristics to the objects to be located entering the above target area (it should be noted that the objects to be located here can include the objects to be located used by the server to collect sensor data in the above text and other positioning objects). After receiving the above satellite signal characteristics broadcast by the server, the above objects to be located can jointly determine the real-time positioning position according to the above satellite signal characteristics and GNSS observation data, so as to improve the accuracy of the positioning position.

[0066] In an embodiment of the present disclosure, for a target area with a relatively harsh environment, GNSS observation data and position correction data of a positioned object entering the target area can be obtained. The position correction data may include, but is not limited to, sensor data. By using the GNSS observation data, the position correction data, and the map data pre-stored on the server, position data with higher accuracy of the positioned object in the target area can be obtained (that is, the position data is the relatively accurate position data of the positioning object in the target area). Furthermore, the measurement error of the GNSS observation data can be deduced from the position data with higher accuracy, and the satellite signal characteristics corresponding to the GNSS observation data can be determined by using the measurement error. In this way, the satellite signal characteristics in the target area can be analyzed. The satellite signal characteristics can reflect in real time the impacts of environmental changes, weather changes, satellite changes, etc. in the target area on the satellite signal. Furthermore, it can assist the positioned object entering the target area to obtain a more accurate real-time positioning position and improve the accuracy of satellite positioning.

[0067] In an alternative implementation manner of this embodiment, the sensor data includes the GNSS positioning position of the positioned object and other sensing data. Step S102, that is, the step of determining the corrected position data of the positioned object according to the GNSS observation data, the position correction data, and the map data, further includes the following steps:

[0068] Based on the map data and the other sensing data, the GNSS positioning position is corrected to obtain the corrected position data.

[0069] In this alternative implementation manner, the server can collect from the positioned object the GNSS positioning position and the GNSS observation data output by the GNSS receiver provided on the positioned object after the positioned object enters the target area (the GNSS receiver calculates and outputs the real-time positioning position based on the observation data), and can also obtain other sensing data collected by other sensors from the positioned object, such as the angular velocity, acceleration, and azimuth of the positioned object. The server can also obtain the map data of the target area from an electronic map service system or a storage medium.

[0070] Based on the above map data and / or other sensing data, the server can correct the GNSS positioning position output by the GNSS receiver of the positioned object. The corrected positioning position can form the trajectory data of the positioned object in the target area, and the trajectory data can be used as the calibrated position data.

[0071] It should be noted that, in some embodiments, the above real-time positioning position can be corrected by using a map matching algorithm and map data. For example, after the GNSS positioning position drifts at a certain moment or certain moments, the drifted GNSS positioning position can be corrected based on the map data. In other embodiments, the above real-time positioning position can be corrected by using a Kalman smoothing method and other sensing data. The specific correction method can be selected based on actual needs and is not limited herein.

[0072] In an alternative implementation manner of this embodiment, step S104, that is, the step of determining the satellite signal characteristics corresponding to the GNSS observation data based on the measurement error, further includes the following steps:

[0073] Rasterize the target area to form a plurality of local areas;

[0074] For the positioned object whose positioning data is within the local area, perform statistical analysis on the measurement error corresponding to the GNSS observation data observed for the positioned object whose positioning data is within the local area, so as to obtain the satellite signal characteristics of each satellite within the local area.

[0075] In this alternative implementation manner, in the same area, the environmental influence on the satellite signal is basically stable within a period of time. Therefore, by statistically analyzing the measurement error of the GNSS observation data affected by the environment within this area, the above satellite signal characteristics affected by the environment can be determined, and such satellite signal characteristics will remain unchanged within this area for a period of time. After determining the above satellite signal characteristics, the positioning device entering this area can correct the GNSS positioning position based on this satellite signal characteristic, so as to eliminate the influence of the environment and obtain a more accurate positioning position.

