Dynamic Signal Filter for Irregular Wireless Updates
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Solution Overview
Problem
Existing wireless networking systems face inaccuracies in estimating distance, motion, and location due to signal strength variance and irregular message transmission intervals, leading to noise in signal attenuation through mediums like air or water.
Innovation Solution
A dynamically windowed filter is used to weight and decay signal strength values, reducing noise by considering a dynamic time window and elapsed time between signal samples, allowing for more accurate signal strength estimation and movement tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If signal strength measurements are taken at irregular intervals, then the system can adapt to variable transmission conditions, but the measurement precision deteriorates due to noise and variance
Solution Approach 1:
The patent applies a dynamically windowed filter that adjusts the time window size based on the elapsed time between consecutive signal samples. When samples arrive at irregular intervals, the filter dynamically modifies its parameters to maintain optimal filtering performance, thereby preserving measurement precision while accommodating variable transmission conditions
Solution Approach 2:
The filter changes its operational parameters (time window size, decay factors) based on the elapsed time between samples. By adjusting these parameters dynamically, the system maintains accurate signal strength estimation despite irregular sampling intervals, resolving the contradiction between adaptability and measurement precision
2Device complexity
If a fixed time window is used for filtering, then the system is simple to implement, but it cannot handle irregular update intervals effectively
Solution Approach 1:
The patent transforms the fixed time window into a dynamic structure that automatically adjusts based on the elapsed time between consecutive samples. This dynamic adaptation allows the filter to handle irregular update intervals effectively while maintaining relatively simple implementation through straightforward parameter adjustments
Solution Approach 2:
The filter uses feedback from the elapsed time between samples to adjust its time window parameters. This feedback mechanism enables the system to adapt to irregular update intervals without requiring complex external control, maintaining simplicity while improving adaptability
3Speed
If signal strength values are used directly without filtering, then the response time is fast, but the noise in the signal reduces the reliability of distance and location estimates
Solution Approach 1:
The dynamically windowed filter provides adaptive noise reduction that maintains responsiveness. By adjusting the time window based on sample intervals, the filter removes noise without introducing excessive delay, thereby maintaining both speed and reliability in distance and location estimation
Solution Approach 2:
The filter changes its processing parameters based on the timing characteristics of incoming samples. This dynamic parameter adjustment allows the system to maintain fast response while effectively filtering noise, improving the reliability of estimates without significant speed penalty
Data Source
AI summary
A technique for determining a received signal strength from multiple messages filters noise from the received signal to provide an accurate signal strength value. Advantageously, the more accurate output signal strength value can be used to identify movement of a station as well as estimate locations and direction of movement.