GNSS Outlier Detection via Doppler-RANSAC Segmentation
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Solution Overview
Problem
Standard GNSS outlier detection algorithms are either inaccurate or inefficient, with some being too complex for practical use in Global Navigation Satellite System (GNSS) applications.
Innovation Solution
A system and method that integrates multiple outlier algorithms in sequence, including Doppler-pseudorange comparison, Random Sampling Consensus (RANSAC) checks, and history-based checks, where RANSAC uses filtered outputs from Doppler-pseudorange comparison to reduce computational subsets and exploit temporal correlations of outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard outlier algorithms are used for GNSS pseudorange measurements, then outlier detection can be performed, but the algorithms are either inaccurate or too complex and inefficient
Solution Approach 1:
The outlier detection process is segmented into three distinct stages: (1) Doppler-pseudorange comparison for initial outlier identification, (2) RANSAC check for selected subsets to confirm outliers, and (3) history-based checks for undetermined satellites. This segmentation allows each algorithm to focus on specific tasks, improving overall accuracy while managing computational complexity through progressive filtering.
Solution Approach 2:
The Doppler-pseudorange comparison is performed as a preliminary action before RANSAC checking. By using the simpler Doppler comparison to pre-filter and identify potential outliers first, the system reduces the number of satellites that need to undergo computationally intensive RANSAC processing, thereby improving efficiency without sacrificing detection accuracy.
2Reliability
If RANSAC check is performed on all satellites, then comprehensive outlier filtering is achieved, but computational complexity increases significantly
Solution Approach 1:
Instead of performing RANSAC checks on all satellites, the system applies partial action by conducting RANSAC checks only on selected subsets of satellites that are identified as potential outliers through the preliminary Doppler-pseudorange comparison. This selective approach maintains sufficient outlier filtering reliability while dramatically reducing computational complexity and improving processing efficiency.
Solution Approach 2:
The system applies different levels of checking quality to different satellites based on their risk profiles. Satellites identified as potential outliers by Doppler comparison undergo rigorous RANSAC verification, while satellites with good initial assessments receive lighter history-based checks. This local differentiation of quality levels optimizes the balance between reliability and computational efficiency.
3Measurement precision
If multiple outlier algorithms are integrated in sequence, then detection accuracy improves, but processing time increases
Solution Approach 1:
The Doppler-pseudorange comparison serves as a preliminary action that quickly identifies potential outliers before more time-consuming algorithms are applied. This preliminary filtering reduces the workload for subsequent RANSAC and history-based checks, thereby minimizing overall processing time while maintaining high detection accuracy through the sequential application of multiple algorithms.
Solution Approach 2:
The system efficiently discards satellites that are clearly identified as outliers or inliers at earlier stages, avoiding unnecessary processing in later stages. By discarding clearly determined cases early and only pursuing ambiguous cases through multiple checking stages, the system reduces average processing time while maintaining comprehensive accuracy for all satellites.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and efficiency of GNSS outlier detection and rejection by combining different algorithms, improving the reliability of satellite status determination and reducing computational complexity.
Implementation Method 1
performing a Doppler-pseudorange comparison for each of the satellites in the GNSS to determine a status of each of the satellites as an outlier or undetermined
Data Source
AI summary
A Global Navigation Satellite System (GNSS) receiver for performing GNSS outlier detection and rejection is provided. When the GNSS receiver receives GNSS signals from satellites in the GNSS, the GNSS receiver processes the GNSS signals to perform positioning. Then, the GNSS receiver sequentially performs a Doppler-pseudorange comparison, a Random Sampling Consensus (RANSAC) check for selected subsets of the satellites, and a history-based check for the satellites to determine a status of each satellites as an outlier or an inlier. Specifically, in the RANSAC check, the subsets of the satellites are selected using results of the Doppler-pseudorange comparison as inputs to filter the satellites, thus reducing the number of subsets needed for computation in the RANSAC check. The status of the satellites are recorded for the history-based check, which further exploits the correlations of outliers across time.