Synthetic Aperture Radar (SAR) automatic target recognition (ATR) technology is one of the key technologies to achieve intelligent interpretation for SAR images. With rapid development deep learning, neural networks have been successively used in ATR and show priority comparison with conventional methods. Recently, more attention paid robustness learning based The reason that maliciously modifi...

We present a model for classification performance estimation for synthetic aperture radar (SAR) automatic target recognition. We adopt a model-based approach, in which classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are compared using a Bayes likeliho...

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation ...

............................................................................................................... 13 CHAPTER 1 – INTRODUCTION ........................................................................... 15 CHAPTER 2 – AUTOMATIC TARGET RECOGNITION .................................. 22 2.1 Automatic Target Recognition for Monostatic Radar ................................. 22 2.1.1 Pr...

Many surveillance systems incorporate High Range Resolution (HRR) radar and Synthetic Aperture Radar (SAR) modes to be able to capture moving and stationary targets. Feature-, signature-, and categorical-aided tracking and Automatic Target Recognition (ATR) applications benefit from HRR radar processing. Successful Simultaneous Tracking and Identification (STID) [6, 12, 65] applications exploit...

Deep learning based synthetic aperture radar automatic target recognition (SAR-ATR) plays an significant role in the military and civilian fields. However, data limitation large computational cost are still severe challenges actual application of SAR-ATR. To improve performance CNN model with limited samples SAR-ATR, this paper proposes a novel multi-domain feature subspaces fusion representati...

In this thesis, a new algorithm to improve automatic target recognition techniques on High Range Resolution (HRR) Profiles is presented and also a number of ways are investigated for target detection using Synthetic Aperture Radar (SAR) images. A new 1-D hybrid Automatic Target Recognition (ATR) algorithm is developed for sequential High Range Resolution (HRR) radar signatures. The proposed hyb...

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the of CNNs depends heavily on a large amount training data. The insufficiency labeled SAR images limits and even invalidates some ATR methods. Furthermore, under few data, many existing are ineffective. To address these challenges, we propose Semi-s...

Distortion-tolerant correlation filter methods have been applied to many video-based automatic target recognition (ATR) applications, but in a single-frame architecture. In this paper we introduce an efficient framework for combining information from multiple correlation outputs in a probabilistic way. Our framework is capable of handling scenes with an unknown number of targets at unknown posi...

Target recognition systems using Synthetic Aperture Radar (SAR) data require well-focused target imagery to achieve high probability of correct classification. Techniques for improving the image quality of complex SAR imagery are investigated. The application of phase gradient re-focusing of target imagery having crossrange smearing is shown to significantly improve the target recognition perfo...