CN114721018A

CN114721018A - Satellite signal characteristic determining method, positioning method and electronic equipment

Info

Publication number: CN114721018A
Application number: CN202210114271.4A
Authority: CN
Inventors: 罗雷刚; 方兴; 高喜乐; 赵启龙; 王超
Original assignee: Autonavi Software Co Ltd
Current assignee: Autonavi Software Co Ltd
Priority date: 2022-01-30
Filing date: 2022-01-30
Publication date: 2022-07-08

Abstract

The embodiment of the disclosure discloses a satellite signal characteristic determining method, a positioning method and electronic equipment, wherein the method comprises the following steps: acquiring GNSS observation data and position correction data received by a positioned object in a target area; the position correction data comprises sensor data; determining the position data of the positioned object after correction according to the GNSS observation data, the position correction data and the 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. According to the technical scheme, the satellite signal characteristics in the target area can be analyzed, the satellite signal characteristics can assist a positioned object entering the target area to obtain a more accurate real-time positioning position, and the satellite positioning precision can be improved.

Description

Satellite signal characteristic determining method, positioning method and electronic equipment

Technical Field

The disclosure relates to the technical field of computers, in particular to a satellite signal feature determination method, a satellite signal feature positioning method and electronic equipment.

Background

With the progress of internet technology, the application of positioning technology is more and more extensive, and people also rely on positioning technology more and more in life such as going out. For example, in a navigation scenario, the position information of the device needs to be acquired continuously, so that the device can be provided with a navigation service continuously.

However, in areas such as many urban high buildings and mountainous canyons, environmental factors such as buildings and mountains in these areas can block, reflect or scatter GNSS satellite signals, which can cause problems such as multipath caused by the fact that GNSS receiver chips carried on devices entering these areas cannot correctly identify the environmental factors, and further cause problems such as jump or drift of GNSS positioning positions and the like, and thus the positioning accuracy is reduced. Therefore, improving the positioning accuracy of the device in these areas is a problem that needs to be solved by those skilled in the art.

Disclosure of Invention

The embodiment of the disclosure provides a satellite signal characteristic determining method, a positioning method and electronic equipment.

In a first aspect, an embodiment of the present disclosure provides a method for determining a satellite signal characteristic, where the method includes:

acquiring GNSS observation data and position correction data received by a positioned object in a target area; the position correction data comprises sensor data;

determining the position data of the positioned object after correction according to the GNSS observation data, the position correction data and the 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.

Further, the sensor data includes GNSS positioning location of the positioned object and other sensor data; determining the corrected position data of the positioned object according to the GNSS observation data, the position correction data and the map data, and the method comprises the following steps:

and correcting the GNSS positioning position based on the map data and/or the other sensing data to obtain the corrected position data.

Further, 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;

and for the positioned object with the positioning data in the local area, performing statistical analysis on the observed measurement error corresponding to the GNSS observation data to obtain the satellite signal characteristics of each satellite in the local area.

Further, for the located object with the positioning data located in the local area, statistically analyzing the observed measurement error corresponding to the GNSS observation data to obtain a satellite signal feature of each satellite in the local area, including:

and training an algorithm model by using the measurement error and the ephemeris of the satellite, so that the trained algorithm model can identify the satellite signal characteristics of each satellite in the local area.

Further, the algorithmic model comprises at least one of:

the satellite selection model is used for screening out a first algorithm model of a satellite which can be selected in the current positioning process from a plurality of candidate satellites;

and the weighting model is used for determining the weights of a plurality of candidate satellites in the current positioning process.

In a second aspect, an embodiment of the present disclosure provides a positioning method, including:

acquiring 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 based on the method of the first aspect;

obtaining a target positioning position of the positioned object based on the GNSS observation data and the satellite signal features.

Further, obtaining a target positioning location of the positioned object based on the GNSS observation data and the satellite signal features comprises:

acquiring a GNSS positioning position output by a GNSS receiver chip of the positioned object;

and correcting the GNSS positioning position based on the satellite signal characteristics by utilizing a first preset positioning algorithm to obtain the target positioning position.

and obtaining the GNSS positioning position of the positioned object based on the GNSS observation data and the satellite signal characteristics by utilizing a second preset positioning algorithm.

