How Smart Is Your Automatic Target Recognizer?

Bruce J. Schachter

doi:10.1117/3.2276891.ch6

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Chapter 6:
How Smart Is Your Automatic Target Recognizer?

Book: Automatic Target Recognition, Second Edition

Author(s): Bruce J. Schachter

Published: 2017
https://doi.org/10.1117/3.2276891.ch6

Abstract

The human brain has about 90 billion neurons, each with roughly one thousand synaptic connections to other neurons. However, it does not follow that if we build a computer with equivalent processing power and connectivity, it would match human functionality as an emergent property. We do not now know how the brain does the vast majority of what it does. The long-term goal of neuromorphic engineering is to design artificial systems patterned after both the design and functionality of biological neural systems (not necessarily human). Much of the human brain is used for scene understanding, object detection, recognition, tracking, multisensor fusion, and motor control. So, in a sense, its function is similar to that of an ATR. The ATR can be viewed as a substitute, or at least a workload reducer, for the warfighter’s brain. For the purpose of this discussion, we will consider the neuromorphic ATR to be a black box. We will not be as passionately concerned as to whether the inner workings of the black box are true to brain biology in all possible respects, as is, for example, Henry Markram, head of the European Human Brain Project. As ATR engineers, we just want the black box to transform its inputs to the required outputs. We want the black box to meet certain key performance requirements involving size, weight, power, cost, latency, mean time between failure, and logistics trail. The black box must demonstrate capabilities that are needed in combat. It must become fully operational in a military environment, having passed a difficult operational test and evaluation process. That is, it must be more than “just research.” It should be more rugged and reliable than a comparable commercial product.

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CHAPTER 6
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