Positioning method and device of positioning system, storage medium and computer program product

Abstract

The present invention proposes a positioning method and device, a storage medium and a computer program product for a positioning system. The method comprises: acquiring first data output by a first navigation and positioning system and second data output by a second navigation and positioning system; eliminating abnormal data in the first data and / or the second data to determine target data; and determining a positioning result according to the target data. The present invention preprocesses the first data and / or the second data to effectively eliminate abnormal data caused by external environmental interference in the first navigation and positioning system and the second navigation and positioning system, thereby avoiding the abnormal data from being mixed into normal data and improving the accuracy of subsequent navigation and positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning systems, and in particular to a positioning method and device, a storage medium and a computer program product of a positioning system. Background Art

[0002] In related technologies, a combined navigation system of the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is used to fuse the navigation data obtained by each navigation subsystem, so as to obtain the optimal positioning result under the combined navigation system. However, in actual applications, compared with the autonomous system of INS, GNSS is more susceptible to interference and influence from the external environment, thereby reducing the accuracy of subsequent navigation positioning. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0004] To this end, an object of the present invention is to propose a positioning method for a positioning system, which effectively eliminates abnormal data caused by external environmental interference in the first navigation and positioning system and the second navigation and positioning system by preprocessing the first data and / or the second data, avoids the abnormal data from being mixed into normal data, and improves the accuracy of subsequent navigation and positioning.

[0005] Therefore, a second object of the present invention is to provide a positioning device for a positioning system.

[0006] To this end, a third object of the present invention is to provide a computer-readable storage medium.

[0007] To this end, a fourth object of the present invention is to provide a computer program product.

[0008] In order to achieve the above-mentioned purpose, an embodiment of the first aspect of the present invention proposes a positioning method for a positioning system, the method comprising: acquiring first data output by a first navigation and positioning system and second data output by a second navigation and positioning system; eliminating abnormal data in the first data and / or the second data to determine target data; and determining a positioning result based on the target data.

[0009] According to the positioning method of the positioning system of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system, so as to eliminate the abnormal data in the first data and / or the second data and obtain the target data, and the positioning result of the positioning system is determined according to the screened target data, and the abnormal data caused by the external environment interference of the first navigation and positioning system and the second navigation and positioning system are effectively eliminated by preprocessing the first data and / or the second data, so as to avoid the abnormal data from being mixed into the normal data, and improve the accuracy of subsequent navigation and positioning.

[0010] In some embodiments, the eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the first data; and eliminating abnormal positioning data in the positioning data in the first data to determine the target data.

[0011] In some embodiments, the step of eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the first data; eliminating abnormal positioning data in the positioning data in the first data to obtain first target data; and determining the target data based on the first target data and the second data.

[0012] In some embodiments, the eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the second data; and eliminating abnormal positioning data in the positioning data in the second data to determine the target data.

[0013] In some embodiments, the process of eliminating abnormal data in the first data and / or the second data to determine the target data includes: obtaining positioning data in the first data and positioning data in the second data; correspondingly eliminating abnormal positioning data in the positioning data in the first data and abnormal positioning data in the positioning data in the second data to obtain first target data and second target data; and determining the target data based on the first target data and the second target data.

[0014] In some embodiments, eliminating abnormal positioning data in the positioning data includes: determining a first boundary average value and a second boundary average value of the positioning data; and eliminating the abnormal positioning data in the positioning data according to the first boundary average value and the second boundary average value.

[0015] In some embodiments, eliminating abnormal positioning data in the positioning data according to the first boundary average value and the second boundary average value includes: determining a valid area according to the first boundary average value and the second boundary average value; and eliminating abnormal positioning data outside the valid area.

[0016] In some embodiments, determining the valid area based on the first boundary average value and the second boundary average value includes: using the first boundary average value as the valid lower limit value and using the second boundary average value as the valid area upper limit value to determine the valid area.

[0017] In some embodiments, the eliminating abnormal positioning data in the positioning data includes: determining an initial cluster center based on the positioning data of a preset type; determining positioning data to be classified of the positioning data; cyclically determining the abnormal positioning data in the positioning data to be classified based on the initial cluster center and the positioning data to be classified, and eliminating the abnormal positioning data.

[0018] In some embodiments, the loop determines abnormal positioning data in the positioning data to be classified based on the initial cluster center and the positioning data to be classified, and eliminates the abnormal positioning data, including: obtaining the distance between the positioning data to be classified and the initial cluster center; dividing the positioning data to be classified into a set cluster with the smallest distance from the initial cluster center; determining the mean of the positioning data to be classified in the set cluster; using the mean as the target cluster center of the set cluster; and when the target cluster center remains unchanged, eliminating the abnormal positioning data outside the set cluster.

[0019] In some embodiments, the elimination of abnormal positioning data in the positioning data includes: obtaining an average value of distances from the positioning data to adjacent positioning data; determining a density-delineated area based on the positioning data and the average value of the distances; obtaining a density value of the density-delineated area; and filtering the abnormal positioning data in the positioning data based on the density value, and eliminating the abnormal positioning data in the positioning data.

[0020] In some embodiments, determining the density demarcation area based on the positioning data and the distance average value includes: taking the positioning data as the center of a circle and the distance average value as a radius; and determining the density demarcation area based on the center of the circle and the radius.

[0021] In some embodiments, obtaining the density value of the density defined area includes: using the quantity of the positioning data in the density defined area as the density value of the defined area.

[0022] In some embodiments, screening the abnormal positioning data in the positioning data according to the density value includes: obtaining high-density points and low-density points in the positioning data according to the density value and the positioning data; and determining the abnormal positioning data according to the high-density points and the low-density points.

