BIOLOGICALLY INSPIRED AUTOMATIC TARGET DETECTION, CLASSIFICATION, AND TRACKING
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Authors
Kim, Eric
Subjects
fire support
kill chain management
artificial intelligence
spiking neural networks
neuromorphic computing
computer vision
computer architectures
kill chain management
artificial intelligence
spiking neural networks
neuromorphic computing
computer vision
computer architectures
Advisors
Monaco, John
Date of Issue
2020-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The decision to use and deliver kinetic and/or non-kinetic fires have been and will forever be entwined into war. The U.S. military and specifically the Marines have been the masters of this process but to maintain this superiority, fire support needs to become more timely, discriminatory, lethal, and effective in today's data-saturated environment. Also, with the distributive nature of the modern operating environment, producing a SWaP-T-compatible solution is vital. To bridge these gaps, the author proposes to offload the target detection, classification, and tracking to a biologically inspired automated system composed of a Dynamic Vision Sensor and a spiking neural network running on neuromorphic hardware. Emphasis was placed on the spiking neural network algorithm development and building/evaluating the system. The author found that this approach could yield a system that will provide the warfighter with actionable information to improve the kill chain process while minimizing power consumption and time taken at the point of collection. The hope is that the research presented here will spur advances in the field of biologically inspired neuromorphic platforms that will produce timely, accurate, distilled, and actionable information to the end user to offload mundane/trivial tasks to allow for more decision time and space.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
Sponsors
Funder
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Citation
Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States