Early Classification of Intent for Maritime Domains Using Multinomial Hidden Markov Models.

Area of Science:

• Naval Operations and Maritime Security
• Artificial Intelligence and Machine Learning
• Behavioral Analysis in Dynamic Systems

Background:

• Increasing maritime security necessitates advanced threat detection capabilities.
• Current intent recognition methods in naval domains often classify behaviors after they are finalized.
• Dynamic maritime environments present unique challenges for real-time agent behavior analysis.

Purpose of the Study:

• To develop an early classification system for hostile behaviors in dynamic maritime settings.
• To propose a novel method for encoding observable symbols using the rate of change for enhanced intent recognition.
• To implement and evaluate a one-versus-all intent classifier based on multinomial hidden Markov models (HMMs).

Main Methods:

• Utilized multinomial hidden Markov models (HMMs) for behavior classification.
• Developed a novel symbol encoding strategy based on the rate of change of relevant parameters.
• Implemented a one-versus-all classification architecture for intent recognition.
• Tested the system on simulated maritime scenarios involving ram, herd, and block behaviors, plus benign activity.

Main Results:

• The proposed method enables early classification of hostile behaviors, significantly before completion.
• The rate-of-change encoding proved effective for distinguishing between hostile and benign actions.
• The multinomial HMM-based classifier demonstrated performance in identifying specific hostile behaviors.

Conclusions:

• The novel encoding of observable symbols as the rate of change is a key enabler for early intent recognition.
• The developed system offers a promising solution for enhancing maritime security through proactive hostile behavior detection.
• Further research can explore expanding the range of behaviors and environmental complexities.