[0023] In some embodiments, acquiring high-density points in the positioning data according to the density value and the positioning data includes: taking points whose density values ​​are greater than or equal to a preset density threshold as the high-density points.

[0024] In some embodiments, acquiring low-density points in the positioning data according to the density value and the positioning data includes: taking points whose density values ​​are less than a preset density threshold as the low-density points.

[0025] In some embodiments, determining the anomaly location data based on the high-density points and the low-density points includes: if there are overlapped high-density points and / or low-density points between the density-divided areas, connecting the high-density points and / or the low-density points to obtain a screening cluster; and using the points outside the screening cluster as the anomaly location data.

[0026] In some embodiments, determining the positioning result according to the target data includes: determining a data difference between the target data and the first data, or determining a data difference between the target data and the second data; and determining the positioning result according to the data difference.

[0027] In some embodiments, determining the positioning result based on the data difference includes: if the data difference is greater than a preset deviation threshold, removing the target data; determining the positioning result based on the first data, or determining the positioning result based on the second data.

[0028] In some embodiments, determining the positioning result based on the data difference includes: if the data difference is less than or equal to a preset deviation threshold, determining the first weight of the target data and the second weight of the first data, or determining the first weight of the target data and the second weight of the second data; weighting the target data and the first data according to the first weight and the second weight, or weighting the target data and the second data according to the first weight and the second weight to determine the positioning result.

[0029] In order to achieve the above-mentioned purpose, an embodiment of the second aspect of the present invention proposes a positioning device of a positioning system, the device comprising: an acquisition module, used to acquire first data output by a first navigation and positioning system and second data output by a second navigation and positioning system; a elimination module, used to eliminate abnormal data in the first data and / or the second data to determine target data; a determination module, used to determine a positioning result based on the target data.

[0030] According to the positioning device of the positioning system of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system, so as to eliminate the abnormal data in the first data and / or the second data and obtain the target data, and the positioning result of the positioning system is determined according to the screened target data. By preprocessing the first data and / or the second data, the abnormal data caused by the interference of the external environment in the first navigation and positioning system and the second navigation and positioning system are effectively eliminated, so as to avoid the abnormal data from being mixed into the normal data and improve the accuracy of subsequent navigation and positioning.

[0031] In order to achieve the above-mentioned purpose, an embodiment of the third aspect of the present invention proposes a computer-readable storage medium, on which a positioning program of a positioning system is stored. When the positioning program of the positioning system is executed by a processor, the positioning method of the positioning system described in the above-mentioned embodiment is implemented.

[0032] In order to achieve the above-mentioned purpose, an embodiment of the fourth aspect of the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, the positioning method of the positioning system described in the above-mentioned embodiment is implemented.

[0033] According to the computer program product of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system to eliminate abnormal data in the first data and / or the second data to obtain target data, and the positioning result of the positioning system is determined according to the screened target data. By preprocessing the first data and / or the second data, abnormal data caused by external environmental interference in the first navigation and positioning system and the second navigation and positioning system are effectively eliminated to avoid abnormal data from being mixed into normal data, thereby improving the accuracy of subsequent navigation and positioning.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0036] Figure 1 It is a structural schematic diagram of a combined navigation system of related technology;

[0037] Figure 2 is a flow chart of a positioning method of a positioning system according to an embodiment of the present invention;

[0038] Figure 3 is a schematic structural diagram of an integrated navigation system according to an embodiment of the present invention;

[0039] Figure 4 is a flow chart of a positioning method of a positioning system according to a specific embodiment of the present invention;

[0040] Figure 5 is a structural block diagram of a positioning device of a positioning system according to an embodiment of the present invention.

[0041] Reference numerals:

[0042] Positioning device 2 of the positioning system;

[0043] Acquisition module 21; elimination module 22; determination module 23. DETAILED DESCRIPTION

[0044] Embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. Embodiments of the present invention are described in detail below.

[0045] Navigation aims to guide moving objects to reach their destinations accurately and provide them with real-time information such as position, speed and attitude. With the rapid development of human activities, navigation and positioning technology has been constantly improving, and its application scope has been widely spread over land, sea and air. Navigation and positioning technology originated in the military field. Now it has been rapidly developed in the civilian field and has gradually been integrated into daily life. For example, online car-hailing, food delivery and shared bicycles that can be seen everywhere on the streets all need navigation and positioning technology to provide support. In the Internet age, the application scenarios of navigation and positioning technology cover almost all walks of life in society, making this technology one of the fastest-growing information industries in the world. Navigation and positioning technology involves the intersection of multiple disciplines. According to the different types of sensors in the navigation and positioning system, the navigation and positioning system can be divided into satellite navigation system, INS and astronomical navigation system, among which satellite navigation system and INS are the most commonly used.

[0046] Vehicle-mounted navigation and positioning systems usually require that their positioning functions meet all-weather, any road conditions, and real-time high-precision requirements. However, different navigation and positioning systems have their own strengths, and it is difficult to achieve the above application scenarios with only a single navigation and positioning system. For example, GNSS has been widely used in many fields due to its all-weather and real-time high-precision characteristics. As of now, the global satellite navigation systems that have been completed and are under construction include the Global Positioning System (GPS) navigation system, the Beidou Navigation Satellite System (BDS) navigation system, the GLONASS navigation system, and the Galileo navigation system.

