Indoor Positioning Model via Bundle Optimization
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Solution Overview
Problem
Current indoor positioning systems face challenges such as high costs, power consumption, and inaccuracy due to sensor drift, and require pre-deployment surveys and complex hardware, making them unsuitable for small devices and dynamic environments.
Innovation Solution
A method and system that generate a model of an environment using distance measurements from receiving units to source locations, optimizing source and receiving unit positions through bundle optimization, allowing for accurate location determination without pre-deployment efforts and enabling continuous improvement of the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used for positioning, then outdoor location accuracy is improved, but cost and power consumption increase
Solution Approach 1:
The positioning system is segmented into two parts: GPS for outdoor positioning and a complementary indoor positioning system using wireless access points and signal strength measurement for indoor positioning. This allows the device to use only the necessary system for the current environment, reducing unnecessary power consumption while maintaining location accuracy both indoors and outdoors.
2Adaptability or versatility
If sensor-based pedestrian dead reckoning is used, then positioning is available without GPS, but drift occurs due to sensor inaccuracies
Solution Approach 1:
The patent combines sensor-based pedestrian dead reckoning with wireless access point positioning. The sensor data provides continuous positioning information while wireless access point measurements provide periodic correction references, merging both approaches to maintain positioning availability without significant drift accumulation.
Solution Approach 2:
The system uses wireless access point signal strength measurements as feedback to correct the drift accumulated by sensor-based dead reckoning. By periodically comparing measured signal strengths against a pre-built RF map, the system can identify and correct position estimation errors, maintaining long-term accuracy.
3Measurement precision
If fingerprinting method is used for Wi-Fi positioning, then location performance is improved, but pre-deployment survey effort increases
Solution Approach 1:
The system performs preliminary action by pre-building an RF map of the indoor environment during a one-time survey phase. This map stores the relationship between wireless access point signal strengths and physical locations, enabling accurate positioning without requiring repeated surveys. The preliminary mapping effort is amortized over many subsequent positioning operations.
Solution Approach 2:
The patent creates a digital copy of the physical environment's RF characteristics through the pre-built map. Instead of performing physical surveys repeatedly, the system uses this digital replica to determine positions by comparing current signal measurements against the stored map data, dramatically reducing the time required for positioning operations.
4Ease of manufacture
If RF indoor propagation model with trilateration is used, then positioning can be performed without detailed RF maps, but location accuracy decreases due to wall reflections and noise
Solution Approach 1:
The patent introduces a pre-built RF map as an intermediary between the raw signal strength measurements and the final position calculation. Instead of directly applying trilateration to noisy measurements, the system uses the RF map to interpret signal strengths in the context of the specific building's RF characteristics, including wall locations and reflection patterns, thereby improving accuracy while maintaining deployment simplicity.
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
This approach provides a robust, cost-effective, and accurate indoor positioning system that can be used with low-cost devices, improving location determination accuracy and reducing the need for extensive surveys, enabling continuous model improvement and adaptation.
Implementation Method 1
receiving a distance measurement based on received signal strength (RSS) of a signal transmitted by the source
Implementation Method 2
the model is determined by bundle optimization for determining a position of the receiving unit by minimizing a difference between the distance measurement and a modeled distance
Data Source
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AI summary
A method for generating a model of an environment enabling positioning within the environment is disclosed. The method comprises: receiving distance-dependent measurements relating to a distance between a receiving unit position and a source position; receiving information relating to geographic positions, each defining a receiving unit position or a source position; forming a candidate model, which defines candidate source positions and candidate receiving unit positions; iteratively reducing a model error of said candidate model, wherein said model error comprises a weighted contribution from distance-dependent errors, which are based on a difference between a distance-dependent value between a candidate source position and a candidate receiving unit position and the corresponding distance-dependent measurement, and geographic position errors, which are based on a difference between a geographic position and a corresponding candidate position, and outputting, from said iterative reducing, determined locations for said plurality of sources to form said model of the environment.