Bayesian Source Identification Using Precomputed Probability Density Functions
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Solution Overview
Problem
Existing methods fail to accurately determine the source of data sampled from multiple sources, particularly in real-time, due to the lack of effective tools for comparing real-time data with prior data from known sources to assign probabilities effectively.
Innovation Solution
A method using Bayesian theory to calculate probability density functions and iteratively update probabilities based on prior observed data from known sources, allowing for the determination of the likelihood that sampled data originates from one of these sources, utilizing sensors and mathematical manipulation to reduce noise and enhance discrimination between sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bayesian theory is applied to calculate probability density functions for source identification, then source determination accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary calculations of probability density functions during training phases before actual source identification is needed. By pre-computing these functions and storing them for reference, the system reduces the computational burden during real-time operation while maintaining high accuracy in source determination.
2Reliability
If iterative probability calculation is performed for each data set, then source identification reliability is improved, but processing time increases
Solution Approach 1:
The system uses iterative probability calculations with feedback mechanisms where each calculation refines the source identification based on previous results. The feedback loop allows the system to converge on the most likely source while providing reliability metrics, balancing thorough analysis with efficient processing through adaptive stopping criteria.
Data Source
AI summary
The present invention provides a method and apparatus for determining the probability that sampled data, associated with a source and obtained from a plurality of data input sources, are from a known source, given prior observed data obtained by the plurality of data input sources for one or more known sources. In one embodiment the data input sources are sensors for detecting molecules conveyed though the air and the method comprises identifying a source of the molecules. The present invention also provides a method of determining information about the position of at least one sensor relative to a previously known type of source.