Sonar and radar SAR processing for parking lot detection

In this paper, SAR processing algorithms for automotive applications are presented and illustrated on data from non-trivial test scenes. The chosen application is parking lot detection. Laboratory results obtained with a teaching sonar experiment emphasize the resolution improvement introduced with range-Doppler SAR processing. A similar improvement is then confirmed through full scale measurements performed with an automotive radar prototype operating at 77GHz in very close range conditions, typical of parking lot detection. The collected data allows a performance comparison between different SAR processing algorithms for realistic targets

Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction

Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.Comment: This is a pre-print of a conference paper published in German Conference on Pattern Recognition (GCPR) 201

A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar

A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877

A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar

A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877

Optimal processings design for synthetic aperture radar

Synthetic aperture radar (SAR) is a microwave imagery system capable ofproducing high resolution images by processing properly data collected by a relatively small antenna. In this papet the bi-dimensionnal received signal, using spatial coordinates, is formulated. The image is reconstructed by a two- dimensionnal filtering operation . We propose a straightforward derivation for the coherent sommation method (or bock-projection of range responses) and the 2-D azimuth matched filtering . The image quality is determined by that of the ambiguity fonction. This latter is analyzed and optimized for two performance criteria. First, for a matched filter receiver (maximal signal-to-noise ratio receiver), the optimal waveform is shown ta be a non linear FM pulse which autocorrelationfunction is a Taylor . The optimal azmiuth weighting fonction is related to that of Taylor by a Fourier transform . Second, for a Wiener filter (least mean-squares receiver), we show that the optimal waveform is the firstprolate spheroidal fonction . The single-hit measurement of the scattering matrix by mean of two optimal orthogonal waves is discussed . Resolutions, SNR as well as ambiguities, are specified .Le radar à visée latérale et à ouverture synthétique est un système d'imagerie micro-onde capable de produire des images de très haute résolution des terrains, et ceci à partir d'un traitement approprié des signaux reçus par une antenne de faible dimension, Dans cet article, nous présentons la formulation exacte du signal reçu, ainsi que les traitements associés afin de former l'image. On met en évidence l'équivalence entre la méthode de la sommation cohérente et le filtrage adapté bidimensionnel en azimut. La qualité de l'image ainsi formée est déterminée par celle de la fonction d'ambiguït

Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination

Caption title.Includes bibliographical references (p. 35-37).Supported by the Air Force Office of Scientific Research. AFOSR-92-J-0002 Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Army Research Office. DAAL03-92-G-0115Mark R. Luettgen, Alan S. Willsky

Vigilância da costa marítima utilizando radar de abertura sintética

Nesta tese apresenta-se uma nova técnica para solucionar a problemática da estimação de velocidade de embarcações em Radar de Abertura Sintética (SAR). A solução proposta combina duas técnicas já publicadas introduzindo como inovação, a Transformada de Radon. Esta transformada vai permitir estimar a posição do rasto que a embarcação gera à medida que se vai deslocando. Com a posição do objecto calculada é então possível estimar a sua distância ao rasto e assim a velocidade em range. Esta estimativa é limitada pelo Pulse Repetition Frequency (PRF) utilizado na missão SAR. Para a velocidade em azimute é usada uma técnica de Multilook que vai executar uma correlação cruzada entre dois look’s consecutivos. Esta operação permite estimar o desvio que um alvo sofreu de um look para outro. Medindo a frequência central de cada look utilizado é possível estimar a velocidade em azimute