124 research outputs found
Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank
A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold
Inspection robots are widely used in the field of smart grid monitoring in
substations, and partial discharge (PD) is an important sign of the insulation
state of equipments. PD direction of arrival (DOA) algorithms using
conventional beamforming and time difference of arrival (TDOA) require
large-scale antenna arrays and high computational complexity, which make them
difficult to implement on inspection robots. To address this problem, a novel
directional multiple signal classification (Dir-MUSIC) algorithm for PD
direction finding based on signal strength is proposed, and a miniaturized
directional spiral antenna circular array is designed in this paper. First, the
Dir-MUSIC algorithm is derived based on the array manifold characteristics.
This method uses strength intensity information rather than the TDOA
information, which could reduce the computational difficulty and the
requirement of array size. Second, the effects of signal-to-noise ratio (SNR)
and array manifold error on the performance of the algorithm are discussed
through simulations in detail. Then according to the positioning requirements,
the antenna array and its arrangement are developed, optimized, and simulation
results suggested that the algorithm has reliable direction-finding performance
in the form of 6 elements. Finally, the effectiveness of the algorithm is
tested by using the designed spiral circular array in real scenarios. The
experimental results show that the PD direction-finding error is 3.39{\deg},
which can meet the need for Partial discharge DOA estimation using inspection
robots in substations.Comment: 8 pages,13 figures,24 reference
Array processing based on time-frequency analysis and higher-order statistics
Ph.DDOCTOR OF PHILOSOPH
Ship target recognition
Includes bibliographical references.In this report the classification of ship targets using a low resolution radar system is investigated. The thesis can be divided into two major parts. The first part summarizes research into the applications of neural networks to the low resolution non-cooperative ship target recognition problem. Three very different neural architectures are investigated and compared, namely; the Feedforward Network with Back-propagation, Kohonen's Supervised Learning Vector Quantization Network, and Simpson's Fuzzy Min-Max neural network. In all cases, pre-processing in the form of the Fourier-Modified Discrete Mellin Transform is used as a means of extracting feature vectors which are insensitive to the aspect angle of the radar. Classification tests are based on both simulated and real data. Classification accuracies of up to 93 are reported. The second part is of a purely investigative nature, and summarizes a body of research aimed at exploring new ground. The crux of this work is centered on the proposal to use synthetic range profiling in order to achieve a much higher range resolution (and hence better classification accuracies). Included in this work is a comprehensive investigation into the use of super-resolution and noise reducing eigendecomposition techniques. Algorithms investigated include the Principal Eigenvector Method, the Total Least Squares Method, and the MUSIC method. A final proposal for future research and development concerns the use of time domain averaging to improve the classification performance of the radar system. The use of an iterative correlation algorithm is investigated
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