GNSS Receiver State Tracking With Iterative Ambiguity Smoothing
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Solution Overview
Problem
Conventional GNSS positioning systems face challenges in accurately resolving integer ambiguities due to loss-of-lock, cycle slips, and distance-dependent biases, especially in urban environments and long receiver separations, leading to estimation errors and difficulties in re-initialization.
Innovation Solution
A system and method utilizing iterative feedback smoothing and probabilistic smoothers, such as Fraser-Potter and Rauch-Tung-Striebel frameworks, to update parameters of a probabilistic estimation model, focusing on kinematic states and integer ambiguity biases, improving estimation accuracy through iterative feedback refining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional position estimation methods are used with discrete consecutive time step measurements, then the system can process GNSS signal data, but estimation errors occur due to loss-of-lock, cycle slips, and distance-dependent biases
Solution Approach 1:
The system performs preliminary actions by collecting and storing GNSS measurements over a batch of consecutive time steps before performing position estimation. This allows the system to have all necessary data available before processing, enabling more robust estimation methods that can handle loss-of-lock and cycle slips by utilizing information from multiple time steps.
Solution Approach 2:
The system implements feedback by using the estimated position and state information to refine the measurement model and improve subsequent estimations. The batch processing approach allows the system to feedback information from all measurements in the batch, enabling correction of errors that occur during signal occlusion or cycle slips.
2Adaptability or versatility
If integer ambiguity values are re-determined after signal occlusion or blocking, then the system can recover positioning capability, but this process takes several seconds to minutes causing estimation errors
Solution Approach 1:
The system maintains preliminary information about integer ambiguities and receiver state from previous time steps within the batch. When signal occlusion or blocking occurs, this pre-collected information allows for faster re-determination of ambiguities compared to conventional real-time methods, reducing the time loss during recovery.
Solution Approach 2:
The system dynamically adapts its estimation approach based on the detected signal conditions. When loss-of-lock or cycle slips are detected, the batch processing framework allows the system to switch to methods that leverage historical data from the batch, enabling faster recovery without requiring several seconds to minutes of re-initialization time.
3Area of stationary object
If receiver separation increases to expand coverage area, then the system can serve more locations, but distance-dependent biases grow making reliable ambiguity resolution more challenging
Solution Approach 1:
The system collects GNSS measurements from multiple receivers over a batch of time steps before performing joint processing. This preliminary data collection allows the system to accumulate sufficient information to resolve distance-dependent biases even when receiver separation is large, as the batch framework enables utilization of temporal correlations and redundant measurements.
Solution Approach 2:
The system merges measurements from multiple separated receivers through joint batch processing. By combining data from multiple receivers and multiple time steps, the system creates a more robust estimation that can overcome distance-dependent biases, allowing reliable ambiguity resolution even with large receiver separations to expand coverage area.
Data Source
AI summary
Provided is a system for tracking a state of a receiver. The system receives a set of measurements collected over a period of time, indicative of a motion of the receiver. The system further processes set of measurements relating to the receiver with a probabilistic smoother to estimate a state trajectory of the receiver for the period of time, the set of measurements is associated with a probabilistic estimation model. The probabilistic smoother iteratively implements a smoothing process followed by a feedback refining process for at least some parameters of the probabilistic estimation model until a termination condition is met. The smoothing process updates the state trajectory based on the at least some parameters. The feedback refining process updates the at least some parameters to improve a metric indicative of a measurement likelihood. The system further renders the estimated state trajectory and at least some estimated model parameters.