Patent Description:
<CIT> discloses motion sensors of a mobile device mounted to a vehicle. The motion sensors of the mobile device are used to detect a mount angle of the mobile device. The motion sensors are used to determine whether the vehicle is accelerating or de-accelerating, whether the vehicle is turning and whether the mount angle of the mobile device is rotating. The mount angle of the mobile device is obtained from data output from the motion sensors and can be used to correct a compass heading. Data from the motion sensors that are obtained while the vehicle is turning or the mobile device is rotating are not used to obtain the mount angle.

In electronically-driven array antennas, when a driving device (e.g., a motor device) fails and an attitude of the array antenna cannot be changed, the array antenna must obtain relevant parameters through a heading and attitude reference system, so as to change a direction of a beam from the array antenna to communicate with a satellite in different attitudes.

However, an axial sensor of a conventional heading and attitude reference system is susceptible to environmental interference during operation, resulting in inaccurate measurement values. When the values measured by the heading and attitude reference system are inaccurate, an efficiency of the antenna beam tracking of the array antenna will be greatly reduced.

In response to the above-referenced technical inadequacy, the present disclosure provides a heading and attitude correction method and a heading and attitude correction system according to independent claims <NUM> and <NUM>, respectively.

In one aspect, the present disclosure provides a heading and attitude correction method. The method includes: obtaining a plurality of attitude data in any one of axial directions in a period of time from an axial sensor; performing a linear regression analysis on the attitude data and a plurality of time points in the time period to obtain a regression line and a standard deviation; obtaining a deviation value between the attitude data and the regression line at each of the time points; excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation; grouping the attitude data according to a grouping value to form a plurality of clusters, wherein the grouping value is a minimum accuracy of the axial sensor of the heading and attitude correction system; comparing a total quantity of the attitude data in each of the clusters, and defining one of the clusters with a largest total quantity as an ideal cluster; and calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction.

In another aspect, the present disclosure provides a heading and attitude correction system. The heading and attitude correction system includes an axial sensor and a calibration module. The axial sensor is configured to sense an attitude of a vehicle to generate a plurality of attitude data in any one of axial directions. The calibration module is electrically coupled to the axial sensor and executes a heading and attitude correction method. The heading and attitude correction method includes: performing a linear regression analysis on the attitude data and a plurality of time points in the time period to obtain a regression line and a standard deviation; obtaining a deviation value between the attitude data and the regression line at each of the time points; excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation; grouping the attitude data according to a grouping value to form a plurality of clusters, wherein the grouping value is a minimum accuracy of the axial sensor of the heading and attitude correction system; comparing a total quantity of the attitude data in each of the clusters, and defining one of the clusters with a largest total quantity as an ideal cluster; and calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction.

Therefore, in the heading and attitude correction method and the heading and attitude correction system provided by the present disclosure, by virtue of "excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation, and grouping the attitude data according to a grouping value to form a plurality of clusters,," and "defining one of the clusters with a largest total quantity as an ideal cluster, and calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction," the heading and attitude correction method and the heading and attitude correction system can correct parameters, thereby ensuring an efficiency of antenna beam tracking.

Referring to <FIG>, the present disclosure provides a heading and attitude correction method and a heading and attitude correction system <NUM>. The heading and attitude correction method is applicable to the heading and attitude correction system <NUM>. In the following description, each component of the heading and attitude correction system <NUM> are first introduced, and then the implementation of the correction method by each component of the heading and attitude correction system <NUM> is specified.

Referring to <FIG>, the heading and attitude correction system <NUM> includes an axial sensor <NUM> and a calibration module <NUM> that is electrically coupled to the axial sensor. The axial sensor <NUM> is configured to sense an attitude of a vehicle (not shown) to generate a plurality of attitude data in any one of axial directions. In practice, the axial sensor <NUM> can be, for example, a nine-axis electronic sensing device with a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, and the axial sensor <NUM> can be used to sense an attitude of the vehicle (e.g., a pitch angle, a roll angle, or a yaw angle), but the present disclosure is not limited thereto.

Furthermore, as shown in <FIG>, the calibration module <NUM> in the present embodiment may be an electronic device with computing functions (e.g., a CPU, an MCU, or an ECU), and the calibration module <NUM> can execute the correction method for correcting an error parameter generated by the axial sensor <NUM>. The calibration method includes a plurality of steps S101~S113. It should be noted that, any one of the above-mentioned steps can be omitted or adjusted within reason according to practical requirements.

The step S101 is implemented by obtaining a plurality of attitude data in any one of axial directions in a period of time from an axial sensor <NUM>. For example, one of the axial directions may be the pitch angle, and each of the attitude data is an attitude state at different time points.

