Local Positioning System Kalman Filter Trilateration Accuracy
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Solution Overview
Problem
Conventional local positioning systems face accuracy issues due to performance limitations in ranging techniques, resulting in significant errors in determining the location of objects or personnel, especially with existing chips like Nanoloc™ which have inaccuracies of ±1 meter outdoors and ±2 meters indoors.
Innovation Solution
The system employs a local positioning system with multiple nodes, including a coordinator and anchors, using trilateration and Kalman filtering to enhance the accuracy of range values by averaging multiple distance measurements and filtering out noise, with the Kalman filter assigning weights to measurement samples based on confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ranging techniques (e.g., Nanoloc chip) are used to determine distance, then the system can obtain range measurements, but the measurement precision is poor with errors of ±1 meter outdoors and ±2 meters indoors
Solution Approach 1:
The patent combines multiple range measurements from different nodes into a single position estimate through trilateration. By merging measurements from at least three nodes, the system creates a position sample that benefits from multiple independent measurements, reducing the impact of individual measurement errors.
Solution Approach 2:
The Kalman filter implements a feedback mechanism where the position estimate from the previous time step serves as a prior that is updated with the current position sample. This recursive filtering process continuously refines the position estimate by comparing predicted positions with actual measurements, thereby improving accuracy over time.
2Measurement precision
If multiple distance measurements are taken to improve accuracy, then the positioning precision improves, but the complexity of the system increases due to the need for filtering algorithms
Solution Approach 1:
The Kalman filter is implemented in a self-contained manner where each node independently performs the filtering operations using local computations. The filter uses only the measurement data and its own internal state to produce position estimates, without requiring complex external processing or coordination infrastructure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of position samples by iteratively updating filter estimates, reducing errors and enhancing the overall precision of the system's location tracking capabilities.
Implementation Method 1
The time required to transmit a signal and to receive a reply is measured, and based on such measurement, the time-of-flight between the node and tag can be determined. Using the time-of-flight, the distance between the node and the tag can be calculated.
Data Source
AI summary
A local positioning system uses at least one node to track a location of a mobile tag. The system measures flight times of signals communicated between the node and the tag to determine values indicative of the range of the tag from the node. If desired, the values may be filtered in an effort to increase the accuracy of the range estimation. As an example, a Kalman filtering algorithm may be used. Multiple antennas are used at both the node and the tag to provide more accurate range estimates and to determine when the tag is entering a dead zone where signals are blocked or attenuated by obstacles.