Indoor Positioning Weighted Sample Estimation
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Solution Overview
Problem
Indoor positioning systems face challenges in accuracy due to signal attenuation and scattering by obstacles and the lack of a fixed network, leading to inherent complexity and lower accuracy compared to outdoor positioning.
Innovation Solution
The method involves weighting indoor positioning system position samples based on time and signal strength, with newer samples and stronger signal samples given higher weights, and removing noise samples by estimating movement velocity, to improve positioning accuracy.
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 wireless networks without requiring additional infrastructure, but the positioning accuracy deteriorates due to signal attenuation and scattering by obstacles
Solution Approach 1:
The patent combines multiple position samples collected over time into a single estimated position using weighted averaging. This merges redundant measurements to improve accuracy while still using the same Wi-Fi/BLE infrastructure, resolving the contradiction between using existing networks and achieving acceptable positioning precision.
Solution Approach 2:
The system performs preliminary data collection and processing by gathering multiple position samples before generating the final position estimate. This preliminary action of collecting and weighting samples improves measurement precision without requiring changes to the underlying wireless positioning infrastructure.
2Measurement precision
If multiple position samples are collected and processed with weighting algorithms, then positioning accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies different weights to different position samples based on their individual qualities (signal strength, time recency). This local quality approach improves overall measurement precision by emphasizing high-quality samples while minimizing the impact of low-quality ones, without requiring equally complex processing for all samples.
Solution Approach 2:
The system changes parameters such as weight factors, time windows, and velocity thresholds to optimize the balance between accuracy and computational complexity. By adjusting these parameters, the system can achieve good positioning accuracy while controlling the level of computational processing required.
3Measurement precision
If position samples are smoothed using weighted averaging, then measurement noise is reduced, but the system may lose responsiveness to actual position changes
Solution Approach 1:
The patent dynamically adjusts the weighting of position samples based on their recency and quality. More recent samples receive higher weights, allowing the system to respond faster to actual position changes while still smoothing out noise. This dynamic weighting resolves the contradiction between noise reduction and responsiveness.
Solution Approach 2:
The system uses velocity estimation as feedback to detect when the mobile device is moving. When movement is detected, the system can adjust its smoothing behavior to maintain responsiveness. This feedback mechanism ensures that noise reduction does not come at the cost of losing track of actual position changes.
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; receiving a current position sample of the mobile device; for each of the plurality of position samples and the current position sample, determining a time weight, the time weight is higher for samples measured at a later time; for each of the plurality of position samples and the current position sample, determining a signal weight, the signal weight is higher for measured with stronger signal; and estimating a current position of the mobile device by weighting at least some of the position samples with the respective weights.