Blind recovery of k/n rate convolutional encoders in a noisy environment

http://jwcn.eurasipjournals.com/content/2011/1/168International audienceIn order to enhance the reliability of digital transmissions, error correcting codes are used in every digital communication system. To meet the new constraints of data rate or reliability, new coding schemes are currently being developed. Therefore, digital communication systems are in perpetual evolution and it is becoming very difficult to remain compatible with all standards used. A cognitive radio system seems to provide an interesting solution to this problem: the conception of an intelligent receiver able to adapt itself to a specific transmission context. This article presents a new algorithm dedicated to the blind recognition of convolutional encoders in the general k/n rate case. After a brief recall of convolutional code and dual code properties, a new iterative method dedicated to the blind estimation of convolutional encoders in a noisy context is developed. Finally, case studies are presented to illustrate the performances of our blind identification method

Some interesting dual-code properties of convolutional encoder for standards self-recognition

In Special Issue : Cognitive CommunicationsInternational audienceFor enhancement of the quality of digital transmissions, standards are in continual evolution, which generates compatibility problems. Cognitive radio systems permit one to solve this problem through the design of intelligent receivers. However, such receivers must be able to adapt themselves to a specific transmission context. This requires the development of new methods in order to blindly estimate error-correcting codes. Coding schemes such as turbocode, composed of convolutional codes, belong to a family of error-correcting codes in use in many standards. In most of the methods designed to identify convolutional encoders the algebraic properties are used implicitly. However usually, these dedicated properties are neither explicated, nor detailed, nor demonstrated. The study reported here investigates the algebraic properties of convolutional encoders, useful for blind recognition methods in the cognitive radio context and more specially the algebraic relationships between different forms of a convolutional code and its corresponding dual code. Moreover, some simulation results are presented to show the relevance of these properties for the blind identification of the convolutional encoder

Classification de mines sous-marines à partir de l'image sonar brute : caractérisation du contour de l'ombre portée par algorithme génétique

National audienceDans le domaine de la chasse aux mines sous-marines, l'objet détecté peut être caractérisé par son ombre portée sur le fond. L'approche classique est séquentielle : l'image sonar est tout d'abord segmentée afin d'obtenir une image binaire partageant les pixels entre la zone d'ombre et la zone de réverbération de fond, puis des attributs caractéristiques sont extraits de la silhouette 2-D correspondant à l'ombre segmentée lesquels servent à classifier l'objet en fin de traitement. A chacune des étapes sont généralement associés des pré- et/ou post-traitements visant à éviter qu'une erreur intervenant à un instant donné de la chaîne de traitement se répercute jusqu'au résultat final. Afin d'optimiser la procédure de classification en se concentrant sur l'objectif ultime de la chaîne de traitement, nous avons mis en œuvre un processus dynamique pour caractériser le contour de l'ombre à partir de l'image sonar brute en offrant en outre la possibilité de classifier l'objet détecté. Cette approche innovante fait appel aux notions de modèles déformables, modèles statistiques et algorithmes génétiques

Blind Recognition of Linear Space Time Block Codes

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceThe blind recognition of communication parameters is a key research issue for commercial and military communication systems. In this paper, we investigate the problem of the blind recognition of Linear Space-Time Block Codes (STBC). To characterize the space time coding, we propose to compute a time-lag correlation of the received samples. Provided the number of transmitters, the noise variance and the symbol timing are well estimated, we show that the theoretical values of the correlation norm only depend on the STBC and are affected by neither the channel nor the symbol modulation. The automatic recognition of the STBC is realized by selecting the STBC which minimizes the distance between the theoretical values and the experimental ones. Simulations show that our method performs well even for low signal to noise ratio (0dB)

Mine Classification based on raw sonar data: an approach combining Fourier Descriptors, Statistical Models and Genetic Algorithms

