Browsing by Author "Merlet, Thomas"

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    Characterisation of sidelobes for multibeam radar based on quasi-orthogonal LFM waveforms
    (IEEE, 2020-06-11) Balleri, Alessio; Kocjancic, Leon; Merlet, Thomas
    Multibeam radars (MBRs) enable multiple independent channels by simultaneously exploiting spatial and waveform diversity. Orthogonal waveforms are employed to form multiple independent antenna beams, each one providing a different function and using different dedicated radar resources. This paper investigates sidelobe levels in MBRs and presents a comparison with those of an Electronic Steerable Array (ESA) that employs a single waveform in transmission to generate multiple simultaneous beams. Simulations are carried out for a 3-channel MBR transmitting quasi-orthogonal Linear Frequency Modulated (LFM) waveforms at Ku band. The response of the MBR to an ideal point target as a function of aspect angle as well as that to multiple targets in different locations has been investigated. Results corroborate the analytical findings and show that the sidelobe levels with respect to angle, at the target range, are attenuated by the cross-ambiguity function properties between the waveforms employed. The range response to a target in low channel isolation suffers from cross-channel interference that may alter the noise floor characteristics of the radar, hence stressing the importance of suitable waveform selection.
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    Explainability of deep SAR ATR through feature analysis
    (IEEE, 2020-10-20) Belloni, Carole; Aouf, Nabil; Balleri, Alessio; Le Caillec, Jean-Marc; Merlet, Thomas
    Understanding the decision-making process of deep learning networks is a key challenge which has rarely been investigated for Synthetic Aperture Radar (SAR) images. In this paper, a set of new analytical tools is proposed and applied to a Convolutional Neural Network (CNN) handling Automatic Target Recognition (ATR) on two SAR datasets containing military targets.
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    Multibeam radar based on linear frequency modulated waveform diversity
    (IET, 2018-08-09) Kocjancic, Leon; Balleri, Alessio; Merlet, Thomas
    Multibeam radar (MBR) systems based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver with digital signal processing. Linear frequency modulated (LFM) signals are extensively used in radar systems due to their pulse compression properties, Doppler tolerance, and ease of generation. Here, the authors investigate the level of isolation between MBR channels based on LFM chirps with rectangular and Gaussian amplitude envