Outlier-Tolerant Navigation Satellite Positioning via Robust Filtering
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Solution Overview
Problem
Current navigation satellite systems face limitations in achieving precise position estimation due to measurement errors and outliers, particularly in code-based positioning methods, which result in lower accuracy and increased computational burden, especially when dealing with ionospheric and tropospheric effects.
Innovation Solution
The implementation of a robust statistical method using a 'robustifier' and 'main estimator' filters in navigation satellite systems, where the robustifier identifies and corrects or rejects outliers, adjusting the stochastic model to improve position estimation accuracy with a reduced computational burden by using fewer state variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If code-based positioning methods are used in navigation satellite systems, then the positioning can be implemented with simpler signal processing, but the positioning accuracy is limited to approximately 15 meters due to atmospheric distortion and electronic detection uncertainty
Solution Approach 1:
The patent combines code-based positioning and carrier phase-based positioning methods into a unified filtering framework. The filter integrates measurements from both methods, allowing the system to benefit from the simplicity of code-based detection while achieving the high precision of carrier phase measurements through their combined processing.
Solution Approach 2:
The filtering system is designed to process multiple types of measurements (code measurements and carrier phase measurements) through a universal framework. This multi-functional approach allows the same filter structure to handle different measurement types with their respective characteristics, achieving both simplicity and precision.
2Measurement precision
If carrier phase measurements are used for positioning, then position precision can be improved to centimetre-level or millimetre-level, but the integer ambiguity problem arises making the phase measurements ambiguous by an unknown number of cycles
Solution Approach 1:
The patent implements a feedback mechanism where the filter continuously updates its estimates of integer ambiguities based on incoming carrier phase measurements. The filter uses previous estimates and new measurements to refine ambiguity resolution, providing feedback that progressively reduces ambiguity while maintaining precision.
Solution Approach 2:
The system performs preliminary processing of carrier phase measurements to establish initial ambiguity estimates before final position calculation. This preliminary action prepares the data in a form that reduces ambiguity impact while preserving the high precision potential of carrier phase measurements.
3Device complexity
If traditional filtering methods process all measurements equally, then the computational model remains simple, but the presence of outliers significantly degrades positioning accuracy
Solution Approach 1:
The patent applies local quality by assigning different weights to different measurements based on their reliability. The filter dynamically adjusts the influence of each measurement on the position estimate, giving less weight to outliers and more weight to reliable measurements, thereby maintaining accuracy without requiring a completely complex computational model.
Solution Approach 2:
The system changes parameters dynamically by adjusting measurement weights and filter characteristics based on the quality of incoming data. When outliers are detected, the filter modifies its parameters to reduce their impact, maintaining positioning accuracy while keeping the computational model relatively simple through adaptive parameter adjustment.
4Measurement precision
If robust statistical methods are implemented to handle outliers, then positioning accuracy is maintained in presence of measurement errors, but the computational burden increases
Solution Approach 1:
The patent implements partial robust statistical processing by applying outlier rejection techniques selectively to specific measurement types or under specific conditions. Rather than applying full robust statistical methods to all measurements, the system uses partial processing where appropriate, reducing computational burden while maintaining accuracy where most needed.
Solution Approach 2:
The filtering process is segmented into stages, with robust statistical methods applied at specific stages rather than continuously. The system segments the processing to apply computationally intensive outlier handling only when necessary, maintaining a balance between accuracy and computational burden through staged processing.
Data Source
AI summary
The invention relates to a method carried out by a navigation satellite system (NSS) receiver or a processing entity receiving data therefrom, for estimating parameters useful to determine a position. The NSS receiver observes NSS signals from NSS satellites. Two filters, called “robustifier” and “main estimator” respectively, both use state variables and compute the values thereof based on: NSS signals observed by the NSS receiver, and/or information derived therefrom. The robustifier identifies, within the input data, measurements that do not match a stochastic model assigned thereto. For each identified measurement, the robustifier rejects the measurement, adjusts the stochastic model assigned to the measurement, and/or corrects the measurement. The robustifier uses fewer state variables than the main estimator. A corresponding system is also disclosed.