International audienceIn the context of mine warfare, detected mines can be classified from their cast shadow. A standard solution is to perform image segmentation first (we obtain binary from graylevel image giving the label zero for pixels belonging to the shadow and the label one elsewhere), and then to perform a classification based on features extracted from the 2D-shape of the segmented shadow. Consequently, if a mistake happens during the process, it will be propagated through the following steps. In this paper, to avoid such drawbacks, we propose a novel approach where a dynamic segmentation scheme is fully classification-oriented. Actually, classification is performed directly from the raw image data. The approach is based on the combination of deformable models, genetic algorithms, and statistical image models

Blind Detection of the Number of Communication Signals Under Spatially Correlated Noise by ICA and K-S Tests

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceThe issue addressed in this paper is the determination of the number of communication signals in a sensor array. Most of the available algorithms rely on the spatial uncorrelation of the additive noise. In practice, this condition is rarely satisfied when the receivers are not sufficiently spaced (MIMO communications for example). In this paper, we propose a new method to detect the number of communication signals based on the fact that the signals are independent and non gaussian and that the background noise is gaussian. By using an Independent Component Analysis in conjunction with Kolomogorov-Smirnov (K-S) tests, the method can detect as many communication signals as the number of receiver antennas. Simulations results show that our method performs well in many environments like those with spatially correlated noise

A 2-D Filter Specification for Sonar Image Thresholding

International audienceWe propose a new image sonar segmentation by combining two complementary competencies. On the one hand, following an image processing approach, we aim at partitioning raw image data to provide a binary image. On the other hand, we take advantage of technological knowledge such as the principle of sonar image formation. For sonar images, grey-level histogram generally presents a single mode which entails a poor separation of two theoretical modes related to reverberation and shadow subpopulations of the image. The separation of these two modes is of critical interest in a further description of objects from their cast shadows. In this paper, an optimal filter is specified by a criterion which aims at changing the statistical properties of each area while making threshold value selection from the histogram easier. While minimizing the output pixels variance, pixel values in each region concentrate around the respective average value while, simultaneously, two distinct modes appear on the histogram of the filtered image. The minimum value found between the two modes in the smoothed histogram leads to the searched threshold. In addition, we show how filter aspect depends as well on the image sonar resolution as on sonar parameters

Mine Classification based on a Fuzzy Characterisation

International audienceHigh resolution sonars provide high-quality acoustic images, allowing the classification of objects from their cast shadow. For a given ground mine except mine with radial symmetry, shadow appearance generally depends on the point of view. After a segmentation step performed on images acquired along a part of a circular trajectory of the sonar around the object, we can match and superimpose binary data. The resulting image displays a fuzzy shadow region whose pixels grey-levels depend on their successive localisation in the images of the sequence, i.e. if they belong or not to the shadow region. As an extension of feature extraction in the binary case, fuzzy geometry is a practical tool to describe fuzzy regions characterised by the degree of membership of each pixel to them. After a Principal Component Analysis applied to a set of fuzzy features, encouraging results have been achieved on simulated sonar images covering both classical and stealthy mines

Phase-coded Radar Waveform Design with Quantum Annealing

The Integrated Side Lobe Ratio (ISLR) problem we consider here consists in finding optimal sequences of phase shifts in order to minimize the mean squared cross-correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP-hard. In our work, we aim to take advantage of the exponential scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimization problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, we discuss the performances and the scalability of our approach on the D-Wave quantum computer. More broadly, our work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimization problems.Comment: 11 pages, 4 figures, 1 table, to be published in IET Radar, Sonar and Navigatio

Synthetic Aperture Radar Image Segmentation with Quantum Annealing

In image processing, image segmentation is the process of partitioning a digital image into multiple image segment. Among state-of-the-art methods, Markov Random Fields (MRF) can be used to model dependencies between pixels, and achieve a segmentation by minimizing an associated cost function. Currently, finding the optimal set of segments for a given image modeled as a MRF appears to be NP-hard. In this paper, we aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of Synthetic Aperture Radar images. For that purpose, we propose an hybrid quantum annealing classical optimization Expectation Maximization algorithm to obtain optimal sets of segments. After proposing suitable formulations, we discuss the performances and the scalability of our approach on the D-Wave quantum computer. We also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimization problems.Comment: 13 pages, 6 figures, to be published in IET Radar, Sonar and Navigatio