In a third aspect, an embodiment of the present disclosure provides a location-based service providing method, where the method locates a location of a served object by using the locating method of the second aspect, and the location-based service includes: one or more of navigation, map rendering, route planning.

In a fourth aspect, an embodiment of the present disclosure provides a method for determining a satellite signal characteristic, including:

a first acquisition module configured to acquire GNSS observation data and position correction data received by a positioned object within a target area; the position correction data comprises sensor data;

a first determining module configured to determine modified position data of the located object according to the GNSS observation data, the position modification data, and map data;

a back-stepping module configured to determine a measurement error of the GNSS observation based on the location data and the GNSS observation;

a second determination module configured to determine satellite signal features corresponding to the GNSS observation based on the measurement error.

The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above-described functions.

In one possible design, the apparatus includes a memory configured to store one or more computer instructions that enable the apparatus to perform the corresponding method, and a processor configured to execute the computer instructions stored in the memory. The apparatus may also include a communication interface for the apparatus to communicate with other devices or a communication network.

In a fifth aspect, the disclosed embodiments provide an electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method of any of the above aspects.

In a sixth aspect, the disclosed embodiments provide a computer-readable storage medium for storing computer instructions for use by any of the above apparatuses, the computer instructions, when executed by a processor, being configured to implement the method of any of the above aspects.

In a seventh aspect, the disclosed embodiments provide a computer program product comprising computer instructions, which when executed by a processor, are configured to implement the method of any one of the above aspects.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to the embodiment of the disclosure, for a target area with a severe environment, GNSS observation data and position correction data of a positioned object entering the target area may be acquired, where the position correction data may include, but is not limited to, sensor data and/or map data of the target area; the method comprises the steps of obtaining position data with high precision of a positioned object in a target area by utilizing GNSS observation data and position correction data, further utilizing the position data with high precision to reversely deduce measurement errors of the GNSS observation data, and utilizing the measurement errors to determine satellite signal characteristics corresponding to the GNSS observation data. By the method, the satellite signal characteristics in the target area can be analyzed, the influence of environmental change, weather change, satellite change and the like in the target area on the satellite signal can be reflected in real time by the satellite signal characteristics, and then a positioned object entering the target area can be assisted to obtain a more accurate real-time positioning position, so that the satellite positioning precision can be improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:

fig. 1 shows a flow chart of a method of determining satellite signal characteristics according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram illustrating an application flow of satellite signal characteristic determination according to an embodiment of the present disclosure;

FIG. 3 shows a flow chart of a positioning method according to an embodiment of the present disclosure;

FIG. 4 illustrates a schematic view of a vehicle navigation application scenario according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device suitable for implementing a satellite signal feature determination method, a positioning method, and/or a location-based service provision method according to an embodiment of the present disclosure.

Detailed Description

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. Furthermore, parts that are not relevant to the description of the exemplary embodiments have been omitted from the drawings for the sake of clarity.

In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, actions, components, parts, or combinations thereof, and do not preclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof are present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

The inventor of the present disclosure finds that the influence of satellite signals in regions with more urban high buildings, mountainous canyons, etc. is related to the surrounding environment of the positioning device, and the correlation is basically stable in a period of time. Therefore, the present disclosure provides a method for determining satellite signal characteristics, which is used to mine satellite signal characteristics of a satellite in a local area, and then assist a positioning device entering the local area to perform position positioning by using the satellite signal characteristics.

The details of the embodiments of the present disclosure are described in detail below with reference to specific embodiments.

Fig. 1 shows a flow chart of a method of determining satellite signal characteristics according to an embodiment of the present disclosure. As shown in fig. 1, the method for determining the satellite signal characteristics includes the following steps:

in step S101, GNSS observation data and position correction data received by a target to be located in a target area are acquired; the position correction data comprises sensor data;

in step S102, determining corrected position data of the positioned object according to the GNSS observation data, the position correction data, and the map data;

in step S103, determining a measurement error of the GNSS observation data according to the position data and the GNSS observation data;

in step S104, a satellite signal feature corresponding to the GNSS observation data is determined based on the measurement error and the GNSS observation data.

