Indoor Positioning Using Particle Filter RSSI Weighting
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Solution Overview
Problem
Indoor positioning technologies using received signal strength indicator (RSSI) face challenges due to accumulated errors and instability caused by factors like signal multi-path effects and radio wave interference, leading to decreased accuracy in location estimation.
Innovation Solution
A particle filter-based method for indoor positioning that initializes particles in a base map, measures distance values between terminals and beacons, calculates particle weights, updates weights, resamples particles, and derives location estimation coordinates, while also using proximity weights and a Kalman filter to preprocess RSSI information and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If RSSI-based positioning method is used, then location estimation is simple, but positioning accuracy deteriorates due to signal interference and multi-path effects
Solution Approach 1:
The patent combines multiple positioning methods (RSSI-based positioning and dead-reckoning using inertial sensors) into a unified particle filter framework. This merging allows the system to leverage the simplicity of RSSI while compensating for its accuracy issues through inertial sensor data, resolving the contradiction between ease of operation and measurement precision.
Solution Approach 2:
The patent creates a composite positioning solution by integrating heterogeneous data sources (RSSI values and inertial sensor measurements) into a particle filter model. This composite approach combines the advantages of different positioning methods while mitigating their individual weaknesses, achieving both simplicity and accuracy.
2Reliability
If particle filter is used to improve positioning accuracy, then computation complexity increases, but positioning stability improves
Solution Approach 1:
The patent segments the particle filter implementation into distinct modules: particle initialization, weight calculation based on RSSI and inertial data, resampling operations, and coordinate derivation. This segmentation makes the complex computation more manageable and implementable while maintaining positioning stability.
Solution Approach 2:
The patent performs preliminary actions by pre-initializing particles in the base map and pre-establishing the particle filter structure before actual positioning begins. This preliminary setup reduces real-time computation complexity while maintaining positioning stability during operation.
3Reliability
If dead-reckoning is used to track location, then positioning can be performed without signal interference, but accumulated error increases over time
Solution Approach 1:
The patent implements feedback mechanisms where the particle filter continuously updates particle weights based on new RSSI measurements and inertial sensor data. This feedback loop corrects accumulated errors from dead-reckoning by comparing estimated positions with sensor-based position updates, maintaining accuracy over time while retaining resistance to signal interference.
Data Source
AI summary
Provided are an indoor positioning method of a positioning apparatus using a particle filter based on an RSSI includes initializing a particle in a base map corresponding to a target space for positioning, measuring a first distance value between a terminal and a beacon and a second distance value between the particle and the beacon, calculating a weight of the particle by comparing the first distance value and the second distance value, updating a previous weight of the particle based on the weight and resampling the particle, deriving location estimation coordinates of the terminal if the resampling has been performed as many as a given number of times, and moving the particle to a given point in the target space and repeatedly measuring the first distance value and the second distance value again.