[0076] In the real environment, the environmental areas that have a greater impact on the satellite signal may only be local areas. For example, when there are viaduct areas, canyon areas, etc., from the relative position relationship between the satellite and the positioned object, the satellite signal is exactly blocked by the viaduct or canyon. If the target area is relatively large, such as when there are both blocked areas and open areas, by statistically analyzing the measurement error corresponding to the GNSS observation data and then statistically analyzing the measurement error to obtain the satellite signal characteristics, it may lead to inaccurate satellite signal characteristics. And if the target area is large and a large amount of data needs to be statistically analyzed, the calculation amount will also be large. Therefore, while causing a greater pressure on the server, it may not be able to output relatively accurate satellite signal characteristics in a timely manner, and ultimately will lead to a reduction in the position accuracy of the positioned object.

[0077] Therefore, in the embodiments of the present disclosure, the target area is rasterized to obtain a plurality of local areas. Each local area corresponds to a grid after rasterization. The rasterization can be performed in ways such as rectangular division or roadside division of roads. The specific division method can be set according to actual needs and is not specifically limited herein.

[0078] For each of the divided local areas, the measurement errors corresponding to the GNSS observation data observed when the object to be located is within the local area can be statistically analyzed, so as to statistically obtain the characteristics of the satellite signals received within the local area from a large number of measurement errors through big data analysis methods. It should be noted that whether the positioning device is within the local area can be determined based on the more accurate position data obtained after correction mentioned above.

[0079] The characteristics of the satellite signals can reflect the influence of the environment on the satellite signals within the local area. Therefore, the server can broadcast the characteristics of the satellite signals to the objects to be located entering the local area, so as to assist the objects to be located in correcting the real-time GNSS positioning positions based on the characteristics of the satellite signals. It should be noted that the server can statistically analyze the characteristics of the satellite signals in each local area at regular intervals or in real time, and broadcast the statistically analyzed characteristics of the satellite signals to the objects to be located entering the local area.

[0080] In an alternative implementation manner of this embodiment, the step of statistically analyzing the measurement errors corresponding to the GNSS observation data obtained within the local area to obtain the characteristics of the satellite signals of each satellite within the local area further includes the following steps:

[0081] Using the measurement errors and the ephemeris of the satellite to train the algorithm model, so that the trained algorithm model can identify the characteristics of the satellite signals of each satellite within the local area.

[0082] In this alternative implementation, the statistical analysis of the measurement errors corresponding to the GNSS observation data obtained in a local area can be achieved by training an artificial intelligence algorithm model. The measurement errors corresponding to the GNSS observation data and the ephemeris of the satellites corresponding to these GNSS observation data can be input into a pre-set algorithm model, so that the algorithm model can learn the signal characteristics of the satellites from which the GNSS observation data is observed. It should be noted that the measurement error of the GNSS observation data is related to the position of the satellite at that time. Therefore, when training the algorithm model, in addition to inputting the measurement error, the ephemeris of the satellite also needs to be input, so as to be able to determine the azimuth of the satellite when the GNSS observation data is received. In this way, an algorithm model can be finally trained. This algorithm model can identify the signal characteristics of the satellite signals received from the satellite based on the azimuth of the satellite (which can be determined based on the ephemeris of the satellite), and based on this signal characteristic, the real-time positioning position of the GNSS of the positioning device in this local area can be corrected.

[0083] In an alternative implementation of this embodiment, the algorithm model includes at least one of the following:

[0084] A satellite selection model, which is a first algorithm model used to screen out the satellites that can be selected during the current positioning process from multiple candidate satellites;

[0085] A weight determination model, which is used to determine the weights of multiple candidate satellites during the current positioning process.

[0086] In this alternative implementation, the satellite selection model can be used to classify multiple candidate satellites. For example, it can screen out NLOS (Non Line of Sight) satellites from multiple candidate satellites. Since the satellite signals received from NLOS satellites are greatly affected by the environment, the GNSS positioning position calculated based on the GNSS observation data of this type of satellite has low accuracy. Therefore, through the satellite selection model, multiple candidate satellites can be classified into NLOS satellites and non-NLOS satellites, and when performing real-time positioning on the object to be located, the NLOS satellites can be excluded, and the target position of the object to be located can be calculated using the GNSS observation data of the non-NLOS satellites. In this way, the satellite positioning accuracy can be improved.

