Local feature based automatic target recognition for future 3D active homing seeker missiles

Abstract

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3 rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes.

Ptychographic X-ray speckle tracking

“…The algorithm presented here is by no means the only approach to solve for the phase gradients and the reference. Indeed, similar problems emerge in many areas of imaging such as computer vision (Demirci et al, 2006), medical imaging (Thirion, 1998) and military targeting applications (Kechagias-Stamatis et al, 2018). In magnetic resonance imaging, the process of identifying the distortions that relate an image to its reference is often termed the 'image registration' problem and generating the reference from a set of distorted views is termed 'atlas construction'.…”

Fusing Deep Learning and Sparse Coding for SAR ATR

“…Then the remapped images are converted into a with base elements the 1D SAR feature vectors of the corresponding SAR training images as created by Eq. (4). In contrast to current SC based SAR ATR methods [8], [11], [14], we do not create the 1D SAR feature vectors from pre-processed grayscale SAR images but from the raw grayscale SAR images.…”

“…Target Recognition (ATR) algorithms to avoid collateral damage and fratricide. During the last decades, both industry and academia have made several ATR attempts in various data domains such as 2D Infrared [1], 3D Light Detection and Ranging (LIDAR) [2]- [4] and 2D Synthetic Aperture Radar [5]- [19] (SAR). Despite each data modality having its own advantages, SAR imagery is appealing because it can be obtained under all-weather night-and-day conditions extending considerably the operational capabilities in the battlefield.…”

“…for Gaussian type. Therefore, given a test SAR image, we first remap it according to Eq (4). and then analyze its core structure to identify if it is a Gaussian or a non-Gaussian type.…”

Target recognition for synthetic aperture radar imagery based on convolutional neural network feature fusion

“…Investigations involve solutions based on numerous spatial, i.e. 2D/ 3D and data domains, such as 2D infrared (IR) [1][2][3][4][5] , 2D Synthetic Aperture Radar (SAR) [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] , 2D Inverse SAR (ISAR) 22 and 3D Light Detection and Ranging (LIDAR) [23][24][25][26][27] , with each of these data modalities having its own strengths and weaknesses. For example, state-of-the-art local feature (data) descriptors from the visual domain have already proven their capabilities in the IR domain, but IR suffers from the time of day and the target's history 28 .…”