A NUFFT Based Step-frequency Chirp Signal High Resolution Imaging Algorithm and Target Recognition Algorithm

Radar Automatic Target Recognition (RATR) is the key technique to be breaked through in the fuure development of intelligent weapon system. Compared to the 2-D SAR image target recognition, High Resolution Range Profile (HRRP) target recognition has the advantage of low data dimension, low requirement of radar system's calculation and storage ability, and the imaging algorithm is also not complicated. HRRP imaging is the first and the key process in target recognition, its speed and imaging quality can directly influence the real-time capability and accuracy of target recognition. In this paper a new HRRP imaging algorithm — NUFFT algorithm is proposed, the derivation of mathematical expression is given, both for the echo simulation process and the imaging process. In the meantime, by analyzing each step's calculation complexity, we compared the calculation complexity of four different imaging algorithms, we also simulate two target's imaging and target recognition processing. Theoretical analysis and simulation both prove that the proposed algorithm's calculation complexity is improved in various degree compared with the others, thus can be effectively used in target recognition

Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description

Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance

A SAR Image Target Recognition Approach via Novel SSF-Net Models

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1

Extraction of Micro-Doppler Feature Using LMD Algorithm Combined Supplement Feature for UAVs and Birds Classification

In the past few decades, the demand for reliable and robust systems capable of monitoring unmanned aerial vehicles (UAVs) increased significantly due to the security threats from its wide applications. During UAVs surveillance, birds are a typical confuser target. Therefore, discriminating UAVs from birds is critical for successful non-cooperative UAVs surveillance. Micro-Doppler signature (m-DS) reflects the scattering characteristics of micro-motion targets and has been utilized for many radar automatic target recognition (RATR) tasks. In this paper, the authors deploy local mean decomposition (LMD) to separate the m-DS of the micro-motion parts from the body returns of the UAVs and birds. After the separation, rotating parts will be obtained without the interference of the body components, and the m-DS features can also be revealed more clearly, which is conducive to feature extraction. What is more, there are some problems in using m-DS only for target classification. Firstly, extracting only m-DS features makes incomplete use of information in the spectrogram. Secondly, m-DS can be observed only for metal rotor UAVs, or large UAVs when they are closer to the radar. Lastly, m-DS cannot be observed when the size of the birds is small, or when it is gliding. The authors thus propose an algorithm for RATR of UAVs and interfering targets under a new system of L band staring radar. In this algorithm, to make full use of the information in the spectrogram and supplement the information in exceptional situations, m-DS, movement, and energy aggregation features of the target are extracted from the spectrogram. On the benchmark dataset, the proposed algorithm demonstrates a better performance than the state-of-the-art algorithms. More specifically, the equal error rate (EER) proposed is 2.56% lower than the existing methods, which demonstrates the effectiveness of the proposed algorithm

Reconnaissance de formes et d objets en environnement incertain (application à la reconnaissance de cibles radar)

La reconnaissance automatique de cibles radar trouve de nombreuses applications en environnement incertain aérien et maritime. Par exemple, pour le cas du trafic des navires qui devient de plus en plus important, et pour le cas des risques de pollution qui sont toujours présents au quotidien. Il s avère donc nécessaire d introduire des méthodes originales permettant la mise à disposition de traitements automatiques pour l aide à la reconnaissance de cibles à partir des images radar ISAR (Inverse Synthetic Aperture Radar). D autre part, le volume de données radar devient de plus en plus très important, ceci conduit à l étude des méthodes semi-automatiques de reconnaissance en faisant intervenir l opérateur humain dans le processus. La méthodologie adoptée dans le cadre de cette thèse est inspirée du processus d extraction de connaissances à partir de données (ECD). Le processus ECD, adapté au domaine radar, est constitué de quatre grandes phases allant de l acquisition et la préparation des données (prétraitement des données et extraction des paramètres) jusqu à l interprétation et l évaluation des résultats, en passant par la phase de classification. Après l acquisition des signaux radar et la reconstruction des images ISAR par l analyse de Fourier, l extraction des caractéristiques les plus discriminantes et en particulier, celles de la forme est réalisée. Dans la phase de classification, nous utilisons dans un premier temps les méthodes de classification telles que les machines à vecteur de support et les k plus proches voisins et dans un deuxième temps nous étudions l influence de la fusion d informations sur les performances globales de reconnaissance. Enfin, nous proposons une autre approche qui fait inclure la pose de la cible au sein du système de reconnaissance.This thesis presents Radar Automatic Target Recognition (RATR) in uncertam environment using Inverse Synthetic Aperture Radar (ISAR) images. By including the human operator in the system, the recognition process 15 achieved from the acquisition step aux! the image reconstruction to the features extraction and the classification step. The methodology adopted in this thesis is inspired from the artificial intelligence approach. This methodology is known as Knowledge Discovery from Data (KDD) which we have adapted to radar target recognition system. After the radar signal acquisition from an ahechoic chamber of ENSIETA (Brest, France) and the ISAR images reconstruction by Fourier analysis, the most discriminant features, in particular the shapes of targets are extracted. The classification stage is performed by supervised methods such as Support Vector Machines (SVM) and K-Nearest Neighbors (K-NN). Then, we investigate the impact of information fusion on recognition performance using fusion methods like the theory of belief functions and the majority vote rule. Finally, we propose another approach that included the pose of the targets in the recognition system.BREST-BU Droit-Sciences-Sports (290192103) / SudocSudocFranceMoroccoFRM