[0087] The weight determination model can be used to determine the weights of each candidate satellite. The weight determination model can assign weight values to multiple candidate satellites that can be observed within a local area. Satellites with good satellite signal quality can be assigned higher weight values, while satellites with poor satellite signal quality can be assigned lower weight values. Through the weight determination model, the object to be located can assign different weight values to the multiple candidate satellites it observes, and when calculating the real-time positioning position of the object to be located, increase the proportion of GNSS observation data corresponding to satellites with high weight values and reduce the proportion of GNSS data corresponding to satellites with low weight values. In this way, the satellite positioning accuracy can be improved.

[0088] In some embodiments, the satellite selection model and the weight determination model can be trained after considering the characteristics of the positioning algorithm used in the real-time positioning process, that is, training the satellite selection model and the weight determination model into models adapted to the positioning algorithm used in the real-time positioning; while in some embodiments, they can also be trained without considering the characteristics of the positioning algorithm used in the real-time positioning, and the satellites to be excluded and the weights of the satellites can be adjusted accordingly based on the characteristics of the positioning algorithm used during real-time positioning.

[0089] Figure 2 The figure shows a schematic application flow diagram determined according to the satellite signal characteristics in an embodiment of the present disclosure. As Figure 2 shown, the client can include multiple user devices, each user device is provided with a GNSS receiver, and other sensors can also be provided on each device, such as a magnetometer, an angular velocity meter, an accelerometer, and a vision sensor. The server side includes one or more servers, and the multiple user devices on the client side can communicate with the servers on the server side. The servers on the server side can collect the GNSS observation data of the user devices within the target area and the GNSS positioning positions calculated by the GNSS receivers based on the GNSS observation data from the user devices. The servers can also collect GNSS observation data and other sensor data from the user devices.

[0090] The server can also clean the collected GNSS positioning positions, GNSS observation data, and other sensor data to exclude abnormal devices and users. A ground truth trajectory post-processing service can be run on the server to obtain the ground truth trajectory of the user device within the target area based on the GNSS positioning positions, GNSS observation data, and other sensor data. The position data in this ground truth trajectory is more accurate than the GNSS positioning positions.

[0091] The true trajectory output by the true trajectory post-processing service can be used to reverse-infer the measurement errors of GNSS observation data. Furthermore, based on the reverse-inferred measurement errors, an error model for the local area can be trained, such as a satellite selection model or a weight determination model, etc. The model parameters of the error model can be broadcast by the server to the positioned object entering the local area. The positioned object can obtain the satellite signal characteristics of the satellites observed in the local area based on the received model parameters of the error model. Furthermore, real-time positioning can be performed based on the satellite signal characteristics and the GNSS observation data. It should be noted that the positioned object can also be a user equipment for the server to collect GNSS observation data and sensor data. Of course, the positioned object can also not be a user equipment for the server to collect GNSS observation data and sensor data.

[0092] Figure 3 The flowchart showing a positioning method according to an embodiment of the present disclosure is as follows. Figure 3 As shown, the positioning method includes the following steps:

[0093] In step S301, GNSS observation data observed on the positioned object and the satellite signal characteristics corresponding to the GNSS observation data are obtained; wherein, the satellite signal characteristics are obtained based on the above-mentioned method for determining satellite signal characteristics.

[0094] In step S302, the target positioning position of the positioned object is obtained based on the GNSS observation data and the satellite signal characteristics.

[0095] In this embodiment, the method can be executed on the positioned object entering the local area. The positioned object can be any positioning object entering the local area. For example, it can be a mobile phone, ipad, computer, smart watch, vehicle, robot, etc. of any user. The server can broadcast the satellite signal characteristics corresponding to each candidate satellite to the positioned object entering the local area. The positioned object can receive the satellite signal characteristics that can be observed in the local area from the server. The satellite signal characteristics can be used to characterize the characteristics of the satellite signals emitted by these satellites after being affected by the environment. The positioned object can calculate the target positioning position in real time based on the observed satellite signal characteristics and the corresponding GNSS observation data. It should be noted that the satellite signal characteristics received by the positioned object from the server can be the direct signal characteristics exhibited by each satellite, or can be model parameters, and based on the model parameters, the above-mentioned direct signal characteristics can be identified.

