New Approaches to Pulse Compression Techniques of Phase-Coded Waveforms in Radar

The present thesis aims to make an in-depth study of Radar pulse compression, Neural Networks and Phase coded pulse compression codes. Pulse compression is a method which combines the high energy of a longer pulse width with the high resolution of a narrow pulse width. The major aspects that are considered for a pulse compression technique are signal to sidelobe ratio (SSR) performance, noise performance and Doppler shift performance. Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask important information. The adaptive filtering techniques like Least Mean Square (LMS), Recursive Least Squares (RLS), and modified RLS algorithms are used for pulse radar detection and the results are compared. In this thesis, a novel approach for pulse compression using Recurrent Neural Network (RNN) is proposed. The 13-bit and 35-bit barker codes are used as signal codes to RNN and results are compared with Multilayer Perceptron (MLP) network. RNN yields better signal-to-sidelobe ratio (SSR), error convergence speed, noise performance, range resolution ability and Doppler shift performance than neural network (NN) and some traditional algorithms like auto correlation function(ACF) algorithm. But the SSR obtained from RNN is less for most of the applications. Hence a Radial Basis Function (RBF) neural network is implemented which yields better convergence speed, higher SSRs in adverse situations of noise and better robustness in Doppler shift tolerance than MLP and ACF algorithm. There is a scope of further improvement in performance in terms of SSR, error convergence speed, and Doppler shift. A novel approach using Recurrent RBF is proposed for pulse radar detection, and the results are compared with RBF, MLP and ACF. Biphase codes, namely barker codes are used as inputs to all these neural networks. The disadvantages of biphase codes include high sidelobes and poor Doppler tolerance. The Golay complementary codes have zero sidelobes but they are poor Doppler tolerant as that of biphase codes. The polyphase codes have low sidelobes and are more Doppler tolerant than biphase codes. The polyphase codes namely Frank, P1, P2, P3, P4 codes are described in detail and autocorrelation outputs, phase values and their Doppler properties are discussed and compared. The sidelobe reduction techniques such as single Two Sample Sliding Window Adder (TSSWA) and double TSSWA after the autocorrelator output are discussed and their performances for P4 code are presented and compared. Weighting techniques can also be applied to substantially reduce the range time sidelobes. The weighting functions such as Kaiser-Bessel amplitude weighting function and classical amplitude weighting functions (i.e. Hamming window) are described and are applied to the receiver waveform of 100 element P4 code and the autocorrelation outputs, Peak Sidelobe Level (PSL), Integrated Sidelobe Level (ISL) values are compared with that of rectangular window. The effects of weighting on the Doppler performance of the P4 code are presented and compared

The RRBF. Dynamic representation of time in radial basis function network

International audienc

Contribution à la surveillance des systèmes de production à l'aide des réseaux de neurones dynamiques : Application à la e-maintenance

Alain BOURJAULT : Professeur à l'ENSMM de Besançon, Jean-Marc FAURE : Professeur à l'ISMCM-CESTI de Paris Denis HAMAD : Professeur à Université du Littoral Côte d'Opale, Calais Raphaël LABOURIER : PDG Sté. AVENSY Ingénierie, Besançon Daniel NOYES : Professeur à l'ENI de Tarbes Daniel RACOCEANU : Maître de Conférences à l'Université de Franche-Comté Jean-Pierre THOMESSE : Professeur à l'ENSEM-INPL de Nancy, Noureddine ZERHOUNI : Professeur à l'ENSMM de BesançonThe industrial monitoring methods are divided into two categories: monitoring methods based on the existence of the equipment formal model, and those which not use any equipment formal model. Generally, there are many uncertainties in the formal model and for complex industrial equipment, it is very difficult to obtain a correct mathematical model. This thesis presents an application of the artificial neural networks to the industrial monitoring. We propose a new architecture of Radial Basis Function Networks which exploits the dynamic properties of the locally recurrent architectures for taking into account the input data temporal aspect. Indeed, the consideration of the dynamic aspect requires rather particular neural networks architectures with special training algorithms which are often very complicated. In this sense, we propose an improved version of the k-means algorithm which allows to determine easily the neural network parameters. The validation tests show that at the convergence of the learning algorithm, the neural network is situated in the zone called « good generalization zone ». The neural network was then decomposed into elementary functions easily interpretable in industrial automation languages. The applicative part of this thesis shows that a real-time monitoring treatment is possible thanks to the automation architectures. The neural network loaded in a PLC is completely configurable at distance by the TCP/IP communication protocol. An Internet connection allows then a distant expert to follow the evolution of its equipment, and also to validate the artificial neural network learning.Les méthodes de surveillance industrielle sont divisées en deux catégories : méthodes de surveillance avec modèle formel de l'équipement, et méthodes de surveillance sans modèle de l'équipement. Les modèles mathématiques formels des équipements industriels sont souvent entachés d'incertitudes et surtout difficiles à obtenir. Cette thèse présente l'application des réseaux de neurones artificiels pour la surveillance d'équipements industriels. Nous proposons une architecture de Réseaux à Fonctions de base Radiales qui exploite les propriétés dynamiques des architectures localement récurrentes pour la prise en compte de l'aspect temporel des données d'entrée. En effet, la prise en compte de l'aspect dynamique nécessite des architectures de réseaux de neurones particulières avec des algorithmes d'apprentissage souvent compliqués. Dans cette optique, nous proposons une version améliorée de l'algorithme des k-moyennes qui permet de déterminer aisément les paramètres du réseau de neurones. Des tests de validation montrent qu'à la convergence de l'algorithme d'apprentissage, le réseau de neurones se situe dans la zone appelée « zone de bonne généralisation ». Le réseau de neurones a été ensuite décomposé en fonctions élémentaires facilement interprétables en langage automate. La partie applicative de cette thèse montre qu'un traitement de surveillance en temps réel est possible grâce aux architectures à automates programmables industriels. Le réseau de neurones chargé dans l'automate est entièrement configurable à distance par le protocole de communication TCP/IP. Une connexion Internet permet alors à un expert distant de suivre l'évolution de son équipement et également de valider l'apprentissage du réseau de neurones artificiel