CN-122017914-A - Abnormal positioning data detection method and system based on mileage reckoning

CN122017914ACN 122017914 ACN122017914 ACN 122017914ACN-122017914-A

Abstract

The invention discloses a method and a system for detecting abnormal positioning data based on mileage calculation, wherein the method comprises the steps of obtaining satellite positioning data and multidimensional milemeter observation data of a target carrier in each epoch, obtaining milemeter environment error data, carrier attitude estimation error, point cloud position estimation error and milemeter estimation error of the target carrier in each epoch according to carrier position data and point cloud characteristic position data, obtaining a position error confidence threshold of the target carrier in each epoch by combining with a pre-obtained milemeter inherent ranging error, and finally obtaining position deviation of the mileage positioning data and the satellite positioning data of each epoch, so as to obtain an abnormal data detection result of the satellite positioning data according to the position deviation and the position error confidence threshold, and improving the accuracy of abnormal detection.

Inventors

YANG HONGZHEN
ZHANG YUNFENG
ZHANG YEHUA
CHENG LUMING
HE CHEN
SONG WEIWEI
GUO WENFEI
LV ZHOU
LI ZHONGXIU
DU YIZHOU
LIAO HAILIN
FAN MINGXIA

Assignees

国网浙江省电力有限公司信息通信分公司

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. The abnormal positioning data detection method based on mileage reckoning is characterized by comprising the following steps of: Acquiring satellite positioning data of a target carrier in each epoch and multidimensional odometer observation data, wherein the multidimensional odometer observation data comprises odometer positioning data, carrier position data, point cloud characteristic position data and a posterior estimation error covariance matrix; acquiring odometer environment error data of the target carrier in each epoch according to the carrier position data and the point cloud characteristic position data; acquiring a carrier attitude estimation error of the target carrier in each epoch according to the carrier position data and the posterior estimation error covariance matrix respectively corresponding to two adjacent epochs; acquiring a point cloud position estimation error of the target carrier in each epoch according to the carrier attitude estimation error and the point cloud characteristic position data corresponding to two adjacent epochs respectively; Acquiring an odometer estimation error of the target carrier in each epoch according to the posterior estimation error covariance matrix; Acquiring a position error confidence threshold of the target carrier in each epoch according to the point cloud position estimation error, the odometer environment error data and the pre-acquired odometer inherent ranging error; and acquiring the position deviation of the mileage positioning data and the satellite positioning data of each epoch, so as to acquire an abnormal data detection result of the satellite positioning data according to the position deviation and the position error confidence threshold.
2. The method for detecting abnormal positioning data based on mileage calculation according to claim 1, wherein the obtaining the odometer environmental error data of the target carrier at each epoch according to the carrier position data and the point cloud feature position data includes: for any epoch, acquiring position data corresponding to each point cloud feature from the point cloud feature position data corresponding to the epoch; For any one of the point cloud features, acquiring a direction vector of the point cloud feature compared with the target carrier according to the position data corresponding to the point cloud feature and the carrier position data corresponding to the epoch; constructing a directional cosine array of the target carrier in the epoch according to a plurality of directional vectors corresponding to the point cloud characteristic position data; Acquiring a covariance matrix of the direction cosine matrix, and performing matrix inversion on the covariance matrix to obtain a weight coefficient matrix; and obtaining the sum of the addition of a plurality of matrix elements on a main diagonal in the weight coefficient matrix, and performing evolution operation on the sum of the addition to obtain the environmental error data of the odometer of the target carrier in the epoch.
3. The method for detecting abnormal positioning data based on mileage calculation according to claim 2, wherein the obtaining the carrier posture estimation error of the target carrier in each epoch according to the carrier position data and the a posteriori estimation error covariance matrix respectively corresponding to two adjacent epochs comprises: for any epoch, acquiring a history epoch adjacent to the epoch and history carrier position data of the target carrier in the history epoch; acquiring the displacement of the target carrier in the epoch according to the carrier position data and the historical carrier position data of the target carrier in the epoch; taking the product of the displacement and a pre-acquired empirical constant coefficient as a carrier attitude estimation error scaling coefficient; acquiring an original attitude error of the target carrier in the epoch from a posterior estimation error covariance matrix corresponding to the epoch, so as to scale the original attitude error into a real-time attitude error according to the carrier attitude estimation error scaling coefficient and a preset spherical interpolation method; and when judging that the real-time attitude error has a perpendicular line observation constraint according to the posterior estimation error covariance matrix of the epoch and the historical posterior estimation error covariance matrix of the historical epoch, taking the real-time attitude error as a carrier attitude estimation error of the target carrier in each epoch.
4. The method for detecting abnormal positioning data based on mileage calculation according to claim 3, wherein the acquiring the carrier posture estimation error of the target carrier in each epoch according to the carrier position data and the a posteriori estimation error covariance matrix corresponding to two adjacent epochs respectively further comprises: When the real-time attitude error is judged to have no vertical line observation constraint, acquiring a historical carrier attitude estimation error of the target carrier in the historical epoch; Taking the product of the historical carrier attitude estimation error and the real-time attitude error as the carrier attitude estimation error of the target carrier in the epoch.