[0096] In some embodiments, the local area can be an area where the satellite signal is greatly affected by the environment, which may cause inaccurate GNSS positioning. Details of the local area can be referred to the description in the above-mentioned satellite signal characteristics and will not be elaborated here.

[0097] In some embodiments, the satellite signal features can be characterized by a trained algorithm model. For example, the server can obtain an algorithm model capable of identifying satellite signal features according to the above-mentioned method for determining satellite signal features, and broadcast the model parameters of the algorithm model to the located objects entering the local area. In some embodiments, the algorithm model can include, but is not limited to, a classification model and a weight determination model. After receiving the model parameters of the algorithm model, the located object can classify or determine the weights of the satellites observed in the local area based on the model parameters, and then calculate the target positioning position in the local area in real time according to the classification or weight determination results and the corresponding GNSS observation data. It should be noted that the server can obtain different types of satellite signal features of each satellite based on the above-mentioned method for determining satellite signal features, and the different types of satellite signal features can be broadcast to all located objects entering the local area, or selectively broadcast to the located objects entering the local area. For example, the satellite signal features that match the positioning algorithm type on the located object can be broadcast to the corresponding located object.

[0098] After receiving the satellite signal features, the located object can call a pre-set positioning algorithm to calculate the target positioning position based on the satellite signal features and the GNSS observation data. It should be noted that in the conventional GNSS positioning method, the GNSS receiver chip on the located object can automatically calculate the chip solution, that is, the GNSS positioning position, based on the observed GNSS observation data. However, considering that the satellite signals are greatly affected by the environment in the local area, the GNSS positioning position directly calculated using the GNSS observation data is not accurate. Therefore, the located object entering the local area can use the satellite signal features of the observed satellites as auxiliary information on the basis of the GNSS observation data to calculate a more accurate target positioning position.

[0099] For other relevant details in this embodiment, reference can be made to the above description of the method for determining satellite signal features, which will not be elaborated here.

[0100] In the embodiments of the present disclosure, the located object entering the local area can calculate the target positioning position based on the satellite signal features pre-determined by the server and the GNSS observation data. This is equivalent to using the satellite signal features as auxiliary information for position calculation on the basis of the GNSS observation data, making a certain correction to the GNSS positioning position and improving the accuracy of satellite positioning.

[0101] In an alternative implementation manner of this embodiment, step S302, that is, the step of obtaining the target positioning position of the located object based on the GNSS observation data and the satellite signal features, further includes the following steps:

[0102] Obtain the GNSS positioning location output by the GNSS receiver chip of the object to be located;

[0103] Use a first preset positioning algorithm to correct the GNSS positioning location based on the satellite signal characteristics to obtain the target positioning location.

[0104] This optional implementation manner can be realized at the software level. The server can send the satellite signal characteristics to the software level of the object to be located. The GNSS receiver on the object to be located uses the observed GNSS observation data to calculate the GNSS positioning location, and at the software level, the GNSS positioning location output by the GNSS receiver is corrected based on the satellite signal characteristics sent by the server by a pre-set first preset positioning algorithm, and finally the corrected target positioning location is obtained. The first preset positioning algorithm can adopt single-point positioning algorithms, filtering algorithms, and double-difference algorithms, etc. The single-point positioning algorithms can be, for example, the SPP algorithm, the filtering algorithms can be, for example, the Kalman filter, the loose combination and tight combination navigation and positioning algorithms, the PPP positioning algorithm, the particle filter algorithm, etc., and the double-difference algorithms can be, for example, the RTK algorithm, etc.