In this embodiment, the method may be performed on a server. The server may collect one or more targeted GNSS observations and position fix data into the target area; the position correction data may include, but is not limited to, map data and/or sensor data. The located object may be an object that can acquire GNSS observation data and sensor data of the located object in a target area, and may be, for example, a mobile phone, ipad, computer, smart watch, vehicle, robot, or the like of a crowdsourced user. In some embodiments, the target area may be a predetermined area where occlusion may exist; in other embodiments, the target area may be any area.

The object to be positioned may receive satellite signals from a plurality of satellites, and it is understood that the satellite signals received by the object to be positioned at different times may be from different satellites, and the number of satellites capable of receiving the satellite signals may also be different. The GNSS receiver provided on the object to be located can resolve GNSS observation data from satellite signals of a plurality of satellites received at the present time, and then resolve a location position of the object to be located at the present time based on the GNSS observation data.

GNSS refers to global satellite navigation systems, i.e., autonomous geospatial positioning satellite systems that cover the globe; GNSS includes GPS (global positioning system), GLONASS (GLONASS system), BDS (beidou satellite navigation system), Galileo (Galileo positioning system). GNSS observation data refers to satellite observations obtained by a receiver and may include, but is not limited to, pseudorange rate, carrier, signal-to-noise ratio, and the like. After the receiver receives the satellite signals, GNSS observation data may be directly or indirectly acquired from the satellite signals.

The sensor data may be data acquired by a sensor disposed on the located object. The sensors may include, but are not limited to, GNSS receivers, accelerometers, gyroscopes, magnetometers, visual sensors, and the like. The sensor data may include, but is not limited to, GNSS positioning position output by the GNSS receiver, acceleration, angular velocity, orientation of the object being positioned, and ambient image data, among others. The map data may be map data including an area through which the object is located, and the map data may be stored in the server in advance.

In the embodiment of the disclosure, if the object to be located is currently located in an area where the satellite signal is greatly affected by the environment, the GNSS observation data is affected by the environment, so that a certain error exists in the GNSS observation data, and further, the GNSS positioning position calculated by the GNSS receiver on the object to be located in real time according to the GNSS observation data is inaccurate, or the accuracy of the GNSS positioning position is low. Therefore, in the embodiments of the present disclosure, the inaccurate GNSS positioning position may also be calibrated by using the map data and/or other sensing data, such as the acceleration, azimuth, and angular velocity of the positioned object, so as to obtain position data with higher accuracy than the GNSS positioning position. In some embodiments, the position data with higher precision can be obtained by using a kalman smoothing algorithm, a map matching algorithm or the like. In some embodiments, the position data may be true trajectory data, referred to as a true trajectory, of the object being located within the target area, the true trajectory having a higher accuracy than the trajectory data obtained from a GNSS location position computed in real time using GNSS observation data.

Therefore, the measurement error of the GNSS observation data can be inversely derived by using the position data with higher accuracy. In some embodiments, the measurement error of the GNSS observation data may include, but is not limited to, a pseudorange error, a pseudorange rate error, and an error of the carrier. In other embodiments, the measurement error of the GNSS observation data may further include a signal-to-noise ratio error, but since the signal-to-noise ratio error is related to hardware, the signal-to-noise ratio of each hardware device, i.e., the object being located, is substantially constant, and thus the signal-to-noise ratio may be used directly in the measurement error of the GNSS observation data.

After obtaining the measurement error of the GNSS observation data, the corresponding satellite signal characteristics may be determined from the measurement error. For example, since a viaduct exists in the target area and the measurement error of GNSS observation data of a satellite blocked by the viaduct is large after the object to be located enters the vicinity of the viaduct, it can be determined that the satellite signal received from the azimuth has the characteristics of being blocked, reflected, or multipath after the object to be located enters the vicinity of the viaduct in the target area.

In some embodiments, after the server determines the satellite signal characteristics based on the above method, the server may broadcast the satellite signal characteristics to the located object entering the target area (note that the located object herein may include the located object used by the server for collecting sensor data and other located objects). After receiving the satellite signal characteristics broadcast by the server, the object to be located may jointly determine a real-time location position according to the satellite signal characteristics and GNSS observation data, so as to improve accuracy of the location position.

