KEYWORDS: Interference (communication), Signal detection, Signal to noise ratio, Received signal strength, Receivers, Electronic components, Antennas, Signal processing, Oscillators, Sensors
Traditional approach of locating devices relies on "tagging" with a special tracking device, for example GPS receiver.
This process of tagging is often impractical and costly since additional devices may be necessary. Conversely, in many
applications it is desired to track electronic devices, which already emit unintentional, passive radio frequency (RF)
signals. These emissions can be used to detect and locate such electronic devices. Existing schemes often rely on a priori
knowledge of the parameters of RF emission, e.g. frequency profile, and work reliably only on short distances. In
contrast, the proposed methodology aims at detecting the inherent self-similarity of the emitted RF signal by using Hurst
parameter, which (1) allows detection of unknown (not-pre-profiled) devices, (2) extends the detection range over signal
strength (peak-detection) methods, and (3) increases probability of detection over the traditional approaches. Moreover,
the distance to the device is estimated based on the Hurst parameter and passive RF signal measurements such that the
detected device can be located. Theoretical and experimental studies demonstrate improved performance of the proposed
methodology over existing ones, for instance the basic received signal strength (RSS) indicator scheme. The proposed
approach increases the detection range by 70%, the probability of detection by 60%, and improves the range estimation
and localization accuracy by 70%.
Unmanned Aerial Vehicles (UAVs) are versatile aircraft with many applications, including the potential for use to detect
unintended electromagnetic emissions from electronic devices. A particular area of recent interest has been helicopter
unmanned aerial vehicles. Because of the nature of these helicopters' dynamics, high-performance controller design for
them presents a challenge. This paper introduces an optimal controller design via output feedback control for trajectory
tracking of a helicopter UAV using a neural network (NN). The output-feedback control system utilizes the backstepping
methodology, employing kinematic, virtual, and dynamic controllers and an observer. Optimal tracking is accomplished
with a single NN utilized for cost function approximation. The controller positions the helicopter, which is equipped
with an antenna, such that the antenna can detect unintended emissions. The overall closed-loop system stability with the
proposed controller is demonstrated by using Lyapunov analysis. Finally, results are provided to demonstrate the
effectiveness of the proposed control design for positioning the helicopter for unintended emissions detection.
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