[0105] In this way, the GNSS positioning location output by the GNSS receiver can be corrected only by the first preset positioning algorithm at the software level, without making too many changes at the system level and / or the hardware level. Therefore, in this way, the application scalability and robustness of the embodiments of the present disclosure can be improved.

[0106] In an optional implementation manner of this embodiment, step S302, that is, the step of obtaining the target positioning location of the object to be located based on the GNSS observation data and the satellite signal characteristics, further includes the following steps:

[0107] Use a second preset positioning algorithm to obtain the GNSS positioning location of the object to be located based on the GNSS observation data and the satellite signal characteristics.

[0108] In this optional implementation, the server can send the satellite signal characteristics to the system level or the hardware level of the object to be located, and improve the positioning algorithm at the system level or the hardware level of the object to be located. Then, the improved positioning algorithm calculates the GNSS positioning position based on the GNSS observation data and the satellite signal characteristics. For example, in the GNSS receiver's solution algorithm, after using the satellite selection model sent by the server to filter out the GNSS observation data of the NLOS model, the GNSS positioning position can be calculated using the remaining GNSS observation data, and this GNSS positioning position is determined as the target positioning position of the object to be located; alternatively, the weight value of each observed satellite can be determined using the weight determination model sent by the server, and after weighting the GNSS observation value corresponding to the satellite using this weight value, the GNSS positioning position can be calculated.

[0109] According to a method for providing a location-based service according to an embodiment of the present disclosure, the method for providing a location-based service includes: using the above positioning method to locate the position of the object to be served, and the location-based service includes one or more of navigation, map rendering, and route planning.

[0110] In this embodiment, the method for providing a location-based service can be executed on a terminal, and the terminal is a mobile phone, an iPad, a computer, a smart watch, a vehicle, etc. In the embodiment of the present disclosure, for a target area where satellite signals are greatly affected by the environment, the server statistically analyzes the satellite signal characteristics of each satellite and sends the satellite signal characteristics to the object to be served entering the target area. Then, the object to be served provides location-based services such as navigation, map rendering, and route planning based on the satellite signal characteristics. By applying the above positioning method to the terminal used by the user, the effect of providing location-based services can be improved, and the user experience can be enhanced.

[0111] The object to be served can be a mobile phone, an iPad, a computer, a smart watch, a vehicle, a robot, etc. The positioning position of the object to be served can be obtained using the above positioning method, and the specific details can refer to the description of the positioning method above and will not be elaborated here.

[0112] Figure 4 Shows a schematic diagram of a vehicle navigation application scenario according to an embodiment of the present disclosure. As Figure 4As shown in the figure, the vehicle navigation scenario can be divided into an offline process and a real-time positioning process. In the offline process, in the viaduct area of City A, the server collects GNSS observation data and sensor data from the vehicle navigation devices of crowdsourcing users passing through the viaduct area. The GNSS observation data can be obtained by parsing the satellite signals received by the vehicle navigation device from Satellite A (illustrated as 1 satellite, and actually multiple satellites in practical applications). The sensor data can include the GNSS positioning position output by the GNSS receiver and other sensor data. The server can also obtain the map data of the viaduct area from the electronic map service system. After collecting the above data, the server can train a satellite selection model and a weight determination model based on the above data. And broadcast the model parameters of the above satellite selection model and weight determination model to any vehicle entering the viaduct area. In the real-time positioning process, after receiving the above model parameters, the navigation terminal on the any vehicle receives satellite signals from Satellite B (illustrated as 1, and actually multiple satellites in practical applications, and Satellite A and Satellite B can be the same or different), and parses the GNSS observation data. Furthermore, the GNSS observation data of the observed satellites are screened by using the above model parameters, or weight values are assigned to the observed satellites. When calculating the GNSS positioning position, the GNSS data can be processed based on the screening result or the weight determination result to obtain a more accurate target positioning position; and the vehicle is navigated based on the target positioning position.

