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. 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.

In this thesis, rotation, scale and translation (RST) invariant automatic target recognition (ATR) for satellite imagery is presented. Template matching is used to realize the target recognition. However, unlike most of the studies of template matching in the literature, RST invariance is required in our problem, since most of the time we will have only a small number of templates of each target, while the targets to be recognized in the scenes will have various orientations, scaling and translations. RST invariance is studied in detail and implemented with some of the competing methods in the literature, such as Fourier-Mellin transform and bipectrum combined with log-polar mapping. Phase correlation and normalized cross-correlation are used as similarity metrics. Encountered drawbacks were overcome with additional operations and modifications of the algorithms. ATR using reconstruction of the target image with respect to the template, based on bispectrum, log-polar mapping and phase correlation outperformed the other methods and successful recognition was realized for various target types, especially for targets on relatively simpler backgrounds, i.e. containing little or no other objects.

Although utilization of polarimetry techniques for recognition of military and civilian targets is well established in the narrowband context, it is not yet fully established in a broadband sense as compared to planetary area of research. The concept of combining polarimetry together with certain areas of broadband technology and thus forming a robust signature and feature set has been the main theme of this thesis. This is important, as basing the feature set on multiple types of signatures can increase the accuracy of the recognition process. In this thesis, the concept of radar target recognition based upon a polarization signature in a broadband context is examined. A proper UWB radar signal can excite the target dominant resonances and, consequently, reveal information about the target principle dimensions; while diversity in the polarization domain revealed information about the target shape. The target dimensions are used to classify the target, and then information about its shape is used to identify it. Fused together and inferred from the target characteristic polarization states, it was verified that the polarization information at dominant resonant frequencies have both a physical interpretation and attributes (as seen in section ‎3.4.3) related to the target symmetry, linearity, and orientation. In addition, this type of information has the ability to detect the presence of major scattering mechanisms such as strong specular reflection as in the case of the cylinder flat ends. Throughout the thesis, simulated canonical targets with similar resonant frequencies were used, and thus identification of radar targets was based solely on polarization information. In this framework, the resonant frequencies were merely identified as peaks in the frequency response for simple or low damping targets such as thin metal wires, or alternatively identified as the imaginary parts of the complex poles for complex or high damping targets with significant diameter and dielectric properties. Therefore, the main contribution of this thesis originates from the ability to integrate the optimum polarization states in a broadband context for improved target recognition performance. In this context, the spectral dispersion originating from the broad nature of the radar signal, the lack of accuracy in extracting the target resonances, the robustness of the polarization feature set, the representation of these states in time domain, and the feature set modelling with spatial variation are among the important issues addressed with several approaches presented to overcome them. The general approach considered involved a subset of “representative” times in the time domain, or correspondingly, “representative frequencies” in the frequency domain with which to associate optimum polarization states with each member of the subset are used. The first approach in chapter ‎3 involved the polarization representation by a set of frequency bands associated with the target resonant frequencies. This type of polarization description involved the formulation of a wideband scattering matrix to accommodate the broad nature of the signal presentation with appropriate bandwidth selection for each resonance; good estimation of the optimum polarization states in this procedure was achievable even for low signal-to-noise ratios. The second approach in chapter ‎4 extended the work of chapter ‎3 and involved the modification of the optimum polarization states by their associated powers. In addition, this approach included an identification algorithm based on the nearest neighbour technique. To identify the target, the identification algorithm involved the states at a set of resonant frequencies to give a majority vote. Then, a comparison of the performance of the modified polarization states and the original states demonstrated good improvement when the modified set is used. Generally, the accuracy of the resonance set estimate is more reliable in the time domain than the frequency domain, especially for resonances well localized in time. Therefore, the third approach in chapter ‎5 deals with the optimum states in the time domain where the extension to a wide band context was possible by the virtue of the polarization information embodied in the energy of the resonances. This procedure used a model-based signature to model the target impulse response as a set of resonances. The relevant resonance parameters, in this case, the resonant frequency and its associated energy, were extracted using the Matrix Pencil of Function algorithm. Again, this approach of sparse representation is necessary to find descriptors from the target impulse response that are time-invariant, and at the same time, can relate robustly to the target physical characteristics. A simple target such as a long wire showed that indeed polarization information contained in the target resonance energies could reflect the target physical attributes. In addition, for noise-corrupted signals and without any pulse averaging, the accuracy in estimating the optimum states was sufficiently good for signal to noise ratios above 20dB. Below this level, extraction of some members of the resonance set are not possible. In addition, using more complex wire models of aircraft, these time-based optimum states could distinguish between similar dimensional targets with small structural differences, e.g. different wing dihedral angles. The results also showed that the dominant resonance set has members belonging to different structural sections of the target. Therefore, incorporation of a time-based polarization set can give the full target physical characteristics. In the final procedure, a statistical Kernel function estimated the feature set derived previously in chapter ‎3, with aspect angle. After sampling the feature set over a wide set of angular aspects, a criterion based on the Bayesian error bisected the target global aspect into smaller sectors to decrease the variance of the estimate and, subsequently, decrease the probability of error. In doing so, discriminative features that have acceptable minimum probability of error were achievable. The minimum probability of error criterion and the angular bisection of the target could separate the feature set of two targets with similar resonances.

