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Novel Battle Damage Assessment Using Sensor Networks

Description:

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: Assessing the effect of a high-power electromagnetic (HPEM) attack is challenging, and potentially requires the use of multiple sensing techniques that must be integrated to determine mission success or failure. We are seeking innovative approaches based on advanced statistical techniques to perform this assessment for an HPEM attack, as well as to quantify the contribution of specific sensors to the assessment. 

DESCRIPTION: Performing BDA for an HPEM attack is a challenging technical problem, potentially involving multiple sensors to attempt to quantify how the target is affected. This includes aspects such as reconstitution time that may not be directly observable, and for which we can only hope to determine probability distributions. In addition, many aspects of the target system may themselves be only imperfectly known. Modern statistical/Bayesian approaches coupled with nonlinear optimization techniques offer the opportunity to approach this problem in a novel way that allows us not only to perform BDA more efficiently and more accurately, but also to understand the role and utility of specific sensors in this process. We are seeking novel approaches to modeling the uncertainties in this problem, with the aim of better understanding what sensors are required for an HPEM attack and quantifying the utility of specific sensors in reducing the total uncertainty of critical mission effectiveness parameters such as target functionality and reconstitution time. 

PHASE I: Develop technical approach and software development plan for combining sensor information and assessing uncertainties in assessed quantities, as well as quantifying utility of specific sensors. 

PHASE II: Build and deliver software tool implementing technical approach, and demonstrate to AFRL/RDH. Apply to one or more test cases (unclassified) to show utility of approach. 

PHASE III: Transition to DoD and work with other industry partners to apply to real world test cases of relevance to HPEM weapon systems. 

REFERENCES: 

1: Bayesian Data Analysis, Third Edition. Gelmanm, Carlin, Dunson, Vetari, and Rubin. Chapman & Hall/CRC Texts in Statistical Science, 2014.

2:  Commander's Handbook for Joint Battle Damage Assessment, 2004, US Joint Forces Command Joint Warfighting Center: http://www.dtic.mil/doctrine/doctrine/jwfc/hbk_jbda.pdf

KEYWORDS: Battle Damage Assessment, Advanced Statistical Techniques, Bayesian, Multiple Sensors 

CONTACT(S): 

Timothy Clarke 

(505) 846-9107 

timothy.clarke@us.af.mil 

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