According to the embodiment of the disclosure, for a target area with a severe environment, GNSS observation data and position correction data of a positioned object entering the target area may be acquired, where the position correction data may include but is not limited to sensor data; the GNSS observation data, the position correction data, and the map data pre-stored in the server are used to obtain the position data of the positioned object with higher precision in the target area (that is, the position data is the more accurate position data of the positioned object in the target area), and then the position data with higher precision is used to reversely derive the measurement error of the GNSS observation data, and the measurement error is used to determine the satellite signal characteristics corresponding to the GNSS observation data. By the method, the satellite signal characteristics in the target area can be analyzed, the influence of environmental change, weather change, satellite change and the like in the target area on the satellite signal can be reflected in real time by the satellite signal characteristics, and then a positioned object entering the target area can be assisted to obtain a more accurate real-time positioning position, so that the satellite positioning precision can be improved.

In an optional implementation manner of this embodiment, the sensor data includes a GNSS positioning position of the positioned object and other sensor data; step S102, namely, 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 steps of:

and correcting the GNSS positioning position based on the map data and the other sensing data to obtain the corrected position data.

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

Based on the map data and/or other sensory data, the server may correct a GNSS positioning position output by the targeted GNSS receiver, and the corrected positioning position may form trajectory data of the targeted object within the target area, which may be used as calibrated position data.

It should be noted that, in some embodiments, the real-time positioning position may be corrected by using a map matching algorithm and map data, for example, after the GNSS positioning position at a certain time or at certain times drifts, the drifted GNSS positioning position may be corrected based on the map data. In other embodiments, the real-time positioning position may be corrected by using a kalman smoothing method and other sensing data. The specific modification mode can be selected based on actual needs, and is not limited herein.

In an optional implementation manner of this embodiment, in 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:

rasterizing the target area to form a plurality of local areas;

for the located object with the positioning data located in the local area, performing statistical analysis on the observed measurement error corresponding to the GNSS observation data to obtain a satellite signal characteristic of each satellite in the local area.

In this alternative implementation, in the same region, the environmental influence on the satellite signal over a period of time is substantially stable, so that by performing statistical analysis on the measurement error of the GNSS observation data affected by the environment in the region, the satellite signal characteristic affected by the environment can be determined, and the satellite signal characteristic will remain unchanged in the region over a period of time. After determining the satellite signal characteristics, the positioning device entering the area may correct the GNSS positioning position based on the satellite signal characteristics, so as to eliminate the influence of the environment, and obtain a more accurate positioning position.

In a real environment, an environmental region that has a large influence on a satellite signal may be only a local region, such as a region where an overpass exists, a canyon region, or the like, and the satellite signal is just blocked by the overpass or the canyon from the relative position relationship between the satellite and the object to be located. If the target area is large, for example, when both a blocked area and an open area exist, the measurement error corresponding to the GNSS observation data is counted, and then the measurement error is counted and analyzed to obtain satellite signal characteristics, which may result in inaccurate satellite signal characteristics, and if the target area is large, the calculated amount is also large under the condition that the data to be counted is large, so that the server is stressed greatly, and meanwhile, the satellite signal characteristics which are accurate may not be output timely, and finally, the position accuracy of the positioned object is reduced.

Therefore, the embodiment of the present disclosure obtains a plurality of local regions by rasterizing the target region. Each local area corresponds to a rasterized grid, the rasterization may adopt rectangular division or road side division, and the specific division mode may be set according to actual needs, and is not specifically limited herein.

For each divided local area, the measurement error corresponding to the GNSS observation data observed when the located object is located in the local area may be counted, so as to obtain the characteristics of the satellite signal received in the local area from a large number of measurement errors through a big data analysis method. It should be noted that whether the positioning device is located in the local area may be determined based on the above-mentioned more accurate position data obtained after correction.

