Kernel Machine Estimator for User-Reported Location Filtering
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Solution Overview
Problem
The accuracy of locating objects, such as speed enforcement devices, is hindered by low resolution geographical coordinates, inaccurate user reports, and false locations in systems that rely on user-provided input from mobile devices.
Innovation Solution
A method and system that utilize machine learning to estimate object locations by collecting and processing user-reported geographical coordinates and object types at a remote server, employing a kernel machine estimator trained with data from known object locations and false locations to filter and score user reports, thereby improving the detection of object locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If user-provided input from mobile devices is used to locate objects, then the system can collect location data from multiple sources, but the accuracy is hindered by low resolution geographical coordinates, inaccurate reports, and false locations
Solution Approach 1:
A kernel machine estimator is introduced as an intermediary between raw user reports and final location determination. This statistical model processes multiple user reports, filtering out inaccurate and false locations while identifying the true object location through pattern recognition and probability analysis.
Solution Approach 2:
The system uses feedback from multiple user reports to continuously refine location estimates. By analyzing the distribution and consistency of reports from different users, the system adjusts its determination of object locations, giving more weight to consistent reports and filtering out outliers.
2Productivity
If geographical coordinates from mobile devices are collected, then location data can be gathered from users in the vicinity, but the low resolution of coordinates limits detection precision
Solution Approach 1:
Multiple low-resolution coordinate reports from different users are merged and analyzed collectively. The kernel machine estimator combines these distributed, low-precision data points to infer the precise location of objects, effectively synthesizing collective imprecise data into accurate location information.
Solution Approach 2:
The system transitions from analyzing individual 2D coordinate points to examining the distribution pattern across multiple dimensions (spatial distribution, temporal patterns, user density). This dimensional expansion allows the system to extract precise location information from otherwise low-resolution inputs.
3Adaptability or versatility
If a database of object locations is maintained using user reports, then the database can be dynamically updated, but false locations and inaccurate reports reduce reliability
Solution Approach 1:
The kernel machine estimator performs preliminary statistical analysis on user reports before committing location data to the database. By pre-filtering and validating reports through probability modeling, the system prevents false and inaccurate locations from being stored, ensuring only reliable data enters the database.
Solution Approach 2:
The system implements continuous feedback loops where new user reports are compared against existing database entries and statistical models. This feedback mechanism identifies and corrects false locations while maintaining accurate ones, progressively improving database reliability over time through iterative validation.
Data Source
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AI summary
A method of estimating the geographical location of an object from location information reported by one or more mobile devices having location determining means comprises collecting location data from the one or more mobile devices, preparing the data by selecting features of the data, training an estimator by inputting selected features of the data, and applying the estimator to a map grid to estimate the location of the object. During training of the estimator a kernel machine operates on histograms of distances that are computed with respect to a considered location and the user reports nearby. During application of the estimator, if the estimator assigns a score above a certain threshold a location is marked as the true location of the object. In case multiple locations are predicted, a subsequent clustering merges multiple predictions into one by using the weighted mean.