Handheld Geodesic Device Multipath Error Compensation
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Solution Overview
Problem
Current GNSS-based navigation systems face challenges in achieving high accuracy, particularly in differential navigation/positioning applications, due to multipath errors caused by signal reflections, especially when the rover and base are moving, and the need for precise relative positioning in environments with obstacles like foliage or buildings.
Innovation Solution
A handheld geodesic device that captures images from multiple known points to determine the position of an unknown point using GNSS signals, horizon sensors for orientation correction, and image processing algorithms to improve positioning accuracy, even in obstructed environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If differential navigation/positioning is used to improve positioning accuracy, then positioning accuracy is improved, but multipath errors from signal reflections worsen measurement reliability
Solution Approach 1:
The patent converts the harmful multipath effect into a useful measurement by detecting reflected GNSS signals and using them to determine reflection characteristics of surfaces. The system identifies that multipath signals carry information about surface properties and uses this information to improve rather than degrade positioning accuracy
Solution Approach 2:
The patent introduces image sensors as intermediaries to capture visual information about the environment and surfaces. These images serve as mediators between the GNSS signals and the processing system, enabling the system to identify reflection characteristics and compensate for multipath errors through image-based analysis
2Measurement precision
If carrier phase measurements are used to improve measurement accuracy, then measurement accuracy improves to within a small fraction of carrier wavelength, but the system becomes more sensitive to multipath errors
Solution Approach 1:
The patent converts the harmful multipath effect into a useful measurement by detecting reflected GNSS signals and using them to determine reflection characteristics of surfaces. The system identifies that multipath signals carry information about surface properties and uses this information to improve rather than degrade positioning accuracy
Solution Approach 2:
The patent implements feedback by using image sensors to continuously monitor surface characteristics and environment changes that affect signal reflections. This visual feedback is processed to identify reflection characteristics, which then feed back into the positioning algorithm to compensate for multipath errors in real-time
3Measurement precision
If a base receiver is used to provide differential corrections to improve positioning accuracy, then positioning accuracy is improved, but the system complexity and reliance on external infrastructure increases
Solution Approach 1:
The patent enables the rover receiver to serve itself by equipping it with image sensors and processing capabilities to independently detect and characterize multipath reflections. The system performs its own error characterization and compensation without requiring external base station infrastructure, making the system self-sufficient
Solution Approach 2:
The patent makes the rover receiver multi-functional by integrating image sensing capabilities alongside GNSS reception. The same device that receives positioning signals also captures visual data for environmental analysis and multipath characterization, eliminating the need for separate base station infrastructure
4Ease of operation
If traditional GNSS receivers are used in obstructed environments, then positioning is achieved, but accuracy deteriorates due to signal blockage and multipath reflections
Solution Approach 1:
The patent introduces image sensors as intermediaries to capture visual information about the environment and surfaces. These images serve as mediators between the GNSS signals and the processing system, enabling the system to identify reflection characteristics and compensate for multipath errors through image-based analysis
Solution Approach 2:
The patent implements feedback by using image sensors to continuously monitor surface characteristics and environment changes that affect signal reflections. This visual feedback is processed to identify reflection characteristics, which then feed back into the positioning algorithm to compensate for multipath errors in real-time
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
Enhances positioning accuracy by compensating for multipath errors and orientation issues, allowing for precise relative positioning of moving objects, such as aircraft or vehicles, with improved productivity and reduced reliance on external base stations.
Implementation Method 1
by measuring the carrier phase of the signal received from a satellite in the base receiver and comparing it with the carrier phase of the same satellite measured in the rover receiver
Implementation Method 2
A first image is captured of the first point using an image sensor; the first image includes the unknown point and at least one of the second point or the third point
Implementation Method 3
horizon sensors for orientation correction
Data Source
AI summary
A method for using a GNSS device to determine a position of an unknown point includes determining positions of a first point, a second point, and a third point using the GNSS device. A first image is captured of the first point using an image sensor, the image includes the unknown point and at least one of the second point or the third point. A second image is captured from the second point; the second image includes the unknown point and at least one of the second point or the third point. A third image is captured from the third point; the third image includes the unknown point and at least one of the second point or the first point. A position of the unknown point is calculated based on the first, second, and third images and the first, second, and third positions.