Indoor Positioning Noise Removal via Movement Direction Estimation
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Solution Overview
Problem
Indoor positioning systems face challenges in accuracy due to signal attenuation and scattering by obstacles and lack of a fixed network, leading to noise in position measurements.
Innovation Solution
A method and system for improving indoor positioning accuracy by gathering position samples, estimating movement direction, and identifying and marking noise samples based on direction and velocity criteria, using a processor to filter out inconsistent samples and update movement directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Wi-Fi or BLE signals are used for indoor positioning, then the system can leverage existing networks and provide location tracking, but signal attenuation and scattering by obstacles lead to noise in position measurements
Solution Approach 1:
The system performs preliminary actions by collecting multiple position samples over time and estimating movement direction before making a final positioning determination. This allows the system to pre-process noisy measurements and identify valid position data through trajectory analysis, resolving the contradiction between using existing Wi-Fi networks and achieving accurate positioning.
2Measurement precision
If multiple position samples are collected and processed to remove noise, then positioning accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system segments the position samples into groups based on movement direction consistency. By dividing the data processing into manageable segments (valid samples vs. noise samples) and applying different processing rules to each segment, the system achieves accurate positioning while keeping computational complexity manageable through structured data organization.
3Reliability
If trajectory calculation is performed to clean position measurements, then noise is removed from position samples, but additional processing steps and computational resources are required
Solution Approach 1:
The system performs self-service by using the movement direction information inherent in the position samples themselves to identify and remove noise. Rather than requiring external validation or complex processing, the trajectory calculation leverages the natural temporal and spatial relationships in the data to automatically filter invalid measurements, achieving reliable position data with minimal additional processing time.
Data Source
AI summary
A computerized method of improving position measurement of an indoor positioning system (IPS), comprising: gathering a plurality of position samples of a mobile device measured by an indoor wireless device, each of the position samples is indicative of a measured position and a measurement time; estimating a movement direction of the mobile device from the plurality of position samples; receiving a current position sample of the mobile device; estimating a temp movement direction of the mobile device from at least one of the plurality of position samples and the current position sample; and when the temp movement direction is identified as false, marking the current position sample as noise.