Dynamic Kalman Filter Position Calculation for Mobile Devices
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Solution Overview
Problem
Existing position calculation methods using Kalman filters in GPS systems, such as those in cellular phones and car navigation devices, face accuracy issues due to constant changes in movement conditions like stop, rotation, acceleration, and deceleration, leading to phenomena like position lag, lead, and jump.
Innovation Solution
A method that calculates the position of a mobile device using GPS signals and determines its moving condition through sensors like acceleration, angular speed, and azimuth sensors, then adjusts error parameters in the Kalman filter process accordingly to correct the calculated position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed parameters are used in Kalman filter position calculation, then the calculation process is simple, but position accuracy deteriorates due to position lag, position lead, and position jump phenomena
Solution Approach 1:
The patent applies dynamics by transitioning from fixed parameters to dynamic parameters in the Kalman filter. The error parameters are no longer constant but are updated in real-time based on the mobile body's movement condition (stop, rotation, acceleration, deceleration). This dynamic adaptation allows the filter to adjust to changing motion states, eliminating position lag, lead, and jump phenomena while maintaining computational feasibility.
Solution Approach 2:
The patent implements parameter changes by modifying the error parameters of the Kalman filter based on detected movement conditions. When the mobile body undergoes acceleration, deceleration, rotation, or stopping, the system detects these conditions and相应ly adjusts the error parameters. This parameter adaptation directly addresses the accuracy deterioration caused by fixed parameters during dynamic movement.
2Measurement precision
If error parameters are changed according to moving condition, then position calculation accuracy is improved, but device complexity increases due to additional sensors and processing
Solution Approach 1:
The patent applies universality by making the movement condition detection system serve multiple functions. The sensor unit (acceleration sensor, angular speed sensor, azimuth sensor) not only detects movement conditions for parameter adjustment but also provides data for comprehensive position and orientation calculation. This multi-functionality justifies the added complexity by extracting maximum utility from each component.
Solution Approach 2:
The system implements self-service by using its own sensor data to automatically adjust its calculation parameters. The movement condition detection unit feeds information back to the Kalman filter parameter adjustment, creating a self-regulating system that adapts to its own operational state without external intervention. This self-service mechanism reduces the need for additional complex control systems.
Data Source
AI summary
A position calculating method includes: calculating a position of a mobile body based on a positioning signal from a positioning satellite; determining the moving condition of the mobile body based on a detection result of a sensor unit including at least one of an acceleration sensor, an angular speed sensor, and an azimuth sensor; changing an error parameter used for a predetermined Kalman filter process according to the determined moving condition; and correcting the calculated position by the Kalman filter process.