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No description available.

Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007. A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from the publicly available Moving and Stationary Target Acquisition and Recognition (MSTAR) database, target orientation was determined to be the best feature for ATR. A rotationally variant algorithm taking advantage of the combination of target orientation and pixel location for classification is proposed in this thesis. Extensive classification results yielding an overall accuracy of 76.78% are presented to demonstrate algorithm functionality.

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No description available.

This thesis presents a new technique for performing image analysis, synthesis, and modification using a generalized Gaussian model. The joint time-frequency characteristics of a generalized Gaussian are combined with the flexibility of the analysis-by-synthesis (ABS) decomposition technique to form the basis of the model. The good localization properties of the Gaussian make it an appealing basis function for image analysis, while the ABS process provides a more flexible representation with enhanced functionality. ABS was first explored in conjunction with sinusoidal modeling of speech and audio signals [George87]. A 2D extension of the ABS technique is developed here to perform the image decomposition. This model forms the basis for new approaches in image analysis and enhancement. The major contribution is made in the resolution enhancement of images generated using coherent imaging modalities such as Synthetic Aperture Radar (SAR) and ultrasound. The ABS generalized Gaussian model is used to decouple natural image features from the speckle and facilitate independent control over feature characteristics and speckle granularity. This has the beneficial effect of increasing the perceived resolution and reducing the obtrusiveness of the speckle while preserving the edges and the definition of the image features. A consequence of its inherent flexibility, the model does not preclude image processing applications for non-coherent image data. This is illustrated by its application as a feature extraction tool for a FLIR imagery complexity measure.

This thesis presents a novel aspect and polarization invariant electromagnetic target recognition technique in resonance region based on use of MUSIC algorithm for the extraction of natural-resonance related target features. In the suggested method, the feature patterns called &ldquo / MUSIC Spectrum Matrices (MSMs)&rdquo / are constructed for each candidate target at each reference aspect angle using targets&rsquo / scattered data at different late-time intervals. These individual MSMs correspond to maps of targets&rsquo / natural-resonance related power distributions. All these patterns are first used to obtain optimal late-time interval for classifier design and a &ldquo / Fused MUSIC Spectrum Matrix (FMSM)&rdquo / is generated over this interval for each target by superposing MSMs. The resulting FMSMs include more complete information for target resonances and are almost insensitive to aspect and polarization. In case of multiple target recognition, the relative locations of a multi-target group and separation distance between targets are also important factors. Therefore, MSM features are computed for each multi-target group at each &ldquo / reference aspect/topology&rdquo / combination to determine the optimum late-time interval. The FMSM feature of a given multi-target group is obtained by the superposition of all these aspect and topology dependent MSMs. In both single and multiple target recognition cases, the resulting FMSM power patterns are main target features of the designed classifier to be used during real-time decisions. At decision phase, the unknown test target is classified either as one of the candidate targets or as an alien target by comparing correlation coefficients computed between MSM of test signal and FMSM of each candidate target.