Particle Filter Direction Vector Generation for Indoor Location
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Solution Overview
Problem
Conventional particle filters used for indoor location estimation in non-moving objects face efficiency issues due to arbitrary particle movement, leading to prolonged convergence times when only the measurement update step is applied.
Innovation Solution
Generating direction vectors for particles based on their similarities with a radio map database, calculating a center of gravity, and performing resampling to determine moving directions and distances, thereby improving location estimation accuracy and reducing convergence time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If particles move in arbitrary direction during measurement update step, then the particle filter can be applied to non-moving objects, but the convergence time is prolonged and efficiency is lowered
Solution Approach 1:
The patent changes the movement behavior parameter of particles from arbitrary movement to directed movement towards the center of gravity. By modifying how particles move (changing their movement parameters), the system achieves faster convergence while maintaining applicability to non-moving objects. The center of gravity is calculated based on similarity values, and particles are directed towards this point, transforming the arbitrary movement into purposeful movement that accelerates convergence.
2Measurement precision
If particles are divided and destroyed through similarity evaluation, then location estimation accuracy is improved, but the computational complexity and time required increase
Solution Approach 1:
The patent applies local quality by directing particle movement based on local similarity information. Instead of uniformly processing all particles, the system calculates the center of gravity based on similarity values and directs particles locally towards this point. This localized approach maintains the accuracy benefits of similarity-based particle division while reducing the overall computational burden by focusing processing efforts where they are most needed.
Data Source
AI summary
Provided is a method of generating direction vectors of particles. The method includes calculating, by a processor, a center of gravity of the particles on the basis of characteristic values of one or more particles in a cluster including the particles, and generating, by the processor, direction vectors of the particles on the basis of the center of gravity.