Heading and Attitude Correction with Regression Filtering for Beam Tracking
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Solution Overview
Problem
Conventional heading and attitude reference systems in electronically-driven array antennas are susceptible to environmental interference, leading to inaccurate measurements and reduced efficiency of antenna beam tracking.
Innovation Solution
A method and system that utilizes an axial sensor and a calibration module to perform linear regression analysis on attitude data, exclude data with deviations greater than twice the standard deviation, group the remaining data into clusters, and define the cluster with the largest data quantity as the ideal cluster to calculate an average for corrected attitude data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If axial sensor is used to obtain attitude data in conventional heading and attitude reference systems, then the system can obtain measurement values, but the measurement values are inaccurate due to environmental interference
Solution Approach 1:
The system performs preliminary actions by collecting multiple attitude data samples over a period of time before making the final heading determination. This allows the system to pre-process the data, identify outliers through statistical analysis, and establish a baseline of normal operation to filter out environmental interference effects.
Solution Approach 2:
The system implements feedback by continuously monitoring the statistical properties (mean, standard deviation) of the attitude data and adjusting the filtering process accordingly. The outlier detection mechanism provides feedback to identify and exclude inaccurate measurements, while the clustering algorithm refines the selection based on data distribution patterns, thereby compensating for environmental interference.
2Productivity
If all attitude data from axial sensor is used for heading calculation, then the calculation can be performed, but the efficiency of antenna beam tracking is greatly reduced due to inaccurate values
Solution Approach 1:
The system extracts and removes outlier data points from the attitude data set through statistical analysis. By identifying data points that deviate significantly from the mean (beyond a threshold based on standard deviation), the system extracts only the reliable measurements for heading calculation, thereby improving both tracking efficiency and reliability.
Solution Approach 2:
The system changes the parameter selection criterion from using all attitude data to using only filtered attitude data that falls within statistically acceptable ranges. This parameter change in data selection, based on mean and standard deviation calculations, ensures that only high-reliability data contributes to the heading calculation, improving antenna beam tracking efficiency.
3Measurement precision
If outlier attitude data is not excluded, then the data set remains complete, but the heading and attitude measurements become inaccurate
Solution Approach 1:
The system applies partial action by selectively including only a portion of the collected attitude data in the final heading calculation. Through outlier detection and clustering, the system identifies and excludes excessive or harmful data points (outliers) while retaining the useful subset, thereby improving measurement precision without unnecessarily discarding valid information.
Solution Approach 2:
The system applies local quality by treating different attitude data points differently based on their statistical properties. Instead of uniformly processing all data, the system identifies regions of high-quality data (within acceptable deviation from mean) and gives them higher weight, while excluding low-quality outliers, thereby improving overall measurement accuracy.
Data Source
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AI summary
A heading and attitude correction method and a heading and attitude correction system (100) are provided. The method includes: obtaining attitude data in a period of time; performing a linear regression analysis on the attitude data and time points in the time period to obtain a regression line (L) and a standard deviation; 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 clusters; 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.