[0047] The weakness of GNSS is that it has poor anti-interference ability and is easily affected by terrain or building obstruction. In the presence of obstruction, the navigation performance of GNSS is very poor. INS uses inertial devices such as accelerometers and gyroscopes to obtain the specific force and angular velocity information of the moving target, and then obtains the trajectory information of the moving target through integral calculation, thereby realizing real-time navigation. Since INS is an autonomous system, it is self-contained and not subject to external interference, and its anti-interference ability is stronger than GNSS. However, in the process of solving the trajectory of the moving target, INS is easily affected by the external environment and its own inertial devices. The errors generated will accumulate over time, resulting in a decrease in the positioning accuracy of INS. Therefore, in order to realize the requirements of all-weather, arbitrary road conditions and real-time high-precision vehicle navigation and positioning, it is necessary to combine the above-mentioned single navigation and positioning systems, make full use of the advantages of each system, and learn from each other's strengths to make up for their weaknesses. For example, the GNSS / INS combined navigation system composed of GNSS and INS can effectively complement the advantages of GNSS and INS, make up for the performance defects caused by the two working alone, and improve the accuracy and reliability of the navigation and positioning system.

[0048] like Figure 1 As shown in the figure, it is a structural diagram of the combined navigation system of the related technology. The GNSS / INS combined navigation system needs to fuse the navigation data obtained by each navigation subsystem to obtain the optimal estimation value under the combined navigation system. Therefore, a suitable data fusion method is very important to improve navigation performance. Kalman filter (KF) has been widely used in optimal state estimation. With the continuous deepening of KF research in recent years, various improved filtering algorithms based on KF have emerged in an endless stream to adapt to different system estimates. For example, the classic KF is only applicable to linear systems, and its state transfer equation and observation equation must be linear. For the state estimation of nonlinear systems, the extended Kalman filter (EKF) is required.

[0049] Combine the following Figure 2-Figure 4The positioning method of the positioning system according to the embodiment of the present invention is described by way of example.

[0050] like Figure 2 As shown, the positioning method of the positioning system of the embodiment of the present invention at least includes steps S1 to S3.

[0051] Step S1, obtaining first data output by a first navigation and positioning system and second data output by a second navigation and positioning system.

[0052] Among them, the navigation and positioning system includes a satellite navigation system, an inertial navigation system, and an astronomical navigation system, etc. The first data is the observation data required for the first navigation and positioning system to perform navigation and positioning, and the second data is the observation data required for the second navigation and positioning system to perform navigation and positioning. By making full use of the navigation and positioning advantages of the first navigation and positioning system and the second navigation and positioning system, the first data and the second data are obtained to provide data support for the combined navigation system to perform navigation and positioning.

[0053] In an embodiment, after the combined navigation system is turned on, the original navigation information of the first navigation and positioning system is obtained, including observation value files, navigation telegram files and meteorological data files, wherein the observation value files are the main data source required for navigation and positioning; each file contains different types of data, and after obtaining the output of the first navigation and positioning system receiver, including but not limited to the Receiver Independent Exchange Format (RINEX) file, the National Marine Electronics Association (NMEA) file, and the Radio Technology Committee of Marine (RTCM) file, the original navigation information type required for navigation and positioning and the corresponding data therein are extracted according to the measurement purpose, and the above data is determined as the first data.

[0054] The second navigation and positioning system uses inertial devices such as accelerometers and gyroscopes to measure the reference direction and initial position information of the moving target, determine the acceleration of the moving target in the inertial reference system, integrate the acceleration over time, and transform it into the navigation coordinate system. It can then obtain information such as the speed, yaw angle, and position in the navigation coordinate system to determine the trajectory information of the moving target, and determine the above detection information as the second data.

[0055] Among them, the first data and the second data can be navigation parameters directly obtained through measurement and calculation, such as position, speed, etc., or the corresponding errors, such as position error, speed error, etc., can be calculated based on the navigation parameters between the first navigation and positioning system and the second navigation and positioning system.

[0056] Step S2: Eliminate abnormal data in the first data and / or the second data to determine target data.

[0057] In an embodiment, Figure 3 As shown, after obtaining the first data, abnormal data in the first data is detected according to an abnormal data detection method, and / or after obtaining the second data, abnormal data in the first data is detected according to an abnormal data detection method, the abnormal data detection method includes machine learning-based detection, data distribution-based detection, distance and density-based detection, etc., and the detected abnormal data is eliminated, and the remaining positioning data after elimination is normal positioning data, and the normal positioning data is determined as target data.

[0058] It is understandable that, with the advancement of technology, the updating and iteration of the abnormal data detection method does not affect the implementation of the present invention. The above method is also applicable to other original navigation information, extraction of other data types and other methods for detecting abnormal data. The above method can effectively detect abnormal data in the first data, and by eliminating abnormal data, it is avoided that it is mixed into normal data and affects the data fusion of subsequent navigation data, thereby improving the accuracy of the combined navigation system.

[0059] Step S3, determining the positioning result according to the target data.

[0060] In an embodiment, after obtaining the target data, the difference between the target data and the first data is calculated, or the difference between the target data and the second data is calculated, and the size relationship between the difference and the preset difference is compared to determine the positioning result of the positioning system and reduce interference with subsequent navigation positioning.

[0061] According to the positioning method of the positioning system of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system, so as to eliminate the abnormal data in the first data and / or the second data and obtain the target data, and the positioning result of the positioning system is determined according to the screened target data, and the abnormal data caused by the external environment interference of the first navigation and positioning system and the second navigation and positioning system are effectively eliminated by preprocessing the first data and / or the second data, so as to avoid the abnormal data from being mixed into the normal data, and improve the accuracy of subsequent navigation and positioning.

[0062] The following is a supplementary explanation by taking the first navigation and positioning system as a GNSS subsystem and the second navigation and positioning system as an INS subsystem as an example.

[0063] In some embodiments, eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the first data; and eliminating abnormal positioning data in the positioning data in the first data to determine the target data.

