Topic
Automatic target recognition
About: Automatic target recognition is a research topic. Over the lifetime, 2777 publications have been published within this topic receiving 32118 citations.
Papers published on a yearly basis
Papers
TL;DR: A new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used, which can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
Abstract: The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
1,324 citations
Book•
1 Jan 1992
TL;DR: This paper presentsractical Aspects of Multisensor Tracking, and an Engineer's Guide to Variable-Structure Multiple-Model Estimation for Tracking.
Abstract: Practical Aspects of Multisensor Tracking Survey of Assignment Techniques for Multitarget Tracking IMM Estimator with Nearest Neighbor Joint Probabilistic Data Association Multiassignment for Tracking a Large Number of Overlapping Objects Finite Difference Methods for Nonlinear Filtering and Automatic Target Recognition Large Scale Ground Target Tracking with Single and Multiple MTI Sensors Radar Systems Modeling for Tracking ECM Modeling for Multitarget Tracking and Data Association Waveform Detection/Tracking Performance at the System Level Engineer's Guide to Variable-Structure Multiple-Model Estimation for Tracking
1,001 citations
TL;DR: A new database of aerial images provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments and gives the performance of baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.
639 citations
TL;DR: Experimental results showed that SVMs outperform conventional classifiers in target classification because SVMs with the Gaussian kernels are able to form a local "bounded" decision region around each class that presents better rejection to confusers.
Abstract: Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local "bounded" decision region around each class that presents better rejection to confusers.
584 citations
TL;DR: This paper presents a framework for feature extraction predicated on parametric models for the radar returns, and presents statistical analysis of the scattering model to describe feature uncertainty, and provides a least-squares algorithm for feature estimation.
Abstract: High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.
552 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 11 |
| 2024 | 25 |
| 2023 | 74 |
| 2022 | 138 |
| 2021 | 75 |
| 2020 | 92 |