Vector Tracking Loops for GNSS Positioning
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Solution Overview
Problem
Vector tracking approaches for GNSS satellite tracking suffer from higher computational load compared to classical scalar tracking methods, particularly due to increased Kalman filter update rates, which is inefficient for Single Point Positioning (SPP) predictions.
Innovation Solution
A method where GNSS signals from multiple channels are connected to a common Kalman filter to form vector tracking loops, allowing for synchronous Kalman filter updates and reducing computational effort by aggregating measurements into a virtual value for iterative processing, thereby maintaining tracking capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vector tracking approaches are used for GNSS satellite tracking, then tracking capability in low signal-to-noise environments is improved, but computational load increases significantly
Solution Approach 1:
The patent segments the computational workload by separating scalar tracking operations (performed independently on each channel at high update rates) from vector tracking operations (performed synchronously across channels at lower update rates). This segmentation allows the system to maintain the tracking robustness of vector tracking while reducing the overall computational burden by performing intensive Kalman filter updates less frequently.
Solution Approach 2:
The patent implements periodic action by performing full Kalman filter updates at specific intervals rather than continuously at every tracking update rate. The system performs scalar tracking continuously at high rates (50-100 Hz per channel) but only performs synchronous vector tracking and Kalman filter updates at lower periodic intervals, thereby reducing computational load while maintaining tracking capability.
2Measurement precision
If synchronous Kalman filter updates are performed for all channels in vector tracking, then position determination accuracy is improved, but computational effort increases
Solution Approach 1:
The patent merges the tracking operations across multiple channels into a unified vector tracking framework where scalar tracking results from all channels are combined and processed together in synchronous Kalman filter updates. This merging improves position determination accuracy by utilizing correlated information from all channels simultaneously, while the systematic combination reduces redundant computations compared to independent scalar tracking on each channel.
Solution Approach 2:
The patent creates a universal Kalman filter that serves multiple channels simultaneously, performing position determination for all satellite signals in a single computational pass. This multi-functional approach allows the same filter to process measurements from multiple channels, improving accuracy through combined information while reducing overall computational effort compared to separate filters for each channel.
Data Source
AI summary
A method is disclosed for position determination by receiving GNSS signals using a GNSS receiver including a plurality of channels, which can be connected in a data-conducting manner to a common Kalman filter to form a plurality of vector tracking loops such that each received GNSS signal can be processed in an iterative manner using a corresponding vector tracking loop while taking into account the measurements returned by the common Kalman filter. The method includes (a) receiving GNSS signals, (b) processing a received GNSS signal using a corresponding vector tracking loop such that the information contained in the GNSS signal is determined while taking into account a virtual value, wherein the virtual value is generated such that measurements to be returned by the common Kalman filter are aggregated to this virtual value for a predefined number of iterations, (c) entering the information determined from the GNSS signal into the common Kalman filter, (d) determining the position using the common Kalman filter based on the information determined from the GNSS signal, and (e) repeating steps (a) to (d) for subsequent iterations.