[0064] In the embodiment, since the present invention processes the data type of navigation positioning, it is sufficient to obtain the data related to navigation positioning in the first data. The following is an example in which the first data adopts an observation value file and the extracted data type is observation data. After extracting the positioning data, namely the carrier phase and pseudorange, and the positioning auxiliary data, namely the Doppler and signal strength, in the observation data, the abnormal positioning data in the positioning data is detected according to the abnormal data detection method. The abnormal data detection method includes detection based on machine learning, detection based on data distribution, detection based on distance and density, etc., and the detected abnormal positioning data is eliminated. The remaining positioning data after elimination is the first normal positioning data. It is determined whether the first normal positioning data and the second data meet the preset conditions of data fusion. If the preset conditions of data fusion are not met, it is considered that the GNSS subsystem can work normally, while the INS subsystem cannot work normally, and the first normal positioning data is determined as the target data. The above method can effectively detect the abnormal positioning data in the positioning data. By eliminating the abnormal positioning data, it is avoided that it is mixed into the normal positioning data, which affects the data fusion of subsequent navigation data, so the accuracy of the combined navigation positioning can be improved.

[0065] Similarly, the first data may also use the navigation message data corresponding to the navigation message file, and after eliminating the abnormal navigation message data therein, obtain the corresponding target data; or the first data may also use the meteorological data corresponding to the meteorological data file, and after eliminating the abnormal meteorological data therein, obtain the corresponding target data.

[0066] In some embodiments, eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the first data; eliminating abnormal positioning data in the positioning data in the first data to obtain first target data; and determining the target data based on the first target data and the second data.

[0067] In an embodiment, after obtaining the positioning data in the first data and eliminating the abnormal positioning data in the positioning data, the positioning data remaining after the elimination is the first normal positioning data, which is determined as the first target data, and it is judged whether the first target data and the second data meet the preset conditions of data fusion. If the preset conditions of data fusion are met, it is considered that the GNSS subsystem and the INS subsystem can work normally, then the first target data and the second data are fused, and the fused data is determined as the target data.

[0068] In some embodiments, eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the second data; and eliminating abnormal positioning data in the positioning data in the second data to determine the target data.

[0069] In an embodiment, the abnormal data detection method of the second data is similar to that of the first data. After obtaining the positioning data in the second data, the abnormal positioning data in the positioning data is detected according to the abnormal data detection method, and the detected abnormal positioning data is eliminated. The remaining positioning data after elimination is the second normal positioning data, and it is judged whether the second normal positioning data and the first data meet the preset conditions of data fusion. If the preset conditions of data fusion are not met, it is considered that the GNSS subsystem cannot work normally, while the INS subsystem can work normally, and the second normal positioning data is determined as the target data. The above method can effectively detect abnormal positioning data in the positioning data, and by eliminating the abnormal positioning data, it is avoided that it is mixed into the normal positioning data and affects the data fusion of subsequent navigation data, so the accuracy of combined navigation positioning can be improved.

[0070] If the preset conditions for data fusion are met and it is considered that the GNSS subsystem and the INS subsystem can operate normally, the second normal positioning data and the first data are fused, and the fused data is determined as the target data.

[0071] In some embodiments, eliminating abnormal data in the first data and / or the second data to determine the target data includes: acquiring positioning data in the first data and positioning data in the second data; correspondingly eliminating abnormal positioning data in the positioning data in the first data and abnormal positioning data in the positioning data in the second data to obtain the first target data and the second target data; determining the target data based on the first target data and the second data.

[0072] In an embodiment, after obtaining the positioning data in the first data and eliminating the abnormal positioning data in the positioning data in the first data, the positioning data remaining after the elimination is the first normal positioning data, which is determined as the first target data; similarly, after obtaining the positioning data in the second data and eliminating the abnormal positioning data in the positioning data in the second data, the positioning data remaining after the elimination is the second normal positioning data, which is determined as the second target data, and it is determined whether the first target data and the second target data meet the preset conditions of data fusion. If the preset conditions of data fusion are met, it is considered that the GNSS subsystem and the INS subsystem can work normally, then the first target data and the second target data are data fused, and the fused data is determined as the target data.

[0073] In some embodiments, eliminating abnormal positioning data in the positioning data includes: determining a first boundary average value and a second boundary average value of the positioning data; and eliminating the abnormal positioning data in the positioning data according to the first boundary average value and the second boundary average value.

[0074] In an embodiment, after obtaining the positioning data, taking the abnormal data detection method as the 3σ statistical distribution algorithm as an example, the first boundary average value and the second boundary average value of the positioning data are determined. For example, the left and right boundaries of the positioning data are the average value ±3σ, and all imported positioning data are detected according to the first boundary average value -3σ and the second boundary average value +3σ. The positioning data outside the range set by the first boundary average value and the second boundary average value is used as abnormal positioning data, and the remaining positioning data after eliminating the abnormal positioning data is used as target data. The above method can effectively detect abnormal positioning data in the positioning data. By eliminating the abnormal positioning data, it is avoided that it is mixed into the normal positioning data and affects the data fusion of subsequent navigation data, so the accuracy of combined navigation positioning can be improved.

[0075] In some embodiments, eliminating abnormal positioning data in the positioning data according to the first boundary average value and the second boundary average value includes: determining a valid area according to the first boundary average value and the second boundary average value; and eliminating abnormal positioning data outside the valid area.

[0076] In the embodiment, after determining the first boundary average value -3σ and the second boundary average value +3σ, the first boundary average value -3σ is used as the effective lower limit value, and the second boundary average value +3σ is used as the upper limit value of the effective area to determine the effective area, i.e. [-3σ, +3σ]. The positioning data within the effective area [-3σ, +3σ] is considered to be normal positioning data, and the positioning data outside the effective area [-3σ, +3σ] is considered to be abnormal positioning data. The abnormal positioning data outside the effective area [-3σ, +3σ] is then eliminated, and the positioning data within the effective area [-3σ, +3σ] is used as the target data to screen out abnormal data that does not meet the preset effective area.

