Kalman Filter Location Estimation for Indoor Positioning Accuracy
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Solution Overview
Problem
Existing location estimation systems, such as GPS, become inaccurate when used indoors due to the inability of GPS signals to penetrate structures, leading to a need for systems that maintain positional accuracy within buildings and other enclosed spaces.
Innovation Solution
A location determination system utilizing a high-speed Kalman tracking device that combines data from sensors like gyroscopes, accelerometers, magnetometers, ultra-wideband position systems, and optional GPS, with multiple Kalman filters to achieve precise three-dimensional location estimation, even in environments where GPS signals are unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS receivers are used for location estimation, then location estimation functionality is provided, but positional accuracy deteriorates when GPS signals cannot reach the receiver (e.g., indoors)
Solution Approach 1:
The patent introduces intermediary technologies (accelerometers, magnetometers, ultra-wideband systems) that can operate independently of GPS signals. These intermediaries provide location estimation capabilities indoors by measuring device orientation, magnetic field direction, and distance to anchors, thereby maintaining reliability when GPS is unavailable.
Solution Approach 2:
The patent merges multiple location estimation technologies into a unified system. By combining GPS receivers with accelerometers, magnetometers, and ultra-wideband systems, the system can switch between or integrate different methods based on availability, maintaining both reliability (GPS when available) and accuracy (multi-method integration when GPS is unavailable).
2Adaptability or versatility
If accelerometers and magnetometers are used to supplement GPS data, then location estimation continues indoors, but positional accuracy rapidly deteriorates
Solution Approach 1:
The patent implements feedback mechanisms through Kalman filters that continuously refine location estimates based on sensor data. The system uses feedback from accelerometer and magnetometer readings to correct drift and maintain accuracy over time, rather than relying on single-use measurements that would rapidly deteriorate.
Solution Approach 2:
The patent performs preliminary calibration and initialization using ultra-wideband anchors to establish accurate initial position and orientation before indoor movement begins. This preliminary action creates a stable reference frame that reduces the rate of accuracy deterioration during subsequent indoor navigation.
3Measurement precision
If multiple sensors and filtering systems are integrated, then positional accuracy is maintained indoors, but device complexity increases
Solution Approach 1:
The patent designs a universal processing platform that handles multiple sensor types (GPS, accelerometer, magnetometer, ultra-wideband) through a common Kalman filter architecture. This multi-functional approach maintains high positional accuracy while managing complexity by using a single unified processing framework rather than separate specialized systems.
Data Source
AI summary
A location estimation system includes a plurality of Kalman filters, a UWB position system, a pressure sensor, a temperature sensor and a MEMs chip that provides gyroscope, accelerometer and magnetometer information. The data is Kalman filtered to determine precise location information that is more precise any sensor that is processed to determine the probably location of a device.