Wireless Location Tracker Filtering Aberrant Samples
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Solution Overview
Problem
Existing wireless location technologies (WILOTs) face inaccuracies and unreliability due to errors in location determination, particularly with trilateration and multilateration methods, and inertial navigators, which result in large regions of uncertainty and erratic location readings.
Innovation Solution
A Discriminating Tracker system that processes data from WILOTs to identify and correct outlier locations by using a processor to determine expected locations and variances, and optionally acquiring additional locations from other WILOTs or GPS systems to validate the accuracy of the target's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trilateration or multilateration methods are used to determine location, then location can be obtained using existing wireless infrastructure, but the location accuracy is poor due to large regions of uncertainty
Solution Approach 1:
The system uses feedback by comparing each new location reading with previously determined locations to identify improbable samples. When a location reading falls outside the expected region of uncertainty based on historical data, it is flagged as improbable and excluded from the final location determination, thereby improving accuracy and reliability
Solution Approach 2:
The system dynamically adjusts the parameters used for location determination by calculating a region of uncertainty based on the standard deviation of previous location readings. This adaptive parameter adjustment allows the system to account for varying signal conditions and improve location accuracy in different environments
2Reliability
If additional WILOTs or GPS systems are used to validate location accuracy, then location reliability improves, but system complexity and cost increase
Solution Approach 1:
The system performs self-validation by using its own historical location data to establish expected regions of uncertainty. Each location reading is automatically evaluated against this expected region, and improbable samples are excluded without requiring external validation systems, thereby maintaining reliability while avoiding increased complexity
Solution Approach 2:
The system performs preliminary action by pre-calculating the region of uncertainty based on historical location data before new location readings are obtained. This allows for real-time filtering of improbable samples without adding complexity to the measurement process itself
Data Source
AI summary
An embodiment of the invention provides a method of determining a location of a mobile target that processes locations for the target provided by a wireless location technology tracker system to determine moving averages of velocity of the target, determines if the locations are outliers responsive to the moving averages, discards locations that are determined to be outliers, and uses locations determined not to be outliers as locations for the target.