Domain-Agnostic Multiple Hypothesis Tracking System
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Solution Overview
Problem
Existing multiple hypothesis tracking methods are limited to specific domains and cannot be effectively used across multiple types of domains, preventing their application in alternative domains or in cyber security contexts.
Innovation Solution
A domain-agnostic multiple hypothesis tracking system and method that receives observations from various domain types, distributes them to association engines, updates track hypothesis models, and selects hypotheses based on probability estimates, allowing for domain-agnostic processing and communication of track information without domain-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing multiple hypothesis tracking methods are used, then tracking accuracy is improved, but domain adaptability deteriorates (limited to specific domains only)
Solution Approach 1:
The patent creates a universal multiple hypothesis tracking system that can process observations from any domain type (radar, cyber security, acoustics, etc.) through a common architecture. The system uses domain-agnostic data structures and processing algorithms that work across different observation types, allowing the same tracking methodology to be applied universally while maintaining domain-specific performance requirements.
Solution Approach 2:
The patent changes the parameters and data structures used in tracking systems to be domain-independent. Instead of domain-specific parameters, the system uses generic observation attributes (timestamp, position, velocity, quality metrics) that can represent any type of observation. This parameter transformation enables the system to maintain high tracking accuracy across diverse domains by adapting to different observation characteristics through configurable parameters rather than domain-hardcoded logic.
2Reliability
If domain-specific tracking systems are created for each domain, then domain performance is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple domain-specific tracking systems into a single unified multiple hypothesis tracking system. By combining the core tracking functionality into one system that handles all domains, the patent reduces overall system complexity while maintaining domain performance through configurable parameters and domain-adapted association engines. This consolidation eliminates redundancy and enables shared resources across domains.
Solution Approach 2:
The patent segments the tracking system into modular components: a domain-agnostic core hypothesis manager, domain-specific association engines, and configurable parameter sets. This segmentation allows each component to be optimized independently while working together in a unified framework, maintaining high domain performance without requiring complete domain-specific systems.
3Adaptability or versatility
If multiple domain types are processed simultaneously, then versatility is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by allowing different processing strategies for different domains while using a common core framework. Each domain can have customized association rules, parameter weights, and hypothesis evaluation criteria tailored to its specific requirements, while the overall system architecture remains standardized. This enables multi-domain versatility without requiring complex custom processing for each domain.
Data Source
AI summary
Embodiments described herein are directed to multiple hypothesis systems and methods for tracking observations that are domain agnostic and involves determining the probability that a given set of observations (i.e., a track) corresponds to a particular target, object or linked set of events. One embodiment described herein relates to cyber security tracking methods and systems.