5. The method for detecting abnormal positioning data based on mileage calculation according to any one of claims 2 to 4, wherein the obtaining the estimated error of the target carrier in the point cloud position of each epoch according to the estimated error of the carrier posture and the point cloud characteristic position data corresponding to two adjacent epochs respectively includes: For any epoch, acquiring historical point cloud characteristic position data of a historical epoch adjacent to the epoch and a target carrier in the historical epoch; acquiring position data of each point cloud feature from the point cloud feature position data corresponding to the epoch, and acquiring historical position data of each historical point cloud feature from the historical point cloud feature position data; Acquiring newly added point cloud features from a plurality of point cloud features according to a comparison result of the historical position data and the position data; Acquiring a direction vector corresponding to the newly added point cloud feature and a historical direction vector corresponding to each historical point cloud feature respectively, so as to match a target historical point cloud feature corresponding to the newly added point cloud feature from a plurality of historical point cloud features according to an included angle between the direction vector and the historical direction vector; Acquiring a displacement vector between the newly added point cloud feature and the target historical point cloud feature according to the position data corresponding to the newly added point cloud feature and the historical position data corresponding to the target historical point cloud feature; acquiring real-time errors corresponding to the newly added point cloud features according to the displacement vectors and carrier attitude estimation errors corresponding to the epochs; Acquiring a historical position error confidence threshold of the target carrier in the historical epoch, and taking the sum of the historical position error confidence threshold and the real-time error as an accumulated error corresponding to the newly added point cloud characteristic; And acquiring a plurality of accumulated errors of the epoch, and taking the accumulated errors as point cloud position estimation errors of the target carrier in the epoch.
6. The method for detecting abnormal positioning data based on mileage calculation according to claim 1, wherein the obtaining the mileage estimation error of the target carrier at each epoch according to the a posteriori estimation error covariance matrix includes: For any epoch, acquiring a position estimation variance of the target carrier in each coordinate axis direction and a square root of each position estimation variance from the posterior estimation error covariance matrix corresponding to the epoch; combining a plurality of the square roots into a multidimensional vector to take the multidimensional vector as an odometer estimation error of the target carrier in the epoch.
7. The method for detecting abnormal positioning data based on mileage calculation according to claim 6, wherein the inherent range error of the odometer includes radar range error and observation noise corresponding to each point cloud feature; The obtaining the position error confidence threshold of the target carrier in each epoch according to the point cloud position estimation error, the odometer environment error data and the pre-obtained odometer inherent ranging error comprises the following steps: For any epoch, acquiring a first product of odometer environment error data and radar ranging error of the epoch; Acquiring the sum of the noise addition of the observation noise of all newly added point cloud features in the epoch; for any newly added point cloud feature of the epoch, obtaining a ratio of the sum of the observed noise of the newly added point cloud feature and the noise, and obtaining a second product of the ratio and the accumulated error of the newly added point cloud feature; obtaining the sum of all the second products in the epoch to obtain the feature observation error corresponding to the epoch; And obtaining the modular length of the odometer estimation error of the epoch, so as to obtain a position error confidence threshold of the target carrier in the epoch according to the sum of the modular length, the first product and the characteristic observation error.
8. The method for detecting abnormal positioning data based on mileage calculation according to claim 1, wherein the obtaining a positional deviation between the mileage positioning data and the satellite positioning data for each epoch to obtain an abnormal data detection result of the satellite positioning data according to the positional deviation and the positional error confidence threshold value comprises: For any epoch, acquiring the position deviation of the mileage positioning data and the satellite positioning data of the epoch; And when the position deviation is larger than the position error confidence threshold, judging that the satellite positioning data corresponding to the epoch is abnormal positioning data.
9. The abnormal positioning data detection system based on mileage calculation is characterized by comprising a data acquisition module, an environment error module, a carrier error module, a point cloud error module, a mileage error module, a position error module and a data detection module; The data acquisition module is used for acquiring satellite positioning data of a target carrier in each epoch and multidimensional odometer observation data, wherein the multidimensional odometer observation data comprises odometer positioning data, carrier position data, point cloud characteristic position data and a posterior estimation error covariance matrix; the environment error module is used for acquiring the odometer environment error data of the target carrier in each epoch according to the carrier position data and the point cloud characteristic position data; The carrier error module is used for acquiring carrier attitude estimation errors of the target carrier in each epoch according to the carrier position data and the posterior estimation error covariance matrix corresponding to two adjacent epochs respectively; The point cloud error module is used for acquiring the point cloud position estimation error of the target carrier in each epoch according to the carrier attitude estimation error and the point cloud characteristic position data corresponding to two adjacent epochs respectively; the mileage error module is used for acquiring the mileage meter estimation error of the target carrier in each epoch according to the posterior estimation error covariance matrix; the position error module is used for acquiring a position error confidence threshold value of the target carrier in each epoch according to the point cloud position estimation error, the odometer environment error data and the pre-acquired odometer inherent ranging error; The data detection module is used for obtaining the position deviation of the mileage positioning data and the satellite positioning data of each epoch so as to obtain an abnormal data detection result of the satellite positioning data according to the position deviation and the position error confidence threshold.
10. The system for detecting abnormal positioning data based on mileage calculation according to claim 9, wherein the environmental error module includes a direction recognition unit and an environmental impact unit; The direction identification unit is used for acquiring position data corresponding to each point cloud feature from the point cloud feature position data corresponding to any epoch, and acquiring a direction vector of the point cloud feature compared with the target carrier according to the position data corresponding to the point cloud feature and the carrier position data corresponding to the epoch for any point cloud feature; The environment influence unit is used for constructing a direction cosine matrix of the target carrier in the epoch according to a plurality of direction vectors corresponding to the point cloud characteristic position data, obtaining a covariance matrix of the direction cosine matrix, performing matrix inversion on the covariance matrix to obtain a weight coefficient matrix, obtaining the sum of addition of a plurality of matrix elements on a main diagonal in the weight coefficient matrix, and performing an evolution operation on the sum of addition to obtain the environmental error data of the odometer of the target carrier in the epoch.

