Automatic Recognition of Isar Images of Multiple Targets and Atr Results

Abstract

Inverse synthetic aperture radar (ISAR) imaging is an effective method to identify unknown targets regardless of weather and illumination conditions. Research results published regarding this topic have focused mainly on imaging and automatic target recognition (ATR) of single targets. However, targets generally fly in formation, so the applicability of ISAR images to ATR of multiple targets must be studied. This paper proposes an ATR procedure for targets flying in formation. ATR accuracy derived using five targets composed of point scatterers and the measured radar signal of a Boeing 747 aircraft was as high as that of the solo flight in terms of SNR and the size of the training database; this result shows that ISAR is an adequate tool for ATR even if an image is contaminated by radar reflections from neighboring targets.

ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform

“…Inverse synthetic aperture radar (ISAR) imaging has been attracting a lot of attention from many radar researchers in recent years [1][2][3]. ISAR imaging technology provides a new way of thinking in automatic aircraft models classification, which is the classification of small boats in littoral environments via polarimetric ISAR and automatic target recognition (ATR) for multiple targets flying in formation [4][5][6]. Different from other imaging systems of the target [7], the main idea of ISAR imaging is to exploit Doppler information of echoes caused by the rotation of targets.…”

ISAR geometry, signal model, and image processing algorithms

“…Algorithms for multi‐feature‐based ISAR image recognition of ship targets and automatic polarimetric ISAR image recognition based on a model matching are discussed in [14, 15]. Automatic target recognition of ISAR images of multiple targets and ATR results and algorithms for automatic ISAR images recognition and classification are presented in [16–18]. An auto target recognition method based on ISAR image is presented in [19] using Fourier coefficients, the wavelet coefficients, and shape features of the ISAR image, and three kinds of classifiers: nearest‐neighbour, Back Propagation (BP) neural network, and SVM are considered and experimented.…”

ISAR Image Recognition Algorithm and Neural Network Implementation

“…Algorithms for multi-feature based ISAR image recognition of ship targets and automatic polarimetric ISAR image recognition based on a model matching are discussed in [2,3]. Automatic target recognition of ISAR images of multiple targets and ATR results, and algorithms for automatic ISAR images recognition and classification are presented in [4][5][6]. Based on a comparison of range-Doppler imagery to 3D ship reference models an ISAR image classification is discussed in [7].…”