We report on a fiber optic sensor based on the physiological aspects of the eye and vision-related neural layers of the common housefly (Musca domestica) that has been developed and built for aerospace applications. The intent of the research is to reproduce select features from the fly’s vision system that are desirable in image processing, including high functionality in low-light and low-contrast environments, sensitivity to motion, compact size, lightweight, and low power and computation requirements. The fly uses a combination of overlapping photoreceptor responses that are well approximated by Gaussian distributions and neural superposition to detect image features, such as object motion, to a much higher degree than just the photoreceptor density would imply. The Gaussian overlap in the biomimetic sensor comes from the front-end optical design, and the neural superposition is accomplished by subsequently combining the signals using analog electronics. The fly eye sensor is being developed to perform real-time tracking of a target on a flexible aircraft wing experiencing bending and torsion loads during flight. We report on results of laboratory experiments using the fly eye sensor to sense a target moving across its field of view.
Reducing the environmental impact of aviation is a primary goal of NASA aeronautics research. One approach to
achieve this goal is to build lighter weight aircraft, which presents complex challenges due to a corresponding increase in
structural flexibility. Wing flexibility can adversely affect aircraft performance from the perspective of aerodynamic
efficiency and safety. Knowledge of the wing position during flight can aid active control methods designed to mitigate
problems due to increased wing flexibility. Current approaches to measuring wing deflection, including strain
measurement devices, accelerometers, or GPS solutions, and new technologies such as fiber optic strain sensors, have
limitations for their practical application to flexible aircraft control. Hence, it was proposed to use a bio-mimetic optical
sensor based on the fly-eye to track wing deflection in real-time. The fly-eye sensor has several advantages over
conventional sensors used for this application, including light weight, low power requirements, fast computation, and a
small form factor. This paper reports on the fly-eye sensor development and its application to real-time wing deflection
measurement.
KEYWORDS: Signal to noise ratio, Target detection, Sensors, Switches, Kinematics, Radar, Mahalanobis distance, Signal processing, Time metrology, Detection and tracking algorithms
Closely-spaced (but resolved) targets pose a significant challenge for single-frame unique measurement-to-track
data association algorithms. This is due to the similarity of the Mahalanobis distances between the closely-spaced
measurements and tracks. Contrary to conventional wisdom, adding target feature information (e.g.,
target amplitude) does not necessarily improve the probability of correctly assigning measurements to tracks.
In this paper, the theoretical limitations of using radar cross section (RCS) data to aid in measurement-totrack
association are reviewed. The results of a high-fidelity simulation assessment of the benefits of RCSaided
measurement-to-track association (using the Signal-to-Noise Ratio) are given and other possibilities for
RCS-aided tracking are discussed. Namely, we show the analytical results of our investigation into using RCS
information to determine the presence of merged measurements.
Conference Committee Involvement (2)
Bioinspiration, Biomimetics, and Bioreplication VI
21 March 2016 | Las Vegas, Nevada, United States
Bioinspiration, Biomimetics, and Bioreplication V
9 March 2015 | San Diego, California, United States
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