Wireless Tracking System Using Sensor Fusion for Indoor Positioning
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Solution Overview
Problem
Existing wireless tracking systems face challenges in accurately determining the location of mobile devices indoors and in environments with obstacles, as radio frequency radiation propagation is affected by building structures and other obstacles, leading to inaccurate location estimates due to non-line-of-sight and multipath propagation.
Innovation Solution
The system employs sensor fusion and environment learning to improve the accuracy of time-based positioning by combining timing measurements with complementary sensor data from inertial sensors, video cameras, laser range finders, and radar, using historical location data and error data to correct for propagation path errors, and storing this data for future reference to enhance real-time positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-based positioning using radio frequency radiation is used, then location estimation can be performed, but accuracy deteriorates in environments with obstacles and building structures due to non-line-of-sight and multipath propagation
Solution Approach 1:
The system performs preliminary actions by collecting timing measurements and sensor data over a period of time to establish historical location data and error characteristics before actual positioning is needed. This pre-collected data is stored and later used to correct real-time positioning errors, effectively preparing the system in advance to handle NLOS and multipath conditions.
Solution Approach 2:
The system implements feedback by using collected sensor data and timing measurements to identify error patterns in location estimation. These identified errors are then fed back into the positioning algorithm to correct future location estimates, continuously improving accuracy by learning from past measurement errors caused by NLOS and multipath propagation.
2Measurement precision
If multiple sensors and historical data processing are used to improve positioning accuracy, then location determination accuracy improves in disturbed environments, but device complexity and computational requirements increase
Solution Approach 1:
The system applies universality by using a multi-functional approach where the same collected data serves multiple purposes: timing measurements are used for both distance calculation and error pattern identification, sensor data serves both for immediate positioning and for training the error model. This multi-functionality reduces the need for separate dedicated components for each function.
Solution Approach 2:
The system performs preliminary data collection and processing to build error models and historical location data before real-time positioning is required. By pre-processing and storing this information, the system reduces the computational burden during actual positioning operations, as the heavy lifting of error pattern recognition has already been done in advance.
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 significantly improves the accuracy of location determination in disturbed environments by learning from past positions and using this information to correct real-time positioning, even in areas with previously visited nodes, reducing computational complexity and storage requirements.
Implementation Method 1
determining a propagation time of the radiation from the stationary device... Using the speed of light of 300,000 km/s as the propagation velocity, the mobile device determines a first distance from the first stationary device
Implementation Method 2
Receiving sensor data from one or more inertial sensors indicative of movement of the first mobile device
Data Source
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AI summary
A wireless tracking system comprises mobile devices and stationary devices at known locations. The system receives a first radio measurement indicative of a first propagation path length and receives sensor data indicative of the movement of the first mobile device. The sensor data is of a type different to the radio measurement. The system then determines a historical location of the first mobile device based on the first radio measurement and the sensor data and determines error data indicative of a difference between a historical radio measurement and the historical location and stores the error data associated with the historical location. The system can then receive a second radio measurement indicative of a second propagation path length of radio frequency radiation and determine an estimated location of the second mobile device based on the second radio measurement and the stored error data.