Vibration signatures sensed from distant vehicles using laser vibrometry systems provide valuable information that may
be used to help identify key vehicle features such as engine type, engine speed, and number of cylinders. While
developing algorithms to blindly extract the aforementioned features from a vehicle's vibration signature, it was shown
that detection of engine speed and number of cylinders was more successful when utilizing a priori knowledge of the
engine type (gas or diesel piston) and optimizing algorithms for each engine type. In practice, implementing different
algorithms based on engine type first requires an algorithm to determine whether a vibration signature was produced by a
gas piston or diesel piston engine. This paper provides a general overview of the observed differences between datasets
from gas and diesel piston engines, and proceeds to detail the current method of differentiating between the two. To date,
research has shown that basic signal processing techniques can be used to distinguish between gas and diesel vibration
datasets with reasonable accuracy for piston engines of different configurations running at various speeds.
Modern LADAR sensors have the potential to utilize a number of sensing modalities that provide a rich array of
information in addition to traditional 3D geometry. Imaging polarization, multi-spectral reflectance/absorption
and vibration spectral signature characteristics can all be sensed, potentially in a single LADAR sensor. This
paper will examine how these rich sensing capabilities enhance the utility of LADAR signature exploitation.
This research utilizes a strong understanding of underlying physical phenomena, enabling the development of
data exploitation capabilities that are not brittle to small variations from assumed targets and environmental
conditions, and minimizing the need for experimentally obtained training data. Physics-based signal processing
research has demonstrated a promising ability to extract useful and actionable intelligence from the various
sensing modalities of modern LADAR systems. A summary of the intelligence provided by the LADAR sensing
modalities is presented as well as a demonstration of how the individual modes and combinations of LADAR sensing
modes can be leveraged to add unique and valuable information to intelligence gathering missions. Particular
utility is demonstrated for detection of adversary presence in cluttered, obstructed, hidden or underground environments.
Furthermore, research has shown 3D geometry, polarization, multi-spectral and vibrometry LADAR
sensing modalities can provide valuable intelligence for identifying and/or classifying the adversary and analyzing
threat.
KEYWORDS: Sensors, Control systems, Interferometers, Actuators, Space telescopes, Error analysis, Digital filtering, Active optics, Digital signal processing, Active vibration control
Experimental results are presented for active vibration control of the Air Force Research Laboratory's UltraLITE Precision Deployable Optical Structure (PDOS), a ground based model of a sparse array, large aperture, deployable optical space telescope. The primary vibration suppression technique employs spatio-temporal filtering, in which a small number of sensors are used to produce modal coordinates for the structural modes to be controlled. The spatio-temporal filtering technique is well suited for the control of complex, real-world structures because it requires little model information, automatically adapts to sensor and actuator failures, is computationally efficient, and can be easily configured to account for time-varying system dynamics. While controller development for PDOS continues, the results obtained thus far indicate the need for an integrated optical/structural control system.
KEYWORDS: Sensors, Actuators, Control systems, Systems modeling, Mirrors, Space telescopes, Filtering (signal processing), Interferometers, Active vibration control, Device simulation
A spatio-temporal filter (STF) based active vibration suppression technique is presented. The STF approach is intended for use for stability and jitter compensation for the UltraLITE Precision Deployable Experiment -- a ground demonstration of a sparse array, deployable, large aperture, optical space telescope concept. This technique is well suited for control of complex, real-world structures because it requires little model information, autonomously accommodates sensor and actuator failures, is computationally efficient and the controller is easily updated to account for time varying system dynamics. An overview of the STF approach is given and experimental active vibration suppression results obtained on the Mirror Mass Simulator testbed at AFRL, Kirtland AFB are presented.
KEYWORDS: Digital filtering, Actuators, Control systems, Neural networks, Sensors, Feedback control, Algorithm development, Adaptive control, System identification, Linear filtering
An adaptive algorithm is proposed for the control of a large space truss structure which uses modal filters for independent modal space control and a simple neural network that provides an on-line system identification capability. The modal filters are computed off-line using measured frequency response functions and estimated pole values for the modes of interest, and provide a coordinate transformation that yields modal coordinates from physical response measurements. The time histories for the modal coordinates are then processed in real time by the neural network, which models a single degree of freedom system transfer function and provides estimates of modal parameters, namely, frequency, damping ratio and modal gain. The modal filters are used to implement independent modal space control on a 3.74 meter space truss using a single reaction-mass actuator and 32 accelerometers. The performance of the modal filter based controller is compared to that of a local rate feedback controller using the same actuator. The applicability of the adaptive filter to adaptive control is demonstrated by real time estimation of the modal parameters of the truss with and without control. Because the modal filter control gain can be adjusted to maintain a desired closed loop damping ratio, which is tracked by the adaptive filter, adaptive control of individual modes in a time-varying system is possible. The goal of this work is to field a control system which can maintain desired closed loop damping ratios for mode frequency variations as high as 10%.
This paper develops an approach for the health monitoring of a smart structures with multiple embedded sensing and actuation capability. For such a structure the consideration of failure consequences is an important component of any real application. The approach developed here is an integrated control and monitoring procedure whereby the sensors, which are assumed to be distributed spatially across the structure, are processed by a set of spatial modal filters which automatically track the modal coordinates of desired, specified modes, and similarly track changes in modal characteristics such as modal frequency, damping, and mode shape. The adaptive modal filter is formulated and applied to tr:a.ck the time varying behavior of specified modes, thereby indicating in some general sense, the health of the structural system. The adaptive modal filter is insensitive to failures or calibration shifts in individual sensors and will automatically ignore failed sensors. It can also be used to detect disturbances entering the system as well as to identify failed actuator locations. A modal controller based on these estimates is then able to adapt to a changing structure and in addition is insensitive to failures in the sensors and actuators. Both the tl1eory and experimental results from a test structure is discussed.
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