Sliding Window Data Backtracking for Global Optimal Navigation Track
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Solution Overview
Problem
The ICCP algorithm is prone to local optimal solutions due to the limited number of matching points, which restricts its ability to find the global optimal navigation track in geophysical field aided navigation systems.
Innovation Solution
A contour line matching method based on sliding window data backtracking is introduced, where matching iterations are performed across all sliding windows to find the closest point on the contour line, utilizing matrix eigenvalue and eigenvector decomposition to obtain a rotation transformation matrix, thereby improving the robustness and precision of contour line matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the ICCP algorithm uses measured physical field value under a local track for matching, then the matching process is computationally efficient, but the matched track result is only the local optimal solution and the global optimal navigation track cannot be obtained
Solution Approach 1:
The patent divides the continuous track into multiple sliding window segments, each containing a limited number of data points. This segmentation allows the ICCP algorithm to be applied efficiently to each segment while the sliding window mechanism systematically moves through the entire track, ensuring both computational efficiency and global optimality.
Solution Approach 2:
The patent performs preliminary rough matching using the TERCOM algorithm to establish an initial position estimate before applying the ICCP algorithm in sliding windows. This preliminary action ensures that the sliding window matching starts from a reasonable position, improving both convergence speed and accuracy while maintaining computational efficiency.
2Measurement precision
If more physical quantity measured values are accumulated for matching, then the matching position accuracy is improved, but the calculation amount increases and real-time matching capability is reduced
Solution Approach 1:
The patent uses a partial action approach by selecting only a limited number of data points within each sliding window for matching, rather than using all available data. This partial selection maintains real-time processing capability while achieving sufficient accuracy through the systematic sliding window approach that covers the entire track.
Solution Approach 2:
By segmenting the data into sliding windows of fixed size, the patent processes a manageable subset of data points at each step. This segmentation enables real-time processing while the sequential sliding through all windows ensures comprehensive coverage and high overall accuracy.
3Measurement precision
If the sliding window size is increased to include more data points, then the global optimal solution is better achieved, but the calculation amount increases and processing time is extended
Solution Approach 1:
The patent uses an optimal window size that includes enough data points to achieve global optimality within the window, but not so many as to cause excessive computation. This balanced partial action approach ensures sufficient accuracy while maintaining acceptable processing time through efficient use of limited data per window.
Solution Approach 2:
The sliding window mechanism performs periodic matching operations at regular intervals along the track, with each window containing a fixed number of data points. This periodic action ensures systematic coverage of the entire track with consistent computational effort at each step, balancing accuracy and processing time.
Data Source
AI summary
To obtain a global optimal navigation track, a contour line matching method based on sliding window data backtracking includes the steps of determining sliding window parameters according to the calculation performance of a real-time multi-task operating system and aided navigation precision requirements, constructing a sliding window data backtracking framework by using historical physical field value matching data, obtaining a rotation transformation matrix from an indication track point set to a closest reference point set by adopting a matrix eigenvalue and eigenvector decomposition method, and moving a sliding window and performing forward and reverse cyclic matching to achieve a global track constraint, thereby improving the matching precision and robustness.