Quantum radars are garnering increasing attention, but one class of quantum radars has not received very much attention: quantum interferometric radars. Such radars use a type of entangled quantum signal called N00N states to enhance phase sensitivity. In this paper, we propose that quantum interferometric radars could be used for biomedical applications such as vital signs monitoring and organ imaging. Due to such radars being able to operate well at low transmit powers and the radiation itself being non-ionizing, they can mitigate any safety risk to patients.
The increased usage of unmanned aerial devices for commercial usage, such as drones, has presented a new challenge in aircraft security and overall public safety. Therefore, there is an urgent need to accurately detect and track drones. The objective of this paper is to classify rotary drones and fixed wing drones based on their trajectories. In order to develop classification models, Stone Soup open-source software framework is used to generate simulated track data using the location information of the drones available through Global Position System (GPS) telemetry data. Stone Soup can be used to study the quality of the tracks when classifying drones. To study the performance of the various classification methods in a realistic environment, false alarms were generated along with the tracker outputs. Tracker output is segmented into sub-trajectories and were used as inputs to the different classification models. Traditional machine learning algorithms namely Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Decision Trees (DT) and a deep learning algorithm namely Convolutional Neural Networks (CNN) were considered for developing classification models. Kinematic features derived from the sub-trajectories were used as features for machine learning algorithms while images obtained from the sub-trajectories were used as input to the CNN. In order to handle class imbalances, data augmentation was used. The performance of the various classification models validated the objective of this paper.
The challenge of ownship navigation for an airborne platform in the absence of precise navigation information is an important problem. In this paper, the problem is solved using the assumed known GPS locations of landmarks by casting it in a Bayesian state-space framework. It is assumed that no information is available from the navigation sensors. The platform kinematic state is inferred by using a nonlinear filter, such as the extended Kalman filter. The performance is assessed as a function of the density of landmarks and platform manoeuvres in a simulation environment.
Visual image tracking involves the estimation of the motion of any desired targets in a surveillance region using a sequence of images. A standard method of isolating moving targets in image tracking uses background subtraction. The standard background subtraction method is often impacted by irrelevant information in the images, which can lead to poor performance in image-based target tracking. In this paper, a B-Spline based image tracking is implemented. The novel method models the background and foreground using the B-Spline method followed by a tracking-by-detection algorithm. The effectiveness of the proposed algorithm is demonstrated.
KEYWORDS: Receivers, Systems modeling, Antennas, Data modeling, Radar, Doppler effect, MATLAB, Wave propagation, Modeling and simulation, Signal processing
In this paper, a high-fidelity RF modeling and simulation framework is demonstrated to model an airborne multi-channel
receiver system that is used to estimate the angle of arrival (AoA) of received signals from a stationary emitter. The
framework is based on System Tool Kit (STK®), Matlab and SystemVue®. The SystemVue-based multi-channel receiver
estimates the AoA of incoming signals using adjacent channel amplitude and phase comparisons, and it estimates the
Doppler frequency shift of the aircraft by processing the transmitted and received signals. The estimated AoA and
Doppler frequency are compared with the ground-truth data provided by STK to validate the efficacy of the modeling
process. Unlike other current RF electronic warfare simulation frameworks, the received signal described herein is
formed using the received power, the propagation delay and the transmitted waveform, and does not require information
such as Doppler frequency shift or radial velocity of the moving platform from the scenario; hence, the simulation is
more computationally efficient. In addition, to further reduce the overall modeling and simulation time, since the high-fidelity
model computation is costly, the high-fidelity electronic system model is evoked only when the received power is
higher than a predetermined threshold.
KEYWORDS: Signal detection, Signal to noise ratio, Receivers, Digital signal processing, Electronic filtering, Detection and tracking algorithms, Statistical analysis, Solids, Fourier transforms, Radar
The fast Fourier sampling (FFS) method is related to the new sampling paradigm, compressive sampling (CS). This
paper explores the application of the FFS method in an ultra-wide band digital receiver. The aim of the study is to
quickly detect sparsely distributed carrier frequencies in an ultra-wide frequency band using fewer digital sampled data
when compared to ubiquitous methods, such as the fast Fourier transform (FFT). Study shows that the FFS method can
be applied to ultra-wide band sparse radar signal detection using randomly selected data from conventional analog-todigital
converter and has the added advantage that it can be implemented on DSP hardware using a short-length of FFT.
A methodology for segmentation of multi-component signals buried in additive white Gaussian noise using
singular value decomposition (SVD) in the time-frequency domain is proposed. The segmentation problem is
posed as a binary statistical hypothesis testing problem. Using the Generalized Likelihood Ratio (GLR), the
optimal test statistic is shown to be the sum of squares of the norms of the principal components of the signal in
the time-frequency domain. The signal-to-noise ratio (SNR) at the dominant signal frequencies is assumed to be
sufficiently high to determine the bandwidth of the signal components. The proposed segmentation methodology
is evaluated on phonocardiogram (PCG) signals.
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