The satellite signal characteristics may reflect the environmental impact of the satellite signal in the local area. Therefore, the server may broadcast the satellite signal characteristics to the positioned object entering the local area, so as to assist the positioned object in correcting the GNSS real-time positioning position based on the satellite signal characteristics. The server may statistically analyze the satellite signal characteristics of each local area at regular intervals or in real time, and broadcast the satellite signal characteristics obtained by the statistical analysis to the target entering the local area.

In an optional implementation manner of this embodiment, the step of performing statistical analysis on the measurement error corresponding to the GNSS observation data obtained in the local area to obtain a satellite signal feature of each satellite in the local area further includes the following steps:

In this optional implementation manner, statistical analysis of the measurement error corresponding to the GNSS observation data obtained in the local area may be implemented by using a mode of training an artificial intelligence algorithm model. The measurement error corresponding to the GNSS observation data and the ephemeris of the satellite corresponding to the GNSS observation data may be input to a preset algorithm model, so that the algorithm model learns the signal characteristics of the satellite observing the GNSS observation data, where it should be noted that the measurement error of the GNSS observation data is related to the current position of the satellite, and therefore when the algorithm model is trained, in addition to the input of the measurement error, the ephemeris of the satellite needs to be input, so as to determine the position of the satellite when the GNSS observation data is received. In this way, an algorithm model can be finally trained, and the algorithm model can identify signal characteristics of satellite signals received from the satellite based on the position of the satellite (which can be determined based on ephemeris of the satellite), and can further correct the GNSS real-time positioning position of the positioning device in the local area according to the signal characteristics.

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

In this alternative implementation, the satellite selection model may be used to classify a plurality of candidate satellites, for example, NLOS (Non Line of Sight) satellites may be screened from the plurality of candidate satellites. Because satellite signals received from NLOS satellites are greatly influenced by the environment, the GNSS positioning position is obtained through calculation based on GNSS observation data of the satellites, and the accuracy is low. Therefore, the satellite selection model can classify a plurality of candidate satellites into NLOS satellites and non-NLOS satellites, and when the positioning is carried out on the positioned object in real time, the NLOS satellites can be excluded, and the target position of the positioned object can be calculated by utilizing GNSS observation data of the non-NLOS satellites, so that the satellite positioning accuracy can be improved.

A weighting model may be used to weight each candidate satellite. The weighting model can assign weight values to a plurality of candidate satellites which can be observed in a local area, satellites with good satellite signal quality can be assigned with higher weight values, and satellites with poor satellite signal quality can be assigned with lower weight values. Through the weighting model, the positioned object can endow different weighting values to a plurality of observed candidate satellites, and when the real-time positioning position of the positioned object is calculated, the proportion of GNSS observation data corresponding to satellites with high weighting values is increased, and the proportion of GNSS data corresponding to satellites with low weighting values is reduced. In this way, the satellite positioning accuracy can be improved.

In some embodiments, the satellite selection model and the weighting model may be obtained by training after considering the characteristics of the positioning algorithm used in the real-time positioning process, that is, the satellite selection model and the weighting model are trained into a model adapted to the positioning algorithm used in the real-time positioning; in some embodiments, the weight of the rejected satellites and the weight of the satellites are adjusted accordingly based on the characteristics of the positioning algorithm used in the real-time positioning.

Fig. 2 is a schematic diagram illustrating an application flow of satellite signal characteristic determination according to an embodiment of the present disclosure. As shown in fig. 2, the user terminal may include a plurality of user devices, each of which is provided with a GNSS receiver, and each of which may also be provided with other sensors, such as a magnetometer, an angular velocity meter, an accelerometer, and a vision sensor. The server includes one or more servers, and a plurality of user devices at the user side can communicate with the server at the server. The server at the server side can collect the GNSS observation data of the user equipment in the target area from the user equipment and the GNSS positioning position calculated by the GNSS receiver based on the GNSS observation data, and can also collect the GNSS observation data and other sensor data from the user equipment.

The server may also perform a cleaning of the collected GNSS positioning locations, GNSS observation data and other sensor data to exclude anomalous devices and users. A true-track post-processing service may be run on the server for obtaining a true-track of the user equipment in the target area based on the GNSS positioning location, the GNSS observation data, and other sensor data, the location data in the true-track being more accurate than the GNSS positioning location.

