PhD Defense by Brian M. Wade
A Framework for the Optimization of Doctrine and Systems in Army Air Defense Units Against a Complex Attack of Ballistic and Cruise Missiles Using Predictive Models of Stochastic Computer Simulations
Brian M. Wade
Advisor: Dr. Daniel Schrage
1:30 PM, Thursday, March 16
Weber Building CoVE
Proliferation of missile technology has increased in recent years. Today, almost every military force in the world maintains an arsenal of Theater Ballistic Missiles (TBMs) and Cruise Missiles (CMs). These technologies are less expensive to acquire and maintain than a conventional Air Force, but offer many of the same advantages, such as precision strike and deep shaping operations. This proliferation is advancing much faster than the Air Defensive Artillery (ADA) systems’ capabilities since the cost of counter-missile systems is much greater than the ballistic or cruise missiles that they target. Additionally, much of the ADA system’s tactics were developed on past battlefields with small and uniform raids made up of only a limited number of TBMs or CMs. Today, the ADA systems face raids including a large number of systems made of combinations of different types of TBMs and CMs.
This thesis presents a new methodology that can be used to address large-scale complex raids made up of different types TBMs and CMs that attempt to overwhelm the ADA systems at a particular location. This method will allow for technology gap identification, but the primary focus will be on how existing ADA systems can adjust their tactics in order to minimize the damage caused by threats that are not shot down and impact friendly forces.
Almost all the literature to date optimizes systems and tactics to reduce the number of leakers — threats not shot down — that impact the ground. However, simply counting the number of leakers does not adequately describe the effects to friendly forces. Instead, the first part of this thesis combines existing methods for external ballistics, concrete penetration, explosive cratering, and weapon blast and fragmentation damage in order to create an integrated program that can describe the damage to an airfield runway, infrastructure, and parked aircraft. The second part of the thesis focuses on modeling the ADA missile engagements. Today’s high fidelity ADA modeling software is extremely accurate, but it runs relatively slow and produces a large amount of data. This thesis uses an accredited Department of Defense ADA simulation model called the Extended Air Defense Simulation (EADSIM).
Both the airfield damage model and ADA simulation have runtimes ranging from minutes to hours. They are also stochastic, so a large number of runs are required for each input vector in order to properly understand the output range. In order to reduce the computation time to allow for later optimization, the methods of Design of Experiments and Machine Learning, such as Neural Networks and Gaussian Process Models, were used to create fast running models that predict the outputs of these simulations.
The final part of the work uses these prediction algorithms to first optimize the enemy fire plan, then optimize the ADA defense tactics, and finally optimize the ADA defense tactics with a new interceptor missile system. Initially, the enemy attack plan must be optimized in order to discover combinations of the different types of TBMs and CMs that cause the most damage to different areas of the airfield. This analysis produces a frontier of non-dominated solutions that maximize different effects such as damage to the runway, aircraft, or fuel. Given this set of optimized fire plans, the friendly ADA tactics are optimized in order to minimize the damage to friendly assets for the lowest cost. A multi-attribute decision making tool is then used to select a specific set of tactics and these tactics are then compared to the based case.
Dr. Daniel Schrage - Advisor
Dr. Dimitri Mavris
Dr. Lakshmi Sankar
Dr. Dave Knudson, Center for Army Analysis