IMM Positioning Data Filtering for Indoor Accuracy and Robustness
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Solution Overview
Problem
Indoor positioning technologies suffer from inaccuracies and instability due to signal obstructions and poor synchronization, leading to unsatisfactory improvements in accuracy and robustness using existing filtering methods.
Innovation Solution
An interacting multiple model (IMM) composed of a first-order Kalman filter and a constant velocity Kalman filter is used to filter positioning data, combining the filtering results of these two filters to enhance accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single filter (mean filter or Kalman filter) is used to filter positioning data, then some filtering effect is achieved, but the accuracy and robustness of positioning are not satisfactory
Solution Approach 1:
The patent combines multiple filtering algorithms (mean filter and Kalman filter) into a unified filtering module that processes positioning data through both filters and integrates their results. This merging approach allows the system to leverage the strengths of each filter while compensating for their individual weaknesses, thereby simultaneously improving positioning accuracy and robustness.
Solution Approach 2:
The filtering module acts as a composite filtering system that integrates different filtering methodologies. By combining the simple averaging approach of the mean filter with the predictive capabilities of the Kalman filter, the system creates a composite filtering solution that achieves both high accuracy and strong robustness against signal obstructions and synchronization issues.
2Measurement precision
If existing filtering methods are used to improve positioning accuracy, then some improvement is achieved, but robustness against signal obstructions and synchronization issues remains insufficient
Solution Approach 1:
The system merges multiple filtering approaches to create a resilient filtering module that can handle various adverse conditions. By processing positioning data through both mean filtering and Kalman filtering simultaneously, the system achieves improved robustness against signal obstructions and synchronization problems while maintaining high positioning accuracy.
Solution Approach 2:
The filtering module dynamically adjusts filtering parameters and weights based on signal quality and environmental conditions. This adaptive parameter adjustment allows the system to optimize its filtering performance in real-time, effectively mitigating the impact of signal obstructions and synchronization issues while maintaining accurate positioning.
Data Source
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AI summary
The present invention relates to a method and apparatus for filtering positioning data, the method comprising: receiving positioning data outputted at a current moment by a positioning engine; and using an interacting multiple model (IMM) composed of two different filters to filter positioning data to be processed that is based on the received positioning data, to obtain filtered positioning data. Using the method and apparatus, the accuracy and robustness of positioning can be improved.