Performance improvement of a 3-D configuration reconstruction algorithm using a passive secondary target has been focused in this study. In earlier studies, a theoretical development of the 3-D configuration reconstruction algorithm was achieved and it was implemented by a computer program on a system consisting of an optical bench and a digital imaging system. The passive secondary target used was a circle with two internal spots. In order to use this reconstruction algorithm in autonomous systems, an automatic target recognition algorithm has been developed in this study. Starting from a pre-captured and stored 8-bit gray-level image, the algorithm automatically detects the elliptical image of a circular target and determines its contour in the scene. It was shown that the algorithm can also be used for partially captured elliptical images. Another improvement achieved in this study is the determination of internal camera parameters of the vision system.

No description available.

No description available.

The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation Pattern Recognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF is presented that holds the potential to further save on storage and computations. The techniques are developed based on the recently proposed Rayleigh Quotient Quadratic Correlation Filter (RQQCF) and simulation results are provided on synthetic and real datasets.

This thesis investigates the use of sequential parametric projection pursuits (SPPP) for hyperspectral dimensionality reduction and invasive species target recognition. The SPPP method is implemented in a top-down fashion, where hyperspectral bands are used to form an increasing number of smaller groups, with each group being projected onto a subspace of dimensionality one. Both supervised and unsupervised potential projections are investigated for their use in the SPPP method. Fisher?s linear discriminant analysis (LDA) is used as a potential supervised projection. Average, Gaussian-weighted average, and principal component analysis (PCA) are used as potential unsupervised projections. The Bhattacharyya distance is used as the SPPP performance index. The performance of the SPPP method is compared to two other currently used dimensionality reduction techniques, namely best spectral band selection (BSBS) and best wavelet coefficient selection (BWCS). The SPPP dimensionality reduction method is combined with a nearest mean classifier to form an automated target recognition (ATR) system. The ATR system is tested on two invasive species hyperspectral datasets: a terrestrial case study of Cogongrass versus Johnsongrass and an aquatic case study of Waterhyacinth versus American Lotus. For both case studies, the SPPP approach either outperforms or performs on par with the BSBS and BWCS methods in terms of classification accuracy; however, the SPPP approach requires significantly less computational time. For the Cogongrass and Waterhyacinth applications, the SPPP method results in overall classification accuracy in the mid to upper 90?s.

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No description available.

No description available.

We present the design, development, and test of three novel, distinct automatic target recognition (ATR) systems for the recognition of airplanes and, more specifically, non-cooperative airplanes, i.e. airplanes that do not provide information when interrogated, in the framework of passive bistatic radar systems. Passive bistatic radar systems use one or more illuminators of opportunity (already present in the field), with frequencies up to 1 GHz for the transmitter part of the systems considered here, and one or more receivers, deployed by the persons managing the system, and not co-located with the transmitters. The sole source of information are the signal scattered on the airplane and the direct-path signal that are collected by the receiver, some basic knowledge about the transmitter, and the geometrical bistatic radar configuration. The three distinct ATR systems that we built respectively use the radar images, the bistatic complex radar cross-section (BS-RCS), and the bistatic radar cross-section (BS-RCS) of the targets. We use data acquired either on scale models of airplanes placed in an anechoic, electromagnetic chamber or on real-size airplanes using a bistatic testbed consisting of a VOR transmitter and a software-defined radio (SDR) receiver, located near Orly airport, France. We describe the radar phenomenology pertinent for the problem at hand, as well as the mathematical underpinnings of the derivation of the bistatic RCS values and of the construction of the radar images.For the classification of the observed targets into pre-defined classes, we use either extremely randomized trees or subspace methods. A key feature of our approach is that we break the recognition problem into a set of sub-problems by decomposing the parameter space, which consists of the frequency, the polarization, the aspect angle, and the bistatic angle, into regions. We build one recognizer for each region. We first validate the extra-trees method on the radar images of the MSTAR dataset, featuring ground vehicles. We then test the method on the images of the airplanes constructed from data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.99.We test the subspace methods on the BS-CRCS and on the BS-RCS of the airplanes extracted from the data acquired in the anechoic chamber, achieving a probability of correct recognition up to 0.98, with variations according to the frequency band, the polarization, the sector of aspect angle, the sector of bistatic angle, and the number of (Tx,Rx) pairs used. The ATR system deployed in the field gives a probability of correct recognition of $0.82$, with variations according to the sector of aspect angle and the sector of bistatic angle.

<p>Laser vibration sensing can be used to classify military targets by its unique vibration signature. A coherent laser radar receives the target´s rapidly oscillating surface vibrations and by using proper demodulation and Doppler technique, stationary, radially moving and even accelerating targets can be taken care of. </p><p>A frequency demodulation method developed at the former FOA, is for the first time validated against real data with turbulence, scattering, rain etc. The issue is to find a robust and reliable system for target recognition and its performance is therefore compared with some frequency distribution methods. The time frequency distributions have got a crucial drawback, they are affected by interference between the frequency and amplitude modulated multicomponent signals. The system requirements are believed to be fulfilled by combining the FOA method with the new statistical method proposed here, the combination being suggested as aimpoint for future investigations.</p>