The true track output by the true track post-processing service can be used for reversely deducing the measurement error of the GNSS observation data, and further an error model of a local area, such as a satellite selection model or a weight determination model, can be obtained through training according to the reversely deduced measurement error. The model parameters of the error model can be broadcasted to the positioned object entering the local area by the server, and the positioned object can obtain the satellite signal characteristics of the satellite observed in the local area based on the received model parameters of the error model, so that real-time positioning can be performed according to the satellite signal characteristics and GNSS observation data. It should be noted that the object to be located may also be a user device for the server to collect GNSS observation data and sensor data, and of course, the object to be located may not be a user device for the server to collect GNSS observation data and sensor data.

Fig. 3 shows a flow chart of a positioning method according to an embodiment of the present disclosure. As shown in fig. 3, the positioning method includes the following steps:

in step S301, GNSS observation data observed on a positioned object and satellite signal features corresponding to the GNSS observation data are acquired; the satellite signal characteristics are obtained based on the determination method of the satellite signal characteristics;

in step S302, a target positioning position of the positioned object is obtained based on the GNSS observation data and the satellite signal features.

In this embodiment, the method may be performed on a localized object entering the local region. The located object may be any located object entering the local area, for example, a cell phone, ipad, computer, smart watch, vehicle, robot, etc. of any user. The server may broadcast satellite signal features corresponding to each candidate satellite to the located object entering the local area, and the located object may receive satellite signal features observable in the local area from the server. The satellite signal characteristics may be characteristics that characterize the satellite signals transmitted by the satellites as affected by the environment. The object being located may calculate a target position location in real time based on the observed satellite signal characteristics and corresponding GNSS observation data. It should be noted that the satellite signal characteristics received by the positioned object from the server may be direct signal characteristics exhibited by each satellite, or may be model parameters, and the direct signal characteristics can be identified based on the model parameters.

In some embodiments, the local region may be a region where satellite signals are more affected by the environment, which may result in inaccurate GNSS positioning. The details of the local region can be found in the description of the above satellite signal features, and are not described herein again.

In some embodiments, the satellite signal features may be characterized by a trained algorithm model, for example, the server may obtain an algorithm model capable of identifying the satellite signal features according to the above determination method of the satellite signal features, and broadcast the model parameters of the algorithm model to the targeted object entering the local area. In some embodiments, the algorithm model may include, but is not limited to, a classification model and a weighting model, and after the positioned object receives the model parameters of the algorithm model, the satellites observed in the local area may be classified or weighted based on the model parameters, and then the target positioning position in the local area is calculated in real time according to the classification or weighting result and the corresponding GNSS observation data. It should be noted that the server may obtain different types of satellite signal characteristics of each satellite based on the above-mentioned determination method of the satellite signal characteristics, and the different types of satellite signal characteristics may be all broadcasted to all the located objects entering the entrance area, or may be selectively broadcasted to the located objects entering the entrance area, for example, the matched satellite signal characteristics may be broadcasted to the corresponding located objects according to the type of the positioning algorithm on the located objects.

After the satellite signal features are received by the object to be positioned, a preset positioning algorithm can be called, and a target positioning position is calculated based on the satellite signal features and GNSS observation data. It should be noted that, in a typical GNSS positioning method, a GNSS receiver chip on a positioned object may automatically calculate a chip solution, that is, a GNSS positioning position, based on observed GNSS observation data. However, in consideration of the fact that satellite signals in a local area are greatly influenced by the environment, the GNSS positioning position obtained by directly using GNSS observation data is not accurate, and therefore a target to be positioned entering the local area can be calculated to obtain a target positioning position with higher accuracy by using the satellite signal characteristics of observation satellites as auxiliary information on the basis of the GNSS observation data.

For other relevant details in this embodiment, reference may be made to the above description of the method for determining the satellite signal characteristics, and details are not described herein again.

In the embodiment of the disclosure, the target positioning position of the positioned object entering the local area can be calculated based on the satellite signal characteristics predetermined by the server and the GNSS observation data, which is equivalent to that on the basis of the GNSS observation data, the satellite signal characteristics are used as auxiliary information for position calculation, so that the GNSS positioning position is corrected to a certain extent, and the satellite positioning accuracy is improved.