[0077] In some embodiments, eliminating abnormal positioning data in the positioning data includes: determining an initial cluster center based on a preset type of positioning data; determining positioning data to be classified of the positioning data; cyclically determining abnormal positioning data in the positioning data to be classified based on the initial cluster center and the positioning data to be classified, and eliminating the abnormal positioning data.

[0078] In an embodiment, after obtaining the positioning data, taking the K-means clustering algorithm as an abnormal data detection method, positioning data of a preset category, such as category k, is randomly selected as the initial clustering center, and the remaining positioning data is determined as the positioning data to be classified, and the positioning data to be classified is detected cyclically according to the distance between the initial clustering center and the positioning data to be classified, and the positioning data to be classified that is not classified into any initial clustering center is determined as abnormal positioning data, and the remaining positioning data to be classified after the abnormal positioning data is eliminated is used as target data. The above method can effectively detect abnormal positioning data in the positioning data, and by eliminating the abnormal positioning data, it is avoided that it is mixed into the normal positioning data and affects the data fusion of subsequent navigation data, so the accuracy of combined navigation positioning can be improved.

[0079] In some embodiments, abnormal positioning data in the positioning data to be classified is determined cyclically based on the initial cluster center and the positioning data to be classified, and the abnormal positioning data is eliminated, including: obtaining the distance between the positioning data to be classified and the initial cluster center; dividing the positioning data to be classified into a set cluster with the smallest distance from the initial cluster center; determining the mean of the positioning data to be classified in the set cluster; taking the mean as the target cluster center of the set cluster; and when the target cluster center remains unchanged, eliminating the abnormal positioning data outside the set cluster.

[0080] In an embodiment, after determining the initial cluster center of the positioning data and the positioning data to be classified, the distance between the positioning data to be classified and the initial cluster center is obtained, and the positioning data to be classified is divided into a set cluster with the smallest distance from the initial cluster center according to the distance. For each set cluster, the mean of the positioning data to be classified in the set cluster is calculated, and the mean is used as the target cluster center of the set cluster. The above steps are repeated until the target cluster center no longer changes. When the target cluster center remains unchanged, the positioning data to be classified that is not classified into any set cluster is determined as abnormal positioning data, and the positioning data to be classified in the set cluster after the abnormal positioning data is eliminated is used as target data to screen out abnormal data that does not meet the set cluster range.

[0081] In some embodiments, eliminating abnormal positioning data in the positioning data includes: obtaining an average value of distances from the positioning data to adjacent positioning data; determining a density-delineated area based on the positioning data and the average value of the distances; obtaining a density value of the density-delineated area; and filtering the abnormal positioning data in the positioning data based on the density value, and eliminating the abnormal positioning data in the positioning data.

[0082] In an embodiment, after obtaining the positioning data, taking the DBSCAN clustering algorithm as an example of an abnormal data detection method, the distance from each positioning data to the adjacent positioning data is obtained and the average is taken to obtain the distance average value, and the preset range of the positioning data is determined according to the positioning data and its distance average value, and the preset range is used as the density demarcation area, the positioning data within the density demarcation area is determined, and the quantity value of the positioning data is obtained, and the quantity value is used as the density value of the demarcated area.

[0083] After obtaining the density value, the positioning data is detected according to the size relationship between the density value and the preset density threshold to determine the positioning data of different densities. Whether the positioning data is abnormal is judged according to the overlap of the positioning data of different densities to determine the abnormal positioning data, and the remaining positioning data after eliminating the abnormal positioning data is used as the target data. According to the prior knowledge of the data set, the empirical value is taken as the preset density threshold. The above method can effectively detect the abnormal positioning data in the positioning data. By eliminating the abnormal positioning data, it is avoided that it is mixed into the normal positioning data and affects the data fusion of the subsequent navigation data. Therefore, the accuracy of the combined navigation positioning can be improved.

[0084] In some embodiments, determining the density demarcation area according to the positioning data and the average distance includes: taking the positioning data as the center of the circle and the average distance as the radius; and determining the density demarcation area according to the center of the circle and the radius.

[0085] In the embodiment, after obtaining the average distance between the positioning data and the adjacent positioning data, the positioning data is used as the center of the circle and the average distance is used as the radius. A circle is drawn according to the center and the radius, and the area contained in the circle is the density demarcation area.

[0086] In some embodiments, screening abnormal positioning data in the positioning data according to the density value includes: obtaining high-density points and low-density points in the positioning data according to the density value and the positioning data; and determining the abnormal positioning data according to the high-density points and the low-density points.

[0087] In an embodiment, after obtaining the density value, the point where the positioning data with a density value greater than or equal to the preset density threshold is located is regarded as a high-density point, and the point where the positioning data with a density value less than the preset density threshold is located is regarded as a low-density point; based on the overlap of the high-density point and the low-density point, it is judged whether the positioning data is abnormal to determine the abnormal positioning data.

[0088] In some embodiments, abnormal location data is determined based on high-density points and low-density points, including: if high-density points and / or low-density points overlap between density-divided areas, the high-density points and / or low-density points are connected to obtain a screening cluster; and points outside the screening cluster are used as abnormal location data.

[0089] In an embodiment, after obtaining high-density points and low-density points in the positioning data, if there are overlapped high-density points and / or low-density points between the density division areas, for example, a high-density point is within the density division area of ​​another high-density point, then one high-density point and another high-density point are connected, and so on, and the remaining high-density points and / or low-density points can be continuously connected in series; similarly, when a low-density point is also within the density division area of ​​a high-density point, a low-density point and the nearest high-density point are also connected, and the density point is called a boundary point, and all the above-mentioned density points that can be connected are used as screening clusters, and points outside the screening clusters, such as low-density points that are not within the density division area of ​​any high-density point, are used as abnormal positioning data to screen out abnormal data that does not meet the preset density.

