GNSS Localization Using SNR Shadow Matching and Non-Linear Filtering
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Solution Overview
Problem
Global Navigation Satellite Systems (GNSS) experience significant localization errors in urban environments due to signal reflections and blockages, leading to inaccurate position fixes, especially in high-rise areas where time-of-flight measurements are corrupted by multipath interference and non-line-of-sight channels.
Innovation Solution
A localization system employing a cache and a non-linear filter that utilizes signal-to-noise ratio (SNR) models and shadow matching data to improve position estimates by determining the likelihood of satellite signal blocking, incorporating first-order reflections to refine location estimates, and using a Bayesian framework with particle filtering to account for uncertainties and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard GNSS time-of-flight measurements are used for location estimation, then the system can provide global localization capability, but localization accuracy deteriorates significantly in urban environments due to signal reflections and blockages
Solution Approach 1:
The patent introduces an intermediary processing layer that receives both GNSS measurements and urban environment map data, then fuses them to produce corrected location estimates. This intermediary system mediates between the raw GNSS signals (which are corrupted in urban areas) and the final location output, using the environment map to identify and correct multipath errors.
Solution Approach 2:
The system changes the parameters used for location estimation by incorporating signal-to-noise ratio (SNR) measurements alongside time-of-flight data, and by using probability distributions to represent location uncertainty. It also transforms the problem from direct trilateration to a probabilistic fusion approach that accounts for urban environment characteristics.
2Device complexity
If GNSS signals are processed using traditional trilateration methods, then computation remains simple, but location accuracy deteriorates due to pseudorange corruption from reflected paths
Solution Approach 1:
The system performs preliminary actions by pre-processing GNSS measurements to identify potential multipath errors using SNR thresholds and urban environment maps before final location calculation. It pre-identifies which satellite signals are likely to be corrupted by reflections based on the urban canyon geometry and signal characteristics.
Solution Approach 2:
The system implements feedback by using the urban environment map and SNR measurements to continuously adjust the weighting and selection of satellite signals in the location calculation. The processed location estimates and their uncertainties feed back into the filtering algorithm to improve subsequent estimates.
3Measurement precision
If satellite SNR data is utilized for shadow matching to improve urban localization, then location accuracy in blocked channels improves, but system complexity increases due to probabilistic processing requirements
Solution Approach 1:
The patent segments the signal processing by treating each satellite channel independently, evaluating SNR and blockage probability for each satellite separately before fusing results. This segmentation allows the complex probabilistic processing to be broken down into manageable per-satellite assessments that are then combined.
Solution Approach 2:
The system uses the urban environment map data structure to efficiently query and retrieve relevant building and obstruction information for each satellite signal without requiring complex external database access. The map data serves itself by being organized in a way that enables rapid lookup of environmental features affecting each satellite path.
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 system provides robust and computationally efficient localization and tracking in urban environments, reducing errors and improving accuracy by leveraging SNR data and 3D environmental information to differentiate between line-of-sight and non-line-of-sight signals, thereby enhancing the reliability of GNSS position fixes.
Implementation Method 1
The non-linear filter utilizes a signal-to-noise ratio (SNR) model to determine the likelihood of a particle being located at a particular position given the observed signal strength from each of the plurality of satellites
Implementation Method 2
the presence of tall buildings generates reflections of the received signals. Because the GNSS location estimate is based, at least in part, on how long it takes the signal to reach the device (i.e., so called 'time of flight' measurements), reflections prove especially problematic
Implementation Method 3
shadow matching data to improve position estimates by determining the likelihood of satellite signal blocking
Data Source
AI summary
A method of determining location of a user device includes receiving global navigation satellite system (GNSS) fix data that represents GNSS calculated position of the user device, receiving signal strength data associated with each satellite communicating with the user device, and receiving satellite data regarding locations of satellites. The method further includes retrieving satellite blocking values from a cache that describe a likelihood of a satellite signal being blocked at a plurality of possible locations. A non-linear filter, implemented by one or more processors, is applied to the GNSS fix data, signal strength data, and satellite blocking values to generate an updated position estimate of the user device.