Satellite Navigation Algorithm Switching for Bias Recovery
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Solution Overview
Problem
Kalman Filter algorithms in GPS receivers take time to recover from biases caused by noisy or erroneous measurements, which can be problematic in environments with satellite signal reflections and noise, such as urban areas, leading to delayed accurate positioning.
Innovation Solution
A method that dynamically switches between Least Square and Kalman Filter algorithms based on signal quality metrics, such as the number of satellites, dilution of precision, and carrier-to-noise ratio, to quickly recover from measurement errors and maintain accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman Filter algorithm is used for continuous positioning, then positioning accuracy is improved, but recovery time from measurement biases increases
Solution Approach 1:
The system dynamically switches between Kalman Filter and Least Square algorithms based on real-time signal quality assessment. When signal degradation is detected (indicating potential bias), the system transitions from Kalman Filter to Least Square algorithm, then returns to Kalman Filter when signal quality improves, enabling adaptive recovery from measurement biases
Solution Approach 2:
The system continuously monitors signal quality metrics (satellite count, DOP, CN0) and uses this feedback to determine when to switch between algorithms. The feedback loop detects bias conditions and triggers appropriate algorithm switching, accelerating recovery time while maintaining positioning accuracy
2Duration of action of stationary object
If Kalman Filter algorithm is used in urban environments with signal reflections, then positioning continuity is maintained, but positioning accuracy deteriorates
Solution Approach 1:
The system adapts its computational method based on environmental conditions by monitoring signal quality metrics. In urban environments with signal reflections, when degradation is detected, the system switches from Kalman Filter to Least Square algorithm, then returns to Kalman Filter when conditions improve, maintaining both continuity and accuracy
Solution Approach 2:
The system changes the algorithm parameter (computational method) based on signal quality parameters (satellite count, DOP, CN0). This parameter switching allows the system to maintain positioning continuity while improving accuracy by selecting the appropriate algorithm for current environmental conditions
3Speed
If Least Square algorithm is used for initial positioning, then positioning speed is improved, but positioning accuracy in noisy conditions deteriorates
Solution Approach 1:
The system uses dynamic algorithm selection based on signal quality assessment. For initial positioning or in noisy conditions, it selects the appropriate algorithm (Least Square for speed, Kalman Filter for accuracy), then switches between them based on real-time signal quality, optimizing both speed and accuracy
Data Source
AI summary
A cinematic parameter computing method of a satellite navigation system, including: receiving signals at a receiving apparatus from a plurality of satellites; processing said signals to provide received data; computing a first cinematic parameter value of said receiving apparatus according to a first computational method using said received data; computing a second cinematic parameter value of said receiving apparatus according to a second computational method using said received data and computing a distance value representing a difference between said first and second cinematic parameter values. The distance value is compared with a reference value providing a comparison result data and selecting one of first and second computational methods based on said comparison result data.