GNSS Receiver Confidence Value Parity Check
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Solution Overview
Problem
GNSS receivers face challenges in decoding data accurately under weak signal conditions due to false positives caused by parity algorithm failures, leading to incorrect user position and system instability.
Innovation Solution
The implementation of a confidence value system for navigation message data bits to detect and filter out false positives, improving data fidelity by using a combination of confidence checks and consistency checks in conjunction with parity checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the parity algorithm is used to check data under weak signal conditions, then data processing speed is maintained, but false positives occur leading to incorrect positioning data
Solution Approach 1:
A confidence value metric is introduced as an intermediary between the parity check and data acceptance. This confidence value, derived from signal quality indicators (coherent/incoherent integration ratios), acts as a mediator that prevents false positives by evaluating signal reliability before accepting parity-checked data, thus maintaining both processing speed and data accuracy
Solution Approach 2:
The confidence value evaluation is performed preliminarily before the final data acceptance decision. By calculating signal quality metrics and confidence values in advance of the parity check results, the system proactively identifies unreliable data packets, preventing incorrect data from being processed further and maintaining positioning accuracy
2Reliability
If multiple check mechanisms are added to filter false positives, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The system changes the parameter being monitored from simple parity bits to a confidence value derived from signal quality parameters (coherent/incoherent integration ratios). This parameter transformation enables more sophisticated error detection without adding complex hardware structures, as the confidence metric can be computed from existing signal processing outputs
Solution Approach 2:
The confidence value mechanism serves multiple functions simultaneously: it evaluates signal quality, predicts potential parity check failures, and provides a basis for data acceptance decisions. This multi-functionality reduces the need for separate dedicated error detection systems, thereby limiting the increase in overall system complexity
3Reliability
If confidence checks are performed on all data bits, then false positives are reduced, but processing time increases
Solution Approach 1:
The confidence check is applied selectively rather than universally to all data bits. The system calculates confidence values based on signal quality metrics for packets that are candidates for processing, applying the check only where signal conditions suggest potential errors. This partial application reduces the overall processing time burden while still catching the majority of false positives
Solution Approach 2:
The signal processing chain itself generates the confidence metric as a byproduct of normal operation. The coherent and incoherent integration ratios, already computed for signal tracking and acquisition, are repurposed to generate confidence values for data validation. This self-service approach avoids adding separate dedicated processing steps, minimizing time loss
Data Source
Figure 1a~1b
Figure 1c
Figure 2
AI summary
This disclosure relates to a positioning receiver, comprising a processor configured to: process a GNSS satellite transmitted navigation message received from at least one respective satellite vehicle to provide a navigation message data packet and to determine for each data bit of the navigation message data packet a respective confidence value; and determine positioning data based on the data bits of the navigation message data packet and respective confidence values.