Multilateration Position Resolution via Pre-Filtered Kalman Estimation
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Solution Overview
Problem
Current satellite position determination methods face challenges in achieving high accuracy due to inaccuracies in Earth's orientation models caused by gravitational influences and other celestial factors, leading to position errors of several feet even with small time inaccuracies.
Innovation Solution
The implementation of a system that includes a pre-filter to reduce covariance of measurement data input to a Kalman filter, utilizing a low-order polynomial filter and fuzzy genetic learning automata to improve the precision of satellite position determination, which enhances the accuracy of satellite position and subsequently user positioning on the ground to centimeter-level accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional satellite position determination methods are used, then the system is simple to operate, but the position accuracy deteriorates to several feet due to Earth orientation model inaccuracies
Solution Approach 1:
The filtering system is segmented into multiple specialized filters: a pre-filter for initial data processing, a low-order polynomial filter for trend removal, and a Kalman filter for optimal state estimation. Each filter handles specific aspects of the measurement data, allowing the system to achieve high accuracy while maintaining manageable complexity through functional decomposition
Solution Approach 2:
A pre-filter is applied before the main Kalman filter to perform preliminary processing on the measurement data. This pre-filter reduces covariance and prepares the data by removing obvious errors and trends, allowing the subsequent Kalman filter to focus on refined estimation and achieve better accuracy with reduced computational burden
2Measurement precision
If measurement data is processed without pre-filtering, then the processing is faster, but the covariance of measurement data increases leading to lower precision
Solution Approach 1:
The pre-filter performs preliminary processing on measurement data before it enters the main Kalman filter. This includes removing trends using polynomial fitting and reducing covariance, which prepares the data for more efficient and accurate processing in subsequent stages, ultimately reducing the computational burden and processing time of the main filter
Solution Approach 2:
The system applies a low-order polynomial filter that removes only the necessary trends and anomalies from the data, rather than performing exhaustive processing. This partial action is sufficient to reduce covariance and improve precision without requiring excessive processing time, achieving the right balance between speed and accuracy
3Measurement precision
If Earth orientation models are improved to account for gravitational influences, then satellite position accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The Kalman filter acts as an intermediary that processes measurement data and Earth orientation model information to produce accurate satellite position estimates. It mediates between the raw measurements and the final position determination, incorporating gravitational influences and other celestial factors through its state transition and measurement models without requiring direct complex calculations in the user equipment
Solution Approach 2:
The system changes parameters in the filtering process, such as adjusting the covariance matrices and filter coefficients, to optimize the balance between measurement data and Earth orientation model predictions. This allows the system to account for gravitational influences and improve accuracy while maintaining computational efficiency through parameter optimization rather than increased structural complexity
Data Source
AI summary
Systems, devices, methods, and computer-readable media for improved location determination of an orbiting device. A method can include receiving, at a transceiver of a device, measurement data from a monitor device, the measurement data representative of a physical state of a mobile object, filtering, using a first of a plurality of first filters of the device, the measurement data based on a character parameter of a state transition matrix representative of the physical state resulting in filtered measurement data, filtering, using a Kalman filter, the filtered measurement data resulting in further filtered measurement data, and providing, by the transceiver, the further filtered measurement data.