Sensor Radio Positioning for Indoor Navigation
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Solution Overview
Problem
Mobile devices face challenges in determining their position indoors or in environments where satellite signals are blocked, as traditional navigation systems like GPS are unreliable, and pedestrian dead reckoning methods accumulate errors over time.
Innovation Solution
A Sensor and Radio Positioning (SRP) method that uses radio frequency signals from transmitters with unknown absolute positions to calculate range measurements, combining these with motion sensor data to estimate and correct the device's position in real-time, without relying on predetermined maps or satellite signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pedestrian dead reckoning is used for positioning when satellite signals are blocked, then positioning can be performed in indoor environments, but accuracy deteriorates over time due to error accumulation
Solution Approach 1:
The system uses WiFi range measurements as feedback to continuously correct and reset the accumulated errors from pedestrian dead reckoning. By comparing measured WiFi ranges against expected ranges from the reference map, the system detects drift and adjusts the position estimate accordingly, preventing error accumulation.
Solution Approach 2:
The system changes the operational parameters by switching between two positioning methods: using pedestrian dead reckoning for continuous tracking and WiFi trilateration for periodic correction. This parameter switching allows the system to maintain accuracy by resetting error accumulation through method transitions.
2Measurement precision
If WiFi trilateration with predetermined reference maps is used for positioning, then positioning accuracy is improved, but the system becomes dependent on external map data availability
Solution Approach 1:
The system performs preliminary actions by downloading and storing reference map data containing WiFi access point locations and signal characteristics before entering the indoor environment. This advance preparation ensures that when the device is indoors without satellite signals, the necessary reference data is already available locally for accurate positioning.
Solution Approach 2:
The positioning system is segmented into two independent components: a reference map module that stores predetermined WiFi location data, and a real-time positioning module that uses both the reference map and current sensor/WiFi measurements. This segmentation allows the system to function with or without external connectivity depending on data availability.
3Speed
If sensor-based pedestrian dead reckoning is used, then real-time positioning is achieved, but magnetic disturbances and sensor precision limits reduce reliability
Solution Approach 1:
The system merges two independent positioning approaches: inertial sensor-based pedestrian dead reckoning for real-time continuous tracking, and WiFi signal-based trilateration for periodic accuracy verification. By combining these methods, the system maintains real-time responsiveness while improving reliability through cross-validation and error correction.
Solution Approach 2:
The reference map of WiFi access point locations serves as an intermediary that connects raw WiFi signal measurements to meaningful position estimates. This intermediary structure allows the system to translate unreliable absolute position estimates from sensors into accurate relative position updates by referencing known WiFi landmark locations.
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
Enables accurate, real-time positioning and mapping within environments by correcting sensor-based positioning errors using radio range measurements, reducing the accumulation of errors and providing a standalone positioning system independent of external maps or satellite signals.
Implementation Method 1
receiving a reference radio signal from a transmitter with unknown absolute position, measuring at least one characteristic of the reference radio signal, and calculating a reference range measurement between the mobile device and the transmitter using the at least one characteristic of the reference radio signal
Implementation Method 2
receiving mobile device motion sensor data and estimating a position of the mobile device based on the mobile device motion sensor data
Data Source
AI summary
Method, hardware, device, computer program, and apparatus for positioning mobile devices in unmapped locations based on motion sensor and radio frequency measurements are described. A reference radio signal is received from a transmitter with an unknown absolute position and a reference range measurement is calculated. Mobile device motion sensor data is used to estimate a relative position of the mobile device. A sample radio signal is received from a transmitter with an unknown absolute position and a sample range measurement is calculated. The reference range measurement and the sample range measurement are compared. The estimated motion sensor based position is adjusted according to the result of the comparison.