GNSS Positioning Deweighting for Multipath Error Mitigation
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Solution Overview
Problem
In multipath environments, such as dense urban areas, Global Navigation Satellite System (GNSS) receivers face challenges in accurately estimating device position due to signal interference and multipath errors, which are correlated over time and affected by device speed.
Innovation Solution
The system adjusts the variance in measurement error based on device speed, using a deweighting module to mitigate multipath errors by increasing deweighting for slower speeds and decreasing it for faster speeds, and employs a Kalman filter to improve position estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard GNSS measurement processing is used, then position estimation is simple, but measurement accuracy deteriorates in multipath environments due to time-correlated errors
Solution Approach 1:
The patent applies dynamics by making the deweighting factor speed-dependent and time-varying. Instead of using a static deweighting approach, the system dynamically adjusts the deweighting factor based on the device's current speed and historical speed information. This allows the measurement processing to adapt to changing multipath conditions in real-time, improving position accuracy without requiring a completely complex new processing framework.
Solution Approach 2:
The patent changes the parameter being dewighted from a fixed value to a speed-dependent variable. The deweighting factor is modified based on the device speed, with different factors applied for different speed ranges. This parameter change allows the system to account for the time-correlated nature of multipath errors that vary with device motion, thereby improving measurement precision while maintaining manageable processing complexity.
2Measurement precision
If deweighting is increased for slower speeds, then multipath error mitigation improves, but processing time increases
Solution Approach 1:
The patent applies partial action by selectively applying different deweighting factors based on speed conditions rather than using a uniform high deweighting approach for all cases. For faster speeds where multipath errors are less correlated, a lower deweighting factor is used, reducing processing time. For slower speeds where time-correlated errors are more significant, a higher deweighting factor is applied to improve accuracy. This selective approach balances accuracy improvement with processing efficiency.
Data Source
AI summary
A device implementing a system for estimating device position includes at least one processor configured to receive a first sensor measurement of a device at a first time, the first sensor measurement having a first variance in measurement error, and to receive a second sensor measurement of the device at a second time, the second sensor measurement having a second variance in measurement error. The at least one processor is further configured to determine a speed of the device based on at least one of the first or second sensor measurements, and adjust the second variance in measurement error based on the determined speed. The at least one processor is further configured to estimate a device position based at least in part on the first variance in measurement error and the adjusted second variance in measurement error.