Zone-Based Displacement Law for Indoor Localization
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Solution Overview
Problem
Existing methods for locating a device within a three-dimensional space, such as indoors, are not precise enough, especially when GPS is unavailable, and often rely on predefined constraints and particle filters that do not account for biases in sensor measurements.
Innovation Solution
The method employs different displacement laws in various areas of a map, incorporates correction factors for biases in direction and amplitude measurements, and uses a particle filter algorithm to iteratively refine the device's position, with predefined constraints like impassable obstacles to accelerate convergence and a preferred direction to enhance weight assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single displacement law is used for all areas in the map, then the method is simple to implement, but the accuracy of position estimation is reduced
Solution Approach 1:
The patent applies local quality by associating different displacement laws with different zones in the map. Each zone can have its own specific displacement law tailored to the characteristics of that area, thereby improving position estimation accuracy in each local region without requiring a completely complex global system.
Solution Approach 2:
The map is divided into multiple zones, each with its own displacement law. This segmentation allows the system to handle different areas with appropriate local models while maintaining overall system manageability and reducing the complexity burden of having multiple laws.
2Measurement precision
If correction factors for biases are incorporated, then the accuracy of localization improves, but the computational complexity increases
Solution Approach 1:
The particle filter algorithm provides feedback by continuously comparing particle positions with sensor measurements and updating correction factors accordingly. This feedback mechanism allows the system to adaptively correct biases while maintaining computational efficiency through iterative refinement rather than complex one-time calculations.
Solution Approach 2:
The system dynamically adjusts correction factors as parameters based on particle filter results. These parameter changes allow the system to compensate for biases in direction and amplitude measurements without requiring a complete redesign of the computational architecture.
3Adaptability or versatility
If particle filters are used to estimate position, then the method can handle three-dimensional movement, but the convergence speed is slow
Solution Approach 1:
By dividing the space into zones with specific constraints (such as impassable obstacles), the particle filter can converge faster within each local zone. The predefined constraints act as local guides that accelerate convergence while maintaining the ability to handle three-dimensional movement across the entire space.
Solution Approach 2:
The system pre-defines constraints and displacement laws for different zones before execution. This preliminary action allows the particle filter to benefit from pre-computed information during runtime, significantly reducing convergence time while maintaining full three-dimensional localization capability.
Data Source
AI summary
This device localization method includes: - providing a map containing several distinct zones, each distinct zone being associated with a displacement law from a set of several different displacement laws, the displacement law associated with a zone allowing to estimate more precisely, from the same measurements received, the direction and amplitude of the device's displacement when it is located inside this zone than if any of the other displacement laws in the set were used, and - an operation (94) of identifying the zone within which a particle is located, then, - an operation of using, for the update of the coordinates of this particle, the only displacement law associated with the zone identified during the identification operation.