Hybrid Navigation Data Fusion via Sensor Error Estimation
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Solution Overview
Problem
Conventional GNSS systems face limitations in accurately combining navigation data from various sensors, leading to errors and inefficiencies in computing universal hybrid navigation information for GNSS-enabled devices.
Innovation Solution
A method and system that collect and format GNSS measurements and non-GNSS sensor data, estimate measurement errors, remove erroneous data, and calibrate sensors to compute accurate navigation information, utilizing a single function to combine GNSS and non-GNSS data from multiple sensors such as cellular radios, WiFi, Bluetooth, and others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple sensors (cellular radio, WiFi, Bluetooth, etc.) are integrated to provide navigation data, then the quantity and diversity of navigation data increases, but the complexity of combining and processing data from different sensor formats increases
Solution Approach 1:
The patent implements a universal data processing function that can handle multiple sensor types (GNSS, cellular radio, WiFi, Bluetooth, magnetic sensors, motion sensors, gyroscopes, pressure sensors, image sensors, sonar sensors) through a single unified interface. This multi-functional approach allows the system to process diverse navigation data sources without requiring separate processing paths for each sensor type, thereby increasing data quantity while controlling processing complexity.
Solution Approach 2:
The system dynamically adjusts processing parameters based on the specific sensor type and data characteristics. By changing processing parameters rather than creating separate processing algorithms for each sensor, the system efficiently handles diverse navigation data with a unified framework, resolving the contradiction between data diversity and processing complexity.
2Loss of information
If all collected non-GNSS sensor data is used in navigation computation, then the completeness of navigation information increases, but the accuracy of navigation results deteriorates due to erroneous measurements
Solution Approach 1:
The patent implements a feedback mechanism where the system estimates measurement errors for each non-GNSS sensor data source and uses this error information to selectively weight or discard measurements. This feedback loop allows the system to maintain completeness of navigation information while filtering out erroneous measurements, thereby preserving accuracy. The system continuously monitors measurement quality and adjusts data inclusion based on estimated error levels.
Solution Approach 2:
The system applies different quality standards and processing treatments to different data sources based on their specific characteristics and error profiles. Rather than uniformly processing all sensor data, the system evaluates each measurement's local quality and applies appropriate weighting or filtering, allowing high-quality data to contribute fully while excluding erroneous measurements.
3Device complexity
If a single function is used to compute navigation information from both GNSS and non-GNSS data, then the simplicity of the processing architecture increases, but the difficulty of handling different data formats increases
Solution Approach 1:
The patent introduces a format conversion intermediary layer that translates diverse sensor data formats into a unified internal representation before processing. This intermediary function handles the complexity of format differences, allowing the main navigation computation function to remain simple and unified. The conversion layer acts as a mediator that isolates format complexity from the core processing logic.
4Measurement precision
If measurement error estimation and data filtering are performed, then the accuracy of navigation results improves, but the processing time and loss of time increase
Solution Approach 1:
The system performs measurement error estimation and filtering selectively rather than universally. It applies error estimation and data filtering only to the extent necessary to achieve required accuracy levels, avoiding excessive processing. This partial action approach maintains accuracy while minimizing unnecessary processing time and computational overhead.
Data Source
AI summary
A Global navigation satellite-based systems (GNSS) enabled device, handling at least two of a plurality of sensors, collects GNSS measurements and navigation related non-GNSS sensor data. The collected navigation related non-GNSS sensor data is automatically formatted into a data format that is compatible with a format of the GNSS measurements. The formatted navigation related non-GNSS sensor data and the GNSS measurements are utilized by a single function to compute navigation information for the GNSS enabled device regardless of sensor configurations such as a cellular radio and/or a motion sensor. Measurement errors in the collected navigation related non-GNSS sensor data is estimated to determine measurement accuracy. The collected navigation related non-GNSS sensor data is selectively adopted, combined with the GNSS measurements, to compute navigation information by the single function based on the determined measurement accuracy. The computed navigation information may be utilized to calibrate sensor and/or sensor data when needed.