The step S103 is implemented by performing a linear regression analysis on the attitude data and a plurality of time points in the time period to obtain a regression line and a standard deviation.

The step S105 is implemented by obtaining a deviation value between the attitude data and the regression line at each of the time points. The deviation value can be understood as the residual between the attitude data and the regression line at the same time point.

The step S107 is implemented by excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation.

The step S109 is implemented by grouping the attitude data according to a grouping value to form a plurality of clusters. The grouping value can be any value selected according to current circumstances. In practice, the grouping value may preferably be a minimum accuracy of the axial sensor <NUM>.

The step S111 is implemented by comparing a total quantity of the attitude data in each of the clusters, and defining one of the clusters with a largest total quantity as an ideal cluster.

The step S113 is implemented by calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction. That is to say, the reasonable attitude data is used to replace the attitude data currently measured by the axial sensor <NUM>.

For the convenience of description, an example will be provided below for explaining the steps S101~S113, but the present disclosure is not limited thereto.

Referring to <FIG> is a schematic diagram of a relationship between times and pitch angles, a horizontal axis of the schematic diagram is time (e.g., milliseconds), and a vertical axis of the schematic diagram is the pitch angle (i.e., the attitude data). A regression line L and a standard deviation can be obtained from the time and attitude data in <FIG> through linear regression analysis. The equation of the regression line L is y = -<NUM> × <NUM>-<NUM>x + <NUM>, and the standard deviation is about <NUM>.

The calibration module <NUM> compares (at each of the time points) a deviation value (i.e., a difference, or a residual) between the attitude data and the regression line. The correction module <NUM> excludes the attitude data whose deviation value is greater than or equal to at least twice the standard deviation. For example, at the 100th millisecond, a value of the regression line is <NUM> (degrees), and the attitude data measured by the axial sensor <NUM> is <NUM> (degrees). Therefore, the attitude data at the 100th millisecond clearly exceeds twice the standard deviation (<NUM> - <NUM> ≥ <NUM> × <NUM>) and is excluded (i.e., the data with a pitch angle of zero in <FIG>).

Then, the calibration module <NUM> groups the attitude data according to a grouping value to form a plurality of clusters. The grouping value is an example of the minimum accuracy of the axial sensor <NUM>, and the grouping value is <NUM>. The attitude data are classified into a cluster within a range from <NUM> to <NUM>, a cluster within a range from <NUM> to <NUM>, a cluster within a range from <NUM> to <NUM>, and a cluster within a range from <NUM> to <NUM>.

The calibration module <NUM> compares the total quantity of the attitude data in each of the clusters (see Table <NUM> below) and defines one of the clusters with the largest total quantity as an ideal cluster, that is, the attitude data with degrees of <NUM> to <NUM> belong to the ideal cluster. The correction module <NUM> calculates an average number (i.e., <NUM> degrees) of the attitude data in the ideal cluster as a reasonable attitude data after correction.

It should be noted that while the actual average value of the attitude data (i.e., all the attitude data) with degrees of <NUM> to <NUM> is <NUM>, the actual pitch angle of the vehicle is in fact closer to <NUM> degrees. That is to say, the heading and attitude correction method and the heading and attitude correction system in the present disclosure can indeed reduce errors effectively.

In conclusion, in the heading and attitude correction method and the heading and attitude correction system provided by the present disclosure, by virtue of "excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation, and grouping the attitude data according to a grouping value to form a plurality of clusters,," and "defining one of the clusters with a largest total quantity as an ideal cluster, and calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction," the heading and attitude correction method and the heading and attitude correction system can correct parameters, thereby ensuring an efficiency of an antenna beam tracking.

Claim 1:
A heading and attitude correction method, which is applicable to a heading and attitude correction system (<NUM>), comprising:
obtaining a plurality of attitude data in any one of axial directions in a period of time from an axial sensor (<NUM>); and
performing a linear regression analysis on the attitude data and a plurality of time points in the time period to obtain a regression line (L) and a standard characterized by comprising:
obtaining a deviation value between the attitude data and the regression line (L) at each of the time points;
excluding the attitude data for which the deviation value is greater than or equal to at least twice the standard deviation;
grouping the attitude data according to a grouping value to form a plurality of clusters, wherein the grouping value is a minimum accuracy of the axial sensor (<NUM>) of the heading and attitude correction system (<NUM>);
comparing a total quantity of the attitude data in each of the clusters, and defining one of the clusters with a largest total quantity as an ideal cluster; and
calculating an average of the attitude data in the ideal cluster as a reasonable attitude data after correction.