[0090] The above methods are only three methods for detecting abnormal data. In addition, the present invention does not limit other methods for detecting abnormal data.

[0091] Taking the K-means clustering algorithm as an example of an abnormal data detection method, as shown in Tables 1 and 2, the positioning results before and after the abnormal data detection are simulated, after the abnormal data in the first data is screened and removed, the errors of the latitude, longitude and altitude of the positioning results are reduced.

[0092] Table 1

[0093]

[0094] Table 2

[0095]

[0096] In some embodiments, determining the positioning result according to the target data includes: determining a data difference between the target data and the first data, or determining a data difference between the target data and the second data; and determining the positioning result according to the data difference.

[0097] In an embodiment, after obtaining the target data, the difference between the target data and the first data is calculated and the absolute value is taken to obtain the data difference, or the difference between the target data and the second data is calculated and the absolute value is taken to obtain the data difference, and the GNSS / INS combined navigation system calculates the deviation of the navigation data of a known position to obtain a preset deviation threshold, and determines whether the GNSS subsystem is working normally based on the size relationship between the data difference and the preset deviation threshold, thereby controlling whether the GNSS subsystem participates in the data fusion of this group, and determining the positioning result based on the navigation data after data fusion.

[0098] In some embodiments, determining the positioning result based on the data difference includes: if the data difference is greater than a preset deviation threshold, removing the target data; determining the positioning result based on the first data, or determining the positioning result based on the second data.

[0099] In an embodiment, after calculating the data difference, it is determined whether the data difference is greater than a preset deviation threshold. If the data difference is greater than the preset deviation threshold, it is considered that the preset conditions for data fusion are not met. The INS subsystem may be unable to work normally and may be blocked by the environment, resulting in signal loss. At this time, the first data and the second data obtained by the two navigation subsystems cannot be fused. Considering that the data update of the extended Kalman filter requires the allocation of weights for the predicted value and the observed value, which is equivalent to the predicted value weight being 0 and the observed value weight being 1, the INS subsystem in the GNSS / INS combined navigation system is decoupled to avoid the impact of the unlocked INS subsystem on the algorithm performance of the combined navigation system, and to achieve the removal of target data. The final positioning result of the GNSS / INS combined navigation system can be determined based on the first data output by the GNSS subsystem, thereby reducing the error rate of data fusion of the GNSS / INS combined navigation system and improving positioning accuracy.

[0100] Or it may be that the GNSS subsystem is no longer working properly, and may be blocked by the environment and cause signal loss. At this time, it is impossible to perform data fusion on the first data and the second data obtained by the two navigation subsystems. Considering that the data update of the extended Kalman filter needs to assign weights to the predicted value and the observed value, which is equivalent to the predicted value weight being 1 and the observed value weight being 0, the GNSS subsystems in the GNSS / INS integrated navigation system are decoupled to avoid the impact of the unlocked GNSS subsystem on the algorithm performance of the integrated navigation system, and to achieve the removal of target data. The final positioning result of the GNSS / INS integrated navigation system can be determined based on the second data output by the INS, thereby reducing the error rate of data fusion of the GNSS / INS integrated navigation system and improving positioning accuracy.

[0101] In some embodiments, determining the positioning result based on the data difference includes: if the data difference is less than or equal to a preset deviation threshold, determining the first weight of the target data and the second weight of the first data, or determining the first weight of the target data and the second weight of the second data; weighting the target data and the first data according to the first weight and the second weight, or weighting the target data and the second data according to the first weight and the second weight to determine the positioning result.

[0102] In an embodiment, if the data difference is less than or equal to a preset deviation threshold, it is considered that the preset conditions for data fusion are met, and the INS subsystem may be able to work normally. Considering that the data update of the extended Kalman filter requires assigning weights to the predicted value and the observed value, the first weight of the target data and the second weight of the first data are determined, the target data is weighted according to the first weight, the first data is weighted according to the second weight, and the positioning result is jointly determined based on the weighted target data and the first data.

[0103] Or it may be that the GNSS subsystem can work normally, then determine the first weight of the target data and the second weight of the second data, weight the target data according to the first weight, weight the second data according to the second weight, and jointly determine the positioning result based on the weighted target data and the second data, thereby reducing the error rate of data fusion of the GNSS / INS combined navigation system and improving positioning accuracy.

[0104] Reference below Figure 4 The positioning method of the positioning system according to the embodiment of the present invention is described by way of example.

[0105] like Figure 4 As shown, the positioning method of the positioning system of the embodiment of the present invention at least includes steps S11 to S30.

[0106] Step S11, acquiring first data output by the first navigation and positioning system and second data output by the second navigation and positioning system.

[0107] Step S12, obtaining positioning data in the first data; eliminating abnormal positioning data in the positioning data in the first data to determine target data.

[0108] Step S13, obtaining positioning data in the first data; eliminating abnormal positioning data in the positioning data in the first data to obtain first target data; and determining the target data according to the first target data and the second data.

[0109] Step S14, obtaining positioning data in the second data; eliminating abnormal positioning data in the positioning data in the second data to determine target data.

[0110] Step S15, obtaining the positioning data in the first data and the positioning data in the second data; correspondingly eliminating the abnormal positioning data in the positioning data in the first data and the abnormal positioning data in the positioning data in the second data to obtain the first target data and the second target data; determining the target data according to the first target data and the second target data.

