100 research outputs found
Reliable detection and characterisation of dim target via track-before-detect
Detection of manoeuvring and small objects is a challenging task in radar surveillance
applications. Small objects in high noise background induce low signal to noise ratio (SNR)
reflections. Conventional methods detect such objects by integrating multiple reflections in the
same range-bearing and doppler bins in sampled versions of received signals. When the objects
manoeuvre, however, these methods are likely to fail to detect them because the integration is
performed without taking into account the possibility of the object movements across resolution
bins. Furthermore, slowly manoeuvring objects create detection difficulties in discriminating
them from radar clutter. Reflections of such objects contain micro-Doppler shifts generated by
their propulsion devices. These shifts can characterise specific types of objects. In this case,
estimation of these shifts is a challenging task because the front-end signals at the receiver are
low SNR reflections and are the superposition of all reflections from the entire object and the
noise background. Conventional estimators for this purpose only use reflections collected in a
coherent processing interval (CPI) and produce poor estimate outputs. In order to achieve the
desired accuracy, one requires more reflections than those collected in a CPI.
This thesis mainly considers the aforementioned two difficulties and aims to develop efficient
algorithms, which can detect low SNR and manoeuvring objects by incorporating long-time
pulse integration and micro-doppler estimation. Main contributions in this thesis are based
on the following two algorithms. The first work considers the detection of manoeuvring and
small objects with radars. The radar systems are considered both co-located and separated
transmitter/receiver pairs, i.e., monostatic and bistatic configurations, respectively, as well
as multistatic settings involving both types. The proposed detection algorithm is capable of
coherently integrating reflected signals within a CPI in all these configurations and continuing
integration for an arbitrarily long time across consecutive CPIs. This approach estimates the
complex value of the reflection coefficients for the integration while simultaneously estimating
the object trajectory. Compounded with this simultaneous tracking and reflection coefficient
estimation is the estimation of the unknown time reference shift of the separated transmitters
necessary for coherent processing. The detection is made by using the resulting integration
value in a Neyman-Pearson test against a constant false alarm rate threshold.
The second work focuses on micro-Doppler signature estimation of manoeuvring and small rotor
based unmanned aerial vehicle (UAV) systems with a monostatic radar. The micro-Doppler
signature is considered rotation frequencies generated by rotating rotor blades of the UAVs.
This estimation uses a maximum likelihood (ML) approach that finds rotation frequencies
to maximise a likelihood function conditioned on an object trajectory, complex reflection
coefficients, and rotation frequencies. In particular, the proposed algorithm uses an
expectation-maximisation (EM) approach such that the expectation of the likelihood mentioned
above is approximated by using the state distributions generated from Bayesian recursive
filtering for the trajectory estimation. The reflection coefficients and the rotation frequencies
are estimated by maximising this approximated expectation. As a result, this algorithm is
capable of simultaneously tracking the trajectory and estimating the reflection coefficients and
the rotation frequencies of the UAVs before the decision on the object presence is made
Religiosity moderates the link between environmental beliefs and pro-environmental support: The role of belief in a controlling god
Calculation of Neoclassical Toroidal Viscosity with a Particle Simulation in the Tokamak Magnetic Breaking Experiments
Accurate calculation of perturbed distribution function #14;δf and perturbed magnetic fi eld #14;δB is essential to achieve prediction of non-ambipolar transport and neoclassical toroidal viscosity (NTV) in perturbed tokamaks. This paper reports a study of the NTV with a #14;δf particle code (POCA) and improved understanding of magnetic braking in tokamak experiments. POCA calculates the NTV by computing #14;f with guiding-center orbit motion and using #14;B from the ideal perturbed equilibrium code (IPEC). POCA simulations are compared with experimental estimations for NTV, which are measured from angular momentum balance (DIII-D) and toroidal rotational damping rate (NSTX). The calculation shows good agreement in total NTV torque for the DIII-D discharge, where an analytic neoclassical theory also gives a consistent result thanks to relatively large aspect-ratio and slow toroidal rotations. In NSTX discharges, where the aspect-ratio is small and the rotation is fast, the theory only gives a qualitative guide for predicting NTV. However, the POCA simulation largely improves the quantitative NTV prediction for NSTX. It is discussed that a self- consistent calculation of δ#14;B using general perturbed equilibria is eventually necessary since a non-ideal plasma response can change the perturbed eld and thereby the NTV torque
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