Mu-Cheng Wang

System of systems for autonomous mission decisions

Manpower is driving up DoD costs while DoD budgets are shrinking; therefore the DoD customer seeks a solution to enable increased mission capacity along with staff reductions. One way to accomplish this is through an autonomous capability to support end-to-end mission activities. The Mission Information Autonomous Intelligent Decision Engine (MiAide) approach for meeting this objective is to create a system of systems (SoS) that work together to enable automation of tasks previously done by humans. MiAide is built upon a novel, but standards-based architecture, that equips MiAide for real end-to-end DoD mission scenarios using a context switching decision engine that distributes actionable information for each specific mission phase to an intelligent mission console (IMC) user interface. The IMC provides secure and high quality communications between mobile end-users and the Mission Operation Center, and it enables graceful handover between operators of different mission phases. MiAide has been demonstrated for aspects of both manned and Unmanned Air Systems (UASs) and proven to reduce staffing while improving mission capacity (e.g., multiply number of missions and mission functions) across all stages of the mission life cycle.

A policy-based approach to automated data reduction for intelligence, surveillance, and reconnaissance systems

Department of Defense (DoD) intelligence, surveillance, and reconnaissance (ISR) assets; including manned and unmanned airborne, space-borne, maritime, and terrestrial systems; play critical roles in support of current and future military operations. However, military services and defense agencies face an ever growing challenge of effectively processing, exploiting, and disseminating ISR data from multiple, diverse senor platforms for end-users who collaborate and share information within a net-centric enterprise environment. Adding to the physical limitations of transport and infrastructure are personnel shortages with respect to the number of operator and analytical staff possessing the required skill sets to effectively exploit collected ISR information. This shortage raises the risk that important information may not be available to war fighters in a timely manner that assures mission success. The Multifactor Analytics Information Engine (MAIE) directly addresses the aforementioned issues by reducing the flood of sensor data to only actionable information that is directly applicable to the mission-at-hand. MAIE focuses on target discovery, communication capacity management, and automation techniques that enable ISR system operators and analysts to derive the knowledge they need to meet end-user mission requirements. A primary feature of the MAIE approach is the use of on-board processing, close to the sensor on the platform where the data originates. This processing includes screening and then compressing the data using established algorithms before transmission to the operations center for dissemination and exploitation. A major contribution of MAIE is its novel approach for automatically selecting these algorithms based on premeditated mission plans and dynamically occurring mission events. We implement a policy-based management system driven by a rules-based, event-correlation engine to select the most appropriate algorithms to reduce sensor data to only mission-required exploitation products. By doing so, MAIE greatly improves productivity of operators and analysts to enable them to meet end-user time-critical needs while using fewer resources.

Design consideration of router-to-radio interface in mobile networks

In order to provide versatile and reliable transport of data, voice and video traffic, military organizations deploy complex communications systems that integrate terrestrial, airborne, and space-based platforms. In these tactical communication systems, typically wireless networks, interface with COTS routers at the sub-network boundaries to form a contiguous network infrastructure.

GIG End-to-End Policy Based Network Management: A new approach to large-scale distributed automation

Providing seamless, reliable communications across the Global Information Grid (GIG) presents many unique challenges for network management. Beyond the fact that, as an entity, the GIG encompasses many diverse, operating environments (wired and wireless, tactical and strategic), it faces the challenge of operating a connected series of separate administrative domains which are each governed by different Services (and organizations with each of the services), Combatant Commands (COCOMs), and Agencies. At the same time, it is expected - in fact it is required - to operate as a seamless whole in order to support on-going missions - despite the challenges of a dynamically-changing environment and shifting mission priorities