[0111] Step S16, determining the first boundary average value and the second boundary average value of the positioning data, taking the first boundary average value as the effective lower limit value, and taking the second boundary average value as the effective area upper limit value, so as to determine the effective area and eliminate abnormal positioning data outside the effective area.

[0112] Step S17, determining an initial cluster center according to the preset types of positioning data; and determining the positioning data to be classified.

[0113] Step S18, obtaining the distance between the positioning data to be classified and the initial cluster center; dividing the positioning data to be classified into the set cluster with the smallest distance from the initial cluster center; determining the mean of the positioning data to be classified in the set cluster; taking the mean as the target cluster center of the set cluster; when the target cluster center remains unchanged, eliminating abnormal positioning data outside the set cluster.

[0114] Step S19, obtaining the average distance between the positioning data and the adjacent positioning data, taking the positioning data as the center of the circle and the average distance as the radius; and determining the density demarcation area according to the center of the circle and the radius.

[0115] Step S20: taking the quantity of the positioning data in the density defined area as the density value of the defined area.

[0116] Step S21, determine whether the density value is greater than or equal to a preset density threshold, if so, execute step S22; otherwise, execute step S23.

[0117] Step S22: taking points whose density values ​​are greater than or equal to a preset density threshold as high-density points.

[0118] Step S23: taking points whose density values ​​are less than a preset density threshold as low-density points.

[0119] Step S24, determining whether there are overlaps of high-density points and / or low-density points between density-divided areas, if so, executing step S25; otherwise, executing step S20.

[0120] Step S25, connecting high-density points and / or low-density points to obtain a screening cluster; and using points outside the screening cluster as abnormal location data.

[0121] Step S26, eliminating abnormal positioning data in the positioning data.

[0122] Step S27, determining a data difference between the target data and the first data, or determining a data difference between the target data and the second data.

[0123] Step S28, determine whether the data difference is greater than a preset deviation threshold, if so, execute step S29; otherwise, execute step S30.

[0124] Step S29, remove the target data; determine the positioning result according to the first data, or determine the positioning result according to the second data.

[0125] Step S30, determine the first weight of the target data and the second weight of the first data, or determine the first weight of the target data and the second weight of the second data; weight the target data and the first data according to the first weight and the second weight, or weight the target data and the second data according to the first weight and the second weight, to determine the positioning result.

[0126] According to the positioning method of the positioning system of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system, so as to eliminate the abnormal data in the first data and / or the second data and obtain the target data, and the positioning result of the positioning system is determined according to the screened target data, and the abnormal data caused by the external environment interference of the first navigation and positioning system and the second navigation and positioning system are effectively eliminated by preprocessing the first data and / or the second data, so as to avoid the abnormal data from being mixed into the normal data, and improve the accuracy of subsequent navigation and positioning.

[0127] Combine the following Figure 5 The positioning device 2 of the positioning system according to the embodiment of the present invention is described.

[0128] like Figure 5 As shown, the positioning device 2 of the positioning system of the embodiment of the present invention includes: an acquisition module 21, a rejection module 22 and a determination module 23, wherein:

[0129] The acquisition module 21 is used to acquire the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system; the elimination module 22 is used to eliminate abnormal data in the first data and / or the second data to determine the target data; the determination module 23 is used to determine the positioning result according to the target data.

[0130] According to the positioning device 2 of the positioning system of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system, so as to eliminate the abnormal data in the first data and / or the second data and obtain the target data, and the positioning result of the positioning system is determined according to the screened target data, and the abnormal data caused by the external environment interference of the first navigation and positioning system and the second navigation and positioning system are effectively eliminated by preprocessing the first data and / or the second data, so as to avoid the abnormal data from being mixed into the normal data, and improve the accuracy of subsequent navigation and positioning.

[0131] The following describes a computer-readable storage medium according to an embodiment of the present invention.

[0132] The computer-readable storage medium of the embodiment of the present invention stores a positioning program of the positioning system, and when the positioning program of the positioning system is executed by the processor, the positioning method of the positioning system of the above embodiment is implemented.

[0133] The following describes a computer program product according to an embodiment of the present invention.

[0134] The computer program product of the embodiment of the present invention includes a computer program, and when the computer program is executed by a processor, the positioning method of the positioning system of the above embodiment is implemented.

[0135] According to the computer program product of the embodiment of the present invention, abnormal data detection is performed on the acquired first data and / or second data through the first data output by the first navigation and positioning system and the second data output by the second navigation and positioning system to eliminate abnormal data in the first data and / or the second data to obtain target data, and the positioning result of the positioning system is determined according to the screened target data. By preprocessing the first data and / or the second data, abnormal data caused by external environmental interference in the first navigation and positioning system and the second navigation and positioning system are effectively eliminated to avoid abnormal data from being mixed into normal data, thereby improving the accuracy of subsequent navigation and positioning.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0137] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A positioning method of a positioning system, characterized in that: include: Acquire first data output by the first navigation and positioning system and second data output by the second navigation and positioning system; Eliminating abnormal data in the first data and / or the second data to determine target data includes: acquiring positioning data in the first data; eliminating abnormal positioning data in the positioning data in the first data to obtain first target data; determining the target data according to the first target data and the second data, Alternatively, obtaining the positioning data in the first data and the positioning data in the second data; correspondingly eliminating abnormal positioning data in the positioning data in the first data and abnormal positioning data in the positioning data in the second data to obtain first target data and second target data; determining the target data according to the first target data and the second target data; Determine a data difference between the target data and the first data, or determine a data difference between the target data and the second data, wherein determining the data difference between the target data and the first data comprises: calculating a difference between the target data and the first data and taking an absolute value to obtain a data difference, and determining the data difference between the target data and the second data comprises: calculating a difference between the target data and the second data and taking an absolute value to obtain a data difference; Determining a positioning result according to the target data includes: if the data difference is greater than a preset deviation threshold, removing the target data; determining a positioning result according to the first data or the second data; If the data difference is less than or equal to a preset deviation threshold, determine the first weight of the target data and the second weight of the first data or the second data; weight the target data and the first data or the second data according to the first weight and the second weight correspondence to determine the positioning result.

