GNSS Localization Using Bayesian Shadow Matching
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Solution Overview
Problem
Global Navigation Satellite Systems (GNSS) localization accuracy is degraded in urban areas due to signal reflections and blockages, leading to significant errors in position fixes, as existing techniques fail to effectively incorporate additional GNSS information such as Doppler shift and carrier phase estimates into probabilistic shadow matching models.
Innovation Solution
A Bayesian framework is employed to fuse GNSS location fixes, pseudorange information, and satellite signal-to-noise ratios (SNRs) using a modified particle filter that increases uncertainty to account for non-line-of-sight reflections and urban environment complexities, incorporating 3D environmental data and shadow matching techniques to improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard GNSS position fix techniques are used, then localization is provided, but localization accuracy is degraded in urban areas due to signal reflections and blockages
Solution Approach 1:
The patent converts the harmful effect of signal reflections into beneficial information by using signal-to-noise ratio (SNR) measurements to detect non-line-of-sight (NLOS) conditions. When SNR falls below a threshold, the system identifies the signal as reflected and excludes it from position calculations, thereby transforming the harmful reflection into a detectable indicator that improves localization accuracy in urban environments
Solution Approach 2:
The patent segments the GNSS signal processing into distinct components: line-of-sight (LOS) signals and non-line-of-sight (NLOS) reflected signals. By separating these signal types using SNR thresholds and processing them differently (including LOS signals in position fix and excluding NLOS signals), the system resolves the contradiction between maintaining position fix capability and achieving accurate localization in reflective urban environments
2Reliability
If pseudorange data from reflected signals is used, then position fix can be obtained, but large errors occur in localization (50 meters or more)
Solution Approach 1:
The patent introduces signal-to-noise ratio (SNR) as an intermediary parameter that mediates between position fix availability and localization accuracy. SNR serves as a quality indicator that allows the system to selectively accept or reject pseudorange data based on signal quality, ensuring that only reliable LOS signals contribute to the position fix while filtering out erroneous NLOS reflected signals that would cause large localization errors
Solution Approach 2:
The patent changes the parameter used for position fix determination from solely relying on pseudorange data to a composite approach that incorporates SNR thresholds. By adding this parameter change criterion, the system maintains position fix availability through LOS signals while eliminating large errors caused by NLOS reflected signals
3Measurement precision
If shadow matching using satellite SNRs is employed, then probabilistic location information is obtained, but device complexity increases
Solution Approach 1:
The patent implements shadow matching using satellite SNRs where the mobile device performs self-service by autonomously comparing measured SNR values against pre-computed shadow maps. This self-service approach enables the device to determine whether it is in the shadow of buildings without requiring complex external processing, thereby achieving improved location information accuracy while keeping device complexity manageable through efficient use of pre-computed data
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
The solution significantly enhances GNSS localization accuracy in urban environments by effectively handling multipath propagation and blockages, providing more reliable position estimates and reducing errors associated with signal reflections.
Implementation Method 1
signal reflections and blockages, leading to significant errors in position fixes
Data Source
AI summary
A localization server improves position estimates of global navigation satellite systems (GNSS) using probabilistic shadow matching and pseudorange matching is disclosed herein. The localization server may utilize one or more of the following information: the locations of the satellites, the GNSS receiver's location estimate and associated estimated uncertainty, the reported pseudoranges of the satellites, the GNSS estimated clock bias, the SNRs of the satellites, and 3D environment information regarding the location of the receiver. The localization server utilizes a Bayesian framework to calculate an improved location estimate using the GNSS location fixes, pseudorange information, and satellite SNRs thereby improving localization and tracking for a user device.