Maritime surveillance of large volume traffic demands robust and scalable network architectures for distributed
information fusion. Operating in an adverse and unpredictable environment, the ability to flexibly adapt to
dynamic changes in the availability of mobile resources and the services they provide is critical for the success
of the surveillance and rescue missions. We present here an extended and enhanced version of the Dynamic
Resource Configuration $ Management Architecture (DRCMA), with new features and improved algorithms
to better address the adaptability requirements of such a resource network. The DRCMA system concept is
described in abstract functional and operational terms based on the Abstract State Machine (ASM) paradigm
and the CoreSM open source tool environment for modeling dynamic properties of distributed systems.
We propose a highly adaptive and auto-configurable, multi-layer network architecture for distributed information
fusion to address large volume surveillance challenges, assuming a multitude of different sensor types on multiple
mobile platforms for intelligence, surveillance and reconnaissance. Our focus is on network enabled operations
to efficiently manage and improve employment of a set of mobile resources, their information fusion engines
and networking capabilities under dynamically changing and essentially unpredictable conditions. A high-level
model of the proposed architecture is formally described in abstract functional and operational terms based on
the Abstract State Machine formalism. This description of the underlying design concepts provides a concise and
precise blueprint for reasoning about key system attributes at an intuitive level of understanding.