The most fundamental problem in radar is the detection of an object or a physical phenomenon. This requires proper discrimination between signal and noise content at the receiver even after the echo containing target information is surrounded by clutter. Traditionally, a series of signal processing operations are carried out to perform this discrimination with varying levels of success. These s...

High resolution range profile (HRRP) plays an important role in wideband radar automatic target recognition (ATR). In order to alleviate the sensitivity to clutter and target aspect, employing a sequence of HRRP is a promising approach to enhance the ATR performance. In this paper, a novel HRRP sequence-matching method based on singular value decomposition (SVD) is proposed. First, the HRRP seq...

A speech translation system will transform a spoken dialogue from the speaker's language to the listener’s automatically and simultaneously. It will undoubtedly be used to overcome language barriers and facilitate communication among the peoples of the world. Creation of such a system will first require developing the various constituent technologies: speech recognition, machine translation, an...

The application of spatial matched filter classifiers to the synthetic aperture radar (SAR) automatic target recognition (ATR) problem is being investigated at MIT Lincoln Laboratory. Initial studies investigating the use of several different spatial matched filter classifiers in the framework of a 2D SAR ATR system are summarized. In particular, a new application is presented of a shift-invari...

The US Air Force Research Laboratory (AFRL) is exploring the decision-level fusion (DLF) trade space in the Fusion for Identifying Targets Experiment (FITE) program. FITE is surveying past DLF approaches and experiments. This paper reports preliminary findings from that survey, which ultimately plans to place the various studies in a common framework, identify trends, and make recommendations o...

Synthetic aperture radar (SAR) is valuable military tool that can generate high-resolution images in any illumination or weather condition. As SAR be used for hostile purposes, it important to develop jamming techniques degrade the quality of images. Jamming performance evaluation (JPE) metrics are evaluate effectiveness systems. Existing JPE using Euclidean distance correlation coefficients, b...

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack SAR image datasets and high cost labeling, these existing DL based approaches can only accurately recognize training dataset. Therefore, precision identification unknown targets practical applications is one important capab...

This work proposes a new automatic target classifier, based on a combined neural networks’ system, by ISAR image processing. The novelty introduced in our work is twofold. We first present a novel automatic classification procedure, and then we discuss an improved multimedia processing of ISAR images for automatic object detection. The classifier, composed by a combination of 20 feed-forward ar...

Synthetic Aperture Radar Imaging Simulated in MATLAB Matthew Schlutz This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB. The project is supervised by Dr. John Saghri and sponsored by Raytheon Space and Airborne Systems. SAR is a type of imaging radar in which the relative movement...

We are currently developing an Automatic Target Recognition (ATR) algorithm to locate an object using multisensor data. The ATR algorithm will determine corresponding points between a range (LADAR) image, a color (CCD) image, a thermal (FLIR) image and a BRL/CAD model of the object being located. The success of this process depends in part on which features can be automatically extracted from t...