In an optional implementation manner of this embodiment, in step S302, the step of obtaining the target positioning location of the positioned object based on the GNSS observation data and the satellite signal features further includes the following steps:

The alternative implementation may be implemented at a software level, and the server may issue the satellite signal characteristics to the software level of the located object. The GNSS receiver on the positioned object calculates a GNSS positioning position by using the observed GNSS observation data, and the GNSS positioning position output by the GNSS receiver is corrected by a preset first preset positioning algorithm on the software level based on the satellite signal characteristics issued by the server, so that the corrected target positioning position is finally obtained. The first preset positioning algorithm may adopt a single-point positioning algorithm, a filtering algorithm, a double-difference algorithm, and the like. The single-point positioning algorithm may be, for example, an SPP algorithm, the filtering algorithm may be, for example, a kalman filter, a loose combination, a tight combination navigation positioning algorithm, a PPP positioning algorithm, a particle filter algorithm, etc., and the double-difference algorithm may be, for example, an RTK algorithm, etc.

In this way, the GNSS positioning position output by the GNSS receiver can be corrected only by the first predetermined positioning algorithm at the software level without making excessive changes at the system level and/or the hardware level. Therefore, in this way, the application extensibility and robustness of the embodiments of the present disclosure can be improved.

In an optional implementation manner of this embodiment, in step S302, the step of obtaining the target positioning position of the positioned object based on the GNSS observation data and the satellite signal features further includes the following steps:

In this optional implementation, the server may issue the satellite signal feature to a system level or a 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, so that the improved positioning algorithm is used to obtain the GNSS positioning location based on the GNSS observation data and the satellite signal feature. For example, in a resolving algorithm of the GNSS receiver, after the GNSS observation data of the NLOS model is filtered out by using the satellite selection model issued by the server, the GNSS positioning position is calculated by using the retained GNSS observation data, and the GNSS positioning position is determined as a target positioning position of the positioned object; or, a weighting model issued by the server may be used to determine a weight value of each observed satellite, and the GNSS observation value corresponding to the satellite is weighted by the weight value, and then the GNSS positioning position is calculated.

A location-based service providing method according to an embodiment of the present disclosure includes: the position of the served object is located by using the locating method, and the position-based service comprises the following steps: one or more of navigation, map rendering, route planning.

In this embodiment, the location-based service providing method may be executed on a terminal, where the terminal is a mobile phone, an ipad, a computer, a smart watch, a vehicle, or the like. According to the positioning method, the server counts the satellite signal characteristics of each satellite for the target area with the satellite signals greatly affected by the environment, the satellite signal characteristics are issued to the served object entering the target area, and then the served object provides position-based services such as navigation, map rendering, route planning and the like based on the satellite signal characteristics.

The served object can be a mobile phone, ipad, computer, smart watch, vehicle, robot, etc. The location position of the served object may be obtained by using the above-mentioned location method, and specific details may refer to the above description of the location method, which is not described herein again.

Fig. 4 shows a schematic view of a vehicle navigation application scenario according to an embodiment of the present disclosure. As shown in fig. 4, the vehicle navigation scenario may be divided into an offline process and a real-time positioning process. In the off-line process, in the viaduct area of city a, the server collects GNSS observation data, which may be obtained by the vehicle navigation apparatus from satellite signals received by satellite a (shown as 1 satellite, and may be multiple in practical applications), and sensor data, which may include GNSS positioning position and other sensor data output by the GNSS receiver, from the vehicle navigation apparatus of the crowdsourced users passing through the viaduct area. The server may also obtain map data for the elevated bridge area from an electronic map service system. After the server collects the data, a star selection model and a weighting model can be obtained based on the data training. And broadcasting the model parameters of the satellite selection model and the weighting model to any vehicle entering the viaduct area. In the real-time positioning process, after receiving the model parameters, the navigation terminal on any vehicle receives satellite signals from satellites B (shown as 1, a plurality of satellites may be used in practical applications, and the satellites a and the satellites B may be the same or different), and analyzes the satellite signals to obtain GNSS observation data, and then filters the GNSS observation data of the observed satellites by using the model parameters, or assigns weight values to the observed satellites, and when calculating a GNSS positioning position, the navigation terminal can process the GNSS data based on the filtering results or the weighting results to obtain a more accurate target positioning position; and navigating the vehicle based on the target positioning position.

