Indoor Localization via Sensor Fusion and Heuristic Mapping
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Solution Overview
Problem
Conventional localization technologies for indoor environments face challenges such as high energy consumption, satellite disconnections, inaccurate RSSI measurements, short communication ranges, and security concerns, making them unreliable for real-time tracking of mobile devices.
Innovation Solution
The integration of sensor data from gyroscopes, accelerometers, magnetometers, and radios to generate heuristic maps that provide relative RSSI comparisons, enabling accurate and sub-second location updates while accounting for physical obstacles, and using dead reckoning to correct position changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional RSSI technology is used for indoor localization, then the system is simple to implement, but the measurement precision is unacceptably high errors
Solution Approach 1:
The patent combines multiple sensors (accelerometers, gyroscopes, magnetometers) with RSSI measurements to create a sensor fusion system. This merging of different sensing modalities allows the system to achieve high localization accuracy by compensating for the weaknesses of individual sensors while maintaining practical system complexity through integrated processing.
Solution Approach 2:
The patent introduces physical obstacle data as an intermediary element that mediates between raw sensor measurements and final localization results. By incorporating pre-acquired obstacle information into the localization algorithm, the system can disqualify impossible locations and resolve ambiguities, thereby improving measurement precision without proportionally increasing device complexity.
2Productivity
If Wi-Fi triangulation techniques are used, then coverage area is extensive, but update frequency is insufficient for real-time tracking
Solution Approach 1:
The patent implements periodic dead reckoning updates using inertial sensors (accelerometers and gyroscopes) that operate at high frequencies independent of Wi-Fi signal availability. This periodic inertial measurement complements Wi-Fi triangulation, ensuring continuous high-frequency updates even when Wi-Fi signals are weak or unavailable, thereby improving both productivity and reliability.
3Reliability
If GPS is used for outdoor localization, then user experience is acceptable, but energy consumption is high and satellite connections are frequent disconnections indoors
Solution Approach 1:
The patent extracts and utilizes inertial measurement capabilities from the mobile device's built-in sensors (accelerometers, gyroscopes, magnetometers) to create a self-contained localization system that operates independently of external satellite infrastructure. This extraction of localization functionality from GPS-dependent systems enables reliable indoor operation without the high energy consumption and connection issues associated with GPS.
4Productivity
If dead reckoning is used for localization, then update frequency is high, but calibration issues and interference cause failures
Solution Approach 1:
The patent implements feedback mechanisms where Wi-Fi RSSI measurements and physical obstacle data are used to continuously validate and correct dead reckoning position estimates. When discrepancies arise between inertial navigation predictions and sensor-based observations, the system uses obstacle-aware algorithms to disqualify impossible locations and adjust the position estimate, thereby maintaining high update frequency while improving reliability through continuous feedback correction.
Data Source
AI summary
The present disclosure provides methods, systems, and devices for tracking a mobile device in an indoor area or bounded area. A method for tracking a mobile device includes receiving sensor data from a mobile device and generating a heuristic map based on the sensor data describing a set of possible current locations of the mobile device in the bounded area. The method further includes receiving additional sensor data from the mobile device, and determining a change in position of the mobile device based on the additional sensor data. The method further yet includes updating the heuristic map to disqualify a first possible current location from the set of possible current locations, and outputting the updated set of possible current locations for display on a user interface.