2. The positioning method of the positioning system according to claim 1, characterized in that: The removing of abnormal positioning data from the positioning data comprises: Determine a first boundary average value and a second boundary average value of the positioning data; Abnormal positioning data in the positioning data is eliminated according to the first boundary average value and the second boundary average value.

3. The positioning method of the positioning system according to claim 2, characterized in that: The removing abnormal positioning data from the positioning data according to the first boundary average value and the second boundary average value comprises: Determine a valid area according to the first boundary average value and the second boundary average value; Abnormal positioning data outside the effective area is eliminated.

4. The positioning method of the positioning system according to claim 3, characterized in that: The determining of the effective area according to the first boundary average value and the second boundary average value comprises: The first boundary average value is used as the effective lower limit value, and the second boundary average value is used as the effective area upper limit value to determine the effective area.

5. The positioning method of the positioning system according to claim 1, characterized in that: The removing of abnormal positioning data from the positioning data comprises: Determining an initial cluster center according to the positioning data of a preset type; Determining the positioning data to be classified; Abnormal positioning data in the positioning data to be classified is determined cyclically according to the initial cluster center and the positioning data to be classified, and the abnormal positioning data is eliminated.

6. The positioning method of the positioning system according to claim 5, characterized in that: The loop determines abnormal positioning data in the positioning data to be classified according to the initial cluster center and the positioning data to be classified, and removes the abnormal positioning data, including: Obtaining the distance between the to-be-classified positioning data and the initial cluster center; Dividing the to-be-classified positioning data into a set cluster with the smallest distance from the initial cluster center; Determine the mean value of the to-be-classified positioning data in the set cluster; Taking the mean as the target cluster center of the set cluster; When the center of the target cluster remains unchanged, the abnormal positioning data outside the set cluster is eliminated.

7. The positioning method of the positioning system according to claim 1, characterized in that: The removing of abnormal positioning data from the positioning data comprises: Obtaining an average value of distances from the positioning data to adjacent positioning data; Determine a density demarcation area based on the positioning data and the distance average value; Obtaining the density value of the density defined area; Abnormal positioning data in the positioning data is screened according to the density value, and the abnormal positioning data in the positioning data is eliminated.

8. The positioning method of the positioning system according to claim 7, characterized in that: The step of determining a density demarcation area according to the positioning data and the distance average value includes: Taking the positioning data as the center of the circle and the average distance as the radius; The density demarcation area is determined according to the center of the circle and the radius.

9. The positioning method of the positioning system according to claim 7, characterized in that: The obtaining of the density value of the density defined area includes: The quantity of the positioning data in the density defined area is used as the density value of the defined area.

10. The positioning method of the positioning system according to claim 7, characterized in that: The step of screening the abnormal positioning data in the positioning data according to the density value comprises: Acquire high-density points and low-density points in the positioning data according to the density value and the positioning data; The abnormality positioning data is determined according to the high-density points and the low-density points.

11. The positioning method of the positioning system according to claim 10, characterized in that: The obtaining of high-density points in the positioning data according to the density value and the positioning data includes: The points whose density values ​​are greater than or equal to a preset density threshold are regarded as the high-density points.

12. The positioning method of the positioning system according to claim 10, characterized in that: The obtaining of low-density points in the positioning data according to the density value and the positioning data includes: The points whose density values ​​are less than a preset density threshold are regarded as the low-density points.

13. The positioning method of the positioning system according to claim 10, characterized in that: The determining the abnormality positioning data according to the high-density points and the low-density points includes: If high-density points and / or low-density points overlap between the density-delimited areas, the high-density points and / or the low-density points are connected to obtain a screening cluster; The points outside the screening cluster are used as the abnormality positioning data.

14. A positioning device of a positioning system, characterized in that: include: An acquisition module, used to acquire first data output by the first navigation and positioning system and second data output by the second navigation and positioning system; a removal module, used for removing abnormal data in the first data and / or the second data to determine target data, comprising: obtaining positioning data in the first data; removing abnormal positioning data in the positioning data in the first data to obtain first target data; determining the target data according to the first target data and the second data, Alternatively, obtaining the positioning data in the first data and the positioning data in the second data; correspondingly eliminating abnormal positioning data in the positioning data in the first data and abnormal positioning data in the positioning data in the second data to obtain first target data and second target data; determining the target data according to the first target data and the second target data; a determination module, configured to determine a data difference between the target data and the first data, or determine a data difference between the target data and the second data, wherein determining the data difference between the target data and the first data comprises: calculating a difference between the target data and the first data and taking an absolute value to obtain a data difference, and determining the data difference between the target data and the second data comprises: calculating a difference between the target data and the second data and taking an absolute value to obtain a data difference; Determining a positioning result according to the target data includes: if the data difference is greater than a preset deviation threshold, removing the target data; determining a positioning result according to the first data or the second data; If the data difference is less than or equal to a preset deviation threshold, determine the first weight of the target data and the second weight of the first data or the second data; weight the target data and the first data or the second data according to the first weight and the second weight correspondence to determine the positioning result.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a positioning program of the positioning system, and when the positioning program of the positioning system is executed by the processor, the positioning method of the positioning system according to any one of claims 1 to 13 is implemented.

16. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the positioning method of the positioning system according to any one of claims 1 to 13 is implemented.

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