GNSS Receiver Startup Using Predictive Satellite Visibility
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Solution Overview
Problem
Existing GNSS receivers with non-directional antennas face challenges in accuracy and reliability due to signal obscuration and multipath interference, particularly in urban and rural environments.
Innovation Solution
The technology involves a cloud-based system that provides predictive data for GNSS receivers, using environmental maps to forecast line-of-sight visibility to satellites and predict satellite obscurations and multipath, enabling better satellite selection and signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GNSS receivers with non-directional antennas are used, then device simplicity is maintained, but positioning accuracy deteriorates due to signal obscuration and multipath interference
Solution Approach 1:
The system performs preliminary forecasting of satellite visibility and signal quality before the receiver actually needs positioning data. Environmental maps are pre-processed to predict line-of-sight conditions and potential multipath interference zones, allowing the receiver to proactively select optimal satellites and adjust processing parameters before entering problematic areas.
Solution Approach 2:
A cloud-based forecasting service acts as an intermediary between the simple non-directional antenna and the positioning algorithm. This intermediary provides processed environmental data and satellite visibility predictions, enabling the simple hardware to achieve performance comparable to complex directional systems without adding physical complexity to the receiver.
2Measurement precision
If cloud-based predictive data systems are implemented, then positioning accuracy improves, but system complexity increases
Solution Approach 1:
The forecasting system serves itself by using pre-computed environmental maps and satellite ephemeris data that are independently available. The system processes this data through automated algorithms to generate predictions, reducing the need for complex real-time computations at the receiver end and minimizing human intervention requirements.
Solution Approach 2:
Complex mechanical or physical directional antenna systems are replaced with computational approaches. Instead of using physically complex directional antennas or multiple sensor arrays, the system substitutes these with software-based signal processing and predictive algorithms that run in the cloud, achieving similar or better performance with simpler hardware.
3Productivity
If real-time satellite visibility forecasting is provided, then satellite selection improves, but data processing time increases
Solution Approach 1:
The system merges pre-computed environmental map data with real-time satellite ephemeris information to generate forecasts. By combining these data sources and processing them through integrated algorithms, the system achieves comprehensive satellite visibility prediction without requiring separate processing steps, thus reducing overall computation time while maintaining selection quality.
Solution Approach 2:
The system computes satellite visibility forecasts for a broader set of satellites and time periods than immediately needed, then selects from this pre-computed set. This excessive computation in advance allows the receiver to quickly select optimal satellites without performing complex real-time calculations, trading some upfront processing for faster operational decision-making.
Data Source
AI summary
Disclosed is reducing starting time for a GNSS receiver that has an imprecise initial starting location by requesting starting assistance from a CDN that caches predictive data including first data indicated predicted LOS visibility from the receiver to individual satellites, wherein the request includes the imprecise initial staring location, receiving, from the CDN, data that includes a first block of the predictive data for the imprecise initial staring location and further adjoining second blocks of predictive data for areas surrounding the imprecise staring location, determining, by the GNSS receiver, commonly available satellites that have visibility from locations in both the first block and the second block, and calculating a first starting position using weighted values for the satellites, the commonly available satellites having higher weighted value than satellites without visibility in both locations, whereby position uncertainty of the first starting position is reduced from the imprecise initial starting location.