Description

Abnormal positioning data detection method and system based on mileage reckoning Technical Field The invention relates to the field of positioning, in particular to an abnormal positioning data detection method and system based on mileage reckoning. Background Currently, positioning by using a combined navigation method based on a satellite signal receiver, an odometer IMU (inertial measurement unit) and a laser radar is a mainstream scheme of a fusion positioning technology, and the scheme can realize higher positioning precision and stability in a conventional environment. However, when the satellite signal receiver is in a scenario where fraud or interference exists (such as malicious signal tampering, electromagnetic interference suppression, etc.), serious deviation or even failure of the positioning result occurs. Although the odometer IMU can provide position calculation data through sensing motion state to maintain positioning output in a short term, long-term operation is affected by error accumulation, positioning accuracy is continuously reduced, while the laser radar has environment three-dimensional feature sensing capability and is not easy to be affected by electromagnetic interference, and long-distance high-accuracy positioning is difficult to realize independently through scene feature matching auxiliary positioning. Disclosure of Invention In order to solve the technical problems, the invention discloses an abnormal positioning data detection method and system based on mileage reckoning, which are used for improving the abnormal detection accuracy of satellite positioning data. In order to achieve the above object, the present invention discloses a method for detecting abnormal positioning data based on mileage reckoning, comprising: Acquiring satellite positioning data of a target carrier in each epoch and multidimensional odometer observation data, wherein the multidimensional odometer observation data comprises odometer positioning data, carrier position data, point cloud characteristic position data and a posterior estimation error covariance matrix; acquiring odometer environment error data of the target carrier in each epoch according to the carrier position data and the point cloud characteristic position data; acquiring a carrier attitude estimation error of the target carrier in each epoch according to the carrier position data and the posterior estimation error covariance matrix respectively corresponding to two adjacent epochs; acquiring a point cloud position estimation error of the target carrier in each epoch according to the carrier attitude estimation error and the point cloud characteristic position data corresponding to two adjacent epochs respectively; Acquiring an odometer estimation error of the target carrier in each epoch according to the posterior estimation error covariance matrix; Acquiring a position error confidence threshold of the target carrier in each epoch according to the point cloud position estimation error, the odometer environment error data and the pre-acquired odometer inherent ranging error; and acquiring the position deviation of the mileage positioning data and the satellite positioning data of each epoch, so as to acquire an abnormal data detection result of the satellite positioning data according to the position deviation and the position error confidence threshold. The invention discloses an abnormal positioning data detection method based on mileage calculation, which comprises the steps of obtaining satellite positioning data and multidimensional mileage meter observation data of a target carrier in each epoch, laying a foundation for subsequent error quantification according to the multidimensional mileage meter observation data, obtaining mileage meter environment error data according to carrier position data and point cloud characteristic position data, directly utilizing an error matrix to simplify the mileage meter error extraction process by comparing the target position with the environment characteristic position, obtaining carrier attitude estimation errors according to carrier position data of two adjacent epochs and a posterior estimation error co-square matrix, utilizing position change and error statistical characteristics of adjacent time points to estimate attitude accumulation errors so as to solve the problem of short-term mileage drift, obtaining point cloud position estimation errors according to the carrier attitude estimation errors and point cloud characteristic position data of two adjacent epochs, combining the attitude errors and the environment characteristic dynamic change, improving the accuracy of point cloud matching, reducing the environment interference, obtaining mileage meter estimation errors according to the posterior estimation error co-square matrix, directly utilizing the error matrix to extract the mileage meter error, obtaining the carrier attitude estimation errors according to the carrier attitude