[0113] According to an embodiment of the present disclosure, a device for determining satellite signal characteristics can be implemented as part or all of an electronic device through software, hardware, or a combination of both. The device for determining satellite signal characteristics includes:

[0114] A first acquisition module configured to acquire GNSS observation data and position correction data received by a positioning object in a target area; the position correction data includes sensor data;

[0115] A first determination module configured to determine the corrected position data of the positioning object according to the GNSS observation data, the position correction data, and map data;

[0116] A backpropagation module configured to determine the measurement error of the GNSS observation data according to the position data and the GNSS observation data;

[0117] A second determination module configured to determine the satellite signal characteristics corresponding to the GNSS observation data based on the measurement error.

[0118] In an alternative implementation of this embodiment, the sensor data includes the GNSS positioning position of the positioning object and other sensing data; the first determination module includes:

[0119] The correction submodule is configured to correct the GNSS positioning position based on the map data and / or the other sensor data to obtain the corrected position data.

[0120] In an optional implementation of this embodiment, the second determining module includes:

[0121] a rasterization submodule, configured to rasterize the target area to form a plurality of local areas;

[0122] The statistical submodule is configured to perform statistical analysis on the measurement errors corresponding to the GNSS observation data observed for the positioned object whose positioning data is located in the local area, so as to obtain satellite signal characteristics of each satellite in the local area.

[0123] In an optional implementation of this embodiment, the statistics submodule includes:

[0124] The identification submodule is configured to use the measurement error and the ephemeris of the satellite to train an algorithm model so that the trained algorithm model can identify the satellite signal characteristics of each satellite in the local area.

[0125] In an optional implementation of this embodiment, the algorithm model includes at least one of the following:

[0126] A satellite selection model is a first algorithm model used to select a satellite that can be selected in the current positioning process from multiple candidate satellites;

[0127] The weight determination model is used to determine the weights of multiple candidate satellites in the current positioning process.

[0128] The device for determining satellite signal characteristics in the embodiment of the present disclosure corresponds to the method for determining satellite signal characteristics described above. For specific details, please refer to the method for determining satellite signal characteristics described above, which will not be repeated here.

[0129] According to an embodiment of the present disclosure, a positioning device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. The positioning device includes:

[0130] Obtaining GNSS observation data observed on the positioned object and satellite signal characteristics corresponding to the GNSS observation data; wherein the satellite signal characteristics are obtained based on the method according to any one of claims 1 to 5;

[0131] A target positioning position of the positioned object is obtained based on the GNSS observation data and the satellite signal characteristics.

[0132] In an alternative implementation of this embodiment, obtaining the target positioning location of the object to be positioned based on the GNSS observation data and the satellite signal characteristics includes:

[0133] Obtaining the GNSS positioning location output by the GNSS receiver chip of the object to be positioned;

[0134] Using a first preset positioning algorithm to correct the GNSS positioning location based on the satellite signal characteristics to obtain the target positioning location.

[0135] In an alternative implementation of this embodiment, obtaining the target positioning location of the object to be positioned based on the GNSS observation data and the satellite signal characteristics includes:

[0136] Using a second preset positioning algorithm to obtain the GNSS positioning location of the object to be positioned based on the GNSS observation data and the satellite signal characteristics.

[0137] The positioning device in the embodiments of the present disclosure corresponds to the above positioning method, and specific details can be referred to the above description of the positioning method, which will not be elaborated here.

[0138] According to an embodiment of the present disclosure, a location-based service providing device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. The location-based service providing device uses the above positioning device to locate the position of the object to be served, and the location-based services include one or more of navigation, map rendering, and route planning.

[0139] The location-based service providing device in the embodiments of the present disclosure corresponds to the above location-based service providing method, and specific details can be referred to the above description of the location-based service providing method, which will not be elaborated here.

[0140] Figure 5 It is a schematic structural diagram of an electronic device suitable for implementing a satellite signal characteristic determination method, a positioning method, and / or a location-based service providing method according to an embodiment of the present disclosure.

[0141] As Figure 5As shown, the electronic device 500 includes a processing unit 501, which can be implemented as a processing unit such as a CPU, GPU, FPGA, NPU, etc. The processing unit 501 can perform various processes in the implementation of any of the above methods of the present disclosure according to a program stored in the read-only memory (ROM) 502 or a program loaded from the storage section 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0142] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage section 508 as needed.