According to the satellite signal characteristic determination device of an embodiment of the present disclosure, the device may be implemented as part or all of an electronic device through software, hardware or a combination of the two. The device for determining the satellite signal characteristics comprises:

In an optional implementation manner of this embodiment, the sensor data includes a GNSS positioning position of the positioned object and other sensor data; the first determining module includes:

a correction submodule configured to correct the GNSS positioning position based on the map data and/or the other sensing data, resulting in corrected position data.

In an optional implementation manner of this embodiment, the second determining module includes:

a rasterization sub-module configured to rasterize the target region to form a plurality of local regions;

a statistic submodule configured to perform statistical analysis on the measurement error corresponding to the GNSS observation data observed for the located object whose positioning data is located in the local area, so as to obtain a satellite signal feature of each satellite in the local area.

In an optional implementation manner of this embodiment, the statistics sub-module includes:

and the identification submodule is configured to train an algorithm model by using the measurement error and ephemeris of the satellite, so that the trained algorithm model can identify the satellite signal characteristics of each satellite in the local area.

The determining device of the satellite signal characteristics in the embodiment of the present disclosure corresponds to and is consistent with the determining method of the satellite signal characteristics, and specific details may be referred to the determining method of the satellite signal characteristics, which is not described herein again.

According to the positioning device of an embodiment of the present disclosure, the device may be implemented as part or all of an electronic device through software, hardware or a combination of the two. The positioning device includes:

acquiring 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 based on the method of any one of claims 1-5;

In an optional implementation manner of this embodiment, obtaining the target positioning position of the located object based on the GNSS observation data and the satellite signal feature includes:

The positioning device in the embodiment of the present disclosure corresponds to the positioning method, and specific details may refer to the positioning method, which are not described herein again.

According to the location-based service providing apparatus of an embodiment of the present disclosure, the apparatus may be implemented as part or all of an electronic device by software, hardware, or a combination of both. The location-based service providing device uses the positioning device to position the location of the served object, and the location-based service includes: one or more of navigation, map rendering, route planning.

The location-based service providing apparatus in the embodiment of the present disclosure corresponds to the location-based service providing method, and specific details may refer to the location-based service providing method, which are not described herein again.

As shown in fig. 5, the electronic device 500 includes a processing unit 501, which may be implemented as a CPU, GPU, FPGA, NPU, or the like processing unit. The processing unit 501 may perform various processes in the embodiments of any one of the methods described above of the present disclosure according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing unit 501, the ROM502, and the RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

In particular, according to embodiments of the present disclosure, any of the methods described above with reference to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing any of the methods of the embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A method for determining characteristics of a satellite signal, comprising:

2. The method of claim 1, wherein the sensor data includes a GNSS positioning location of the located object and other sensor data; determining the corrected position data of the positioned object according to the GNSS observation data, the position correction data and the map data, comprising:

3. The method of claim 1 or 2, wherein determining satellite signal characteristics corresponding to the GNSS observation data based on the measurement error comprises:

rasterizing the target area to form a plurality of local areas;

4. The method according to claim 1 or 2, wherein statistically analyzing the observed measurement error corresponding to the GNSS observation data for the located object whose positioning data is located in the local area to obtain satellite signal characteristics of each satellite in the local area comprises:

5. The method of claim 4, wherein the algorithmic model comprises at least one of:

6. A method of positioning, comprising:

7. The method of claim 6, wherein obtaining a target position location of the located object based on the GNSS observation data and the satellite signal features comprises:

8. The method of claim 6, wherein obtaining a target position location of the located object based on the GNSS observation data and the satellite signal features comprises:

9. A location-based service providing method, wherein the method locates a location of a served object using the locating method of any one of claims 6-8, the location-based service comprising: one or more of navigation, map rendering, route planning.

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