Strict Reverse Navigation Method for Fine Alignment
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Solution Overview
Problem
Current navigation technologies face challenges in achieving accurate and rapid convergence during the initial alignment process, especially in dynamic environments like water surfaces, due to accumulation of approximation errors from repeated forward and reverse calculations, leading to increased alignment time and reduced accuracy.
Innovation Solution
A strict reverse navigation method with adaptive control over the number of forward and reverse calculations, using a control function to adjust the number of iterations based on time periods, ensuring accurate and efficient alignment by minimizing errors and accelerating convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a small-angle approximation is adopted for angular speed of reverse rotation when posture matrix is reversely updated, then calculation complexity is reduced, but approximation errors accumulate with repeated forward and reverse calculations
Solution Approach 1:
The patent applies dynamics by making the reverse rotation angular speed adaptive rather than static. The angular speed is dynamically adjusted based on the current alignment error estimates, allowing the system to transition from high correction rates when errors are large to finer adjustments as alignment improves. This dynamic adaptation eliminates the need for small-angle approximations while managing calculation complexity through intelligent parameter adjustment rather than brute-force precise calculations at all times.
Solution Approach 2:
The patent changes the parameter of reverse rotation angular speed from a fixed approximate value to a dynamically adjusted value based on alignment error estimates. By modifying this key parameter adaptively during the alignment process, the system achieves high precision without requiring small-angle approximations, resolving the contradiction between calculation simplicity and alignment accuracy.
2Measurement precision
If the number of forward and reverse calculations is increased to improve alignment accuracy, then convergence precision is improved, but alignment time increases
Solution Approach 1:
The patent applies dynamics by making the number of forward and reverse calculations adaptive rather than fixed. The system dynamically adjusts the calculation intensity based on the current alignment error estimates, performing more calculations when errors are large and fewer calculations as alignment improves. This dynamic approach optimizes the trade-off between alignment accuracy and alignment time by concentrating computational resources when they are most needed.
Solution Approach 2:
The patent applies partial action by performing the full sequence of forward and reverse calculations selectively rather than continuously. When alignment errors are small, the system reduces or skips certain calculation cycles, performing only the necessary partial computations needed to maintain or improve alignment. This avoids the excessive time consumption of continuous full calculations while still achieving high precision alignment.
3Loss of time
If adaptive control is applied to the number of forward and reverse calculations in different time periods, then alignment time is reduced, but calculation logic complexity increases
Solution Approach 1:
The patent applies dynamics by implementing adaptive control that automatically adjusts the number of forward and reverse calculations based on real-time alignment error estimates. The control logic dynamically transitions between different calculation intensities based on the system's current state, reducing alignment time through intelligent resource allocation. The complexity is managed by tying the adaptive control directly to measurable alignment errors rather than requiring complex external control systems.
Data Source
AI summary
A strict reverse navigation method for optimal estimation of fine alignment is provided. The strict reverse navigation method including: establishing an adaptive control function; performing a forward navigation calculation process; performing a reverse navigation calculation process; and performing the adaptive control for a number of forward and reverse calculations. The strict reverse navigation method shortens an alignment time for the optimal estimation of fine alignment while ensuring an alignment accuracy. The strict reverse navigation method provided effectively solves a problem that an error of an initial value of filtering in an initial stage of the optimal estimation of fine alignment affects convergence speeds of subsequent stages. In the initial stage, a larger number of the forward and reverse navigation calculations are adopted to reduce an error of the initial value as much as possible and increase a convergence speed of the filtering.