[0143] Specifically, according to an embodiment of the present disclosure, any of the above methods with reference to the embodiments of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly embodied on a machine-readable medium, and the computer program includes program codes for performing any of the methods in the embodiments of the present disclosure. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from the removable medium 511.

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0145] The units or modules involved in the embodiments described in the present disclosure can be implemented in software or in hardware. The described units or modules can also be provided in a processor, and the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0146] On the other hand, the present disclosure also provides a computer-readable storage medium, which can be the computer-readable storage medium included in the device described in the above embodiments; or it can exist separately and be a computer-readable storage medium not assembled into the device. The computer-readable storage medium stores one or more programs, and the one or more programs are used by one or more processors to execute the methods described in the present disclosure.

[0147] The above description is only the preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present disclosure.

Claims

1. A method for determining satellite signal characteristics, wherein, Including: Obtaining GNSS observation data and position correction data received by a positioned object within a target area; The position correction data includes sensor data; The target area is an area where GNSS signals are blocked; Determining corrected position data of the positioned object according to the GNSS observation data, the position correction data, and map data; Determining a measurement error of the GNSS observation data according to the position data and the GNSS observation data; Determining satellite signal characteristics corresponding to the GNSS observation data based on the measurement error; 2. The method according to claim 1, wherein, The sensor data includes the GNSS positioning position of the positioned object and other sensing data; Determining corrected position data of the positioned object according to the GNSS observation data, the position correction data, and map data includes: Correcting the GNSS positioning position based on the map data and / or the other sensing data to obtain the corrected position data; 3. The method according to claim 1 or 2, wherein Determining satellite signal characteristics corresponding to the GNSS observation data based on the measurement error includes: Rasterizing the target area to form a plurality of local areas; For the positioned object whose positioning data is within the local area, statistically analyzing the measurement error corresponding to the observed GNSS observation data to obtain satellite signal characteristics of each satellite within the local area; 4. The method according to claim 3, wherein For the positioned object whose positioning data is within the local area, statistically analyzing the measurement error corresponding to the observed GNSS observation data to obtain satellite signal characteristics of each satellite within the local area includes: Training an algorithm model using the measurement error and the ephemeris of the satellite, so that the trained algorithm model can identify satellite signal characteristics of each satellite within the local area; 5. The method according to claim 4, wherein The algorithm model includes at least one of the following: A satellite selection model, which is a first algorithm model for screening out satellites that can be selected during the current positioning process from a plurality of candidate satellites; A weight determination model, which is used to determine the weights of a plurality of candidate satellites during the current positioning process; 6. A positioning method, wherein, Including: Obtaining GNSS observation data observed on a positioned object and satellite signal characteristics corresponding to the GNSS observation data; wherein, the satellite signal characteristics are obtained according to the satellite signal characteristic determination method described in any one of claims 1-5; Obtaining a target positioning position of the positioned object based on the GNSS observation data and the satellite signal characteristics; 7. The method according to claim 6, wherein, Obtaining a target positioning position of the positioned object based on the GNSS observation data and the satellite signal characteristics includes: Obtaining the GNSS positioning position output by the GNSS receiver chip of the positioned object; Correcting the GNSS positioning position based on the satellite signal characteristics using a first preset positioning algorithm to obtain the target positioning position; 8. The method according to claim 6, wherein Obtaining a target positioning position of the positioned object based on the GNSS observation data and the satellite signal characteristics includes: The GNSS positioning position of the object to be positioned is obtained based on the GNSS observation data and the satellite signal characteristics by using a second preset positioning algorithm.

9. A method for providing location-based services, wherein, The location-based service providing method locates the location of the object to be served by using the positioning method according to any one of claims 6-8, and the location-based service includes one or more of navigation, map rendering, and route planning.

10. An electronic device, wherein, It includes a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method according to any one of claims 1-9.

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