Touch-Based Tracking System for Urban Target Probability
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Solution Overview
Problem
Target tracking in complex urban environments is challenging due to obstructions, human variability, and the integration of hard and soft data, which often results in incomplete and unreadable outputs.
Innovation Solution
A touch-based tracking system that combines human and non-human sensor data using a Bayesian probability algorithm, allowing for real-time data fusion and presentation through a graphical user interface, enabling flexible target tracking in various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human observations and automated sensor data are combined in complex urban environments, then tracking coverage and data sources are improved, but data fusion complexity and output readability deteriorate
Solution Approach 1:
The patent introduces a Bayesian probability algorithm as an intermediary that processes and fuses human observations with automated sensor data. This algorithm acts as a mediator that transforms complex multi-source data into coherent probability distributions, resolving the contradiction by providing a systematic method to handle data fusion complexity while maintaining comprehensive tracking coverage across urban environments with obstructions and varying signal penetration characteristics.
2Reliability
If multiple data sources are integrated to track targets in urban environments, then tracking reliability is improved, but output readability and ease of comprehension deteriorate
Solution Approach 1:
The patent transforms multi-source tracking data into probability distribution parameters that represent target location uncertainty. By changing the representation from raw sensor data and human observations to standardized probability distributions, the system maintains high tracking reliability through data fusion while presenting results in a readable, comprehensible format that shows likelihoods and confidence levels rather than complex raw data streams.
3Measurement precision
If hard data from sensors and soft data from human observations are fused, then measurement precision is improved, but system complexity and difficulty of integration worsen
Solution Approach 1:
The patent merges hard sensor data and soft human observation data into a unified Bayesian probability framework. This merging approach allows both data types to contribute to target location estimation with appropriate weighting based on their respective reliabilities, improving measurement precision while managing integration complexity through a single coherent mathematical model rather than separate processing systems.
Data Source
AI summary
A touch-based tracking method comprises starting a GUI which displays an environment; observing at least one of the presence or absence of one or more targets in relation to features in the environment; when an observation is made, reporting the observation through the GUI to form an input; reporting the observation in the GUI with a hand gesture; applying an algorithm to convert the input into a probability distribution; and updating a target state estimate and alters the environment display. The environment may be an area or a map, and the map may include a plurality of features, e.g. roads, building structures, forest, and water. The observation indicates the presence or non-presence of the one or more targets. The hand gesture is made on the map, such as a swiping motion with one or more fingers on the GUI, wherein the hand gesture indicates the strength of the observation.