Indoor Terminal Localization Using RSS Probability Mapping
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Solution Overview
Problem
Existing indoor localization methods are imprecise due to environmental changes and hardware variations, requiring complex training and database updates, and are sensitive to node failures, leading to inefficient and costly implementations.
Innovation Solution
A localization method using a plurality of antennas to measure received signal strength (RSS) and apply a path-loss model to generate a probability distribution of terminal positions, with auto-calibration to adjust for environmental changes, without needing extensive training or databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If deterministic methods with RSS comparison and databases are used, then localization can be performed, but measurement precision deteriorates due to environmental conditions and hardware variations
Solution Approach 1:
The patent changes the fundamental parameter used for localization from raw RSS values to Time of Flight (ToF) measurements. By converting RSS to ToF using the path-loss model, the system transforms the measurement into a physical time parameter that is less sensitive to environmental variations and hardware differences, thereby improving measurement precision while maintaining operational ease.
Solution Approach 2:
The patent replaces the traditional RSS-based deterministic localization mechanism with a ToF-based mechanism. This substitution involves using the time delay of electromagnetic signals rather than signal strength comparison, which fundamentally changes how localization is achieved and reduces sensitivity to environmental and hardware variations.
2Measurement precision
If automatic learning methods with training steps are used, then localization accuracy can be improved, but device complexity and installation cost increase due to training requirements
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing path-loss model parameters for different environmental zones before actual localization occurs. This preliminary preparation eliminates the need for complex real-time training, as the system only needs to retrieve pre-computed parameters during operation, significantly reducing device complexity while maintaining accuracy.
Solution Approach 2:
The patent creates simplified copies of environmental characteristics through pre-computed path-loss models for each zone, rather than requiring complex training data. These models capture the essential propagation characteristics as simplified mathematical representations, avoiding the need for extensive training procedures while preserving localization accuracy.
3Measurement precision
If training is performed when environmental conditions change or hardware is modified, then localization accuracy can be maintained, but loss of time increases due to retraining requirements
Solution Approach 1:
The patent introduces dynamic adaptation by allowing the system to automatically detect environmental changes and update path-loss models only when necessary, rather than requiring periodic retraining. The system dynamically adjusts to new conditions by comparing current measurements with stored models and updating only affected zones, significantly reducing time loss while maintaining accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors localization performance and environmental conditions. When changes are detected, the feedback loop triggers selective model updates based on the feedback information, allowing the system to adapt to changes without requiring comprehensive retraining, thus reducing time loss while preserving accuracy.
4Ease of operation
If model-based supervised learning is used, then localization can be performed, but reliability deteriorates due to sensitivity to node failures
Solution Approach 1:
The patent segments the localization system into independent zone-based path-loss models rather than using a single global model. Each zone has its own pre-computed parameters, so when a node fails or environmental conditions change in one zone, only that specific zone's model needs updating or replacement, not the entire system. This segmentation maintains reliability by isolating failures to local areas.
Solution Approach 2:
The patent prepares for node failures beforehand by pre-computing and storing path-loss models for multiple zones with redundant information. This prior cushioning ensures that when a node fails, the system can immediately switch to alternative zones or use pre-stored backup parameters, maintaining localization functionality without requiring complex real-time recovery procedures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate and flexible localization with reduced installation costs and labor, maintaining precision even with changing environments and hardware configurations.
Implementation Method 1
For each antenna, a measurement is made of the received strength signal, RSS, representative of a strength of the identification signal as it is acquired by that antenna
Implementation Method 2
A probability distribution of a position of the terminal with respect to each antenna is generated as a function of the RSS acquired by the antennas by applying a path-loss model
Data Source
AI summary
A method for localizing terminals is carried out by preparing a plurality of antennas at distinct points of an area to be monitored and acquiring by means of said antennas an identification signal uniquely associated with a terminal present within the area to be monitored. For each antenna, a measurement is made of a received strength signal, RSS, representative of a strength of the identification signal acquired by the antennas and a probability distribution of a position of the terminal with respect to each antenna is generated as a function of the respective RSS. The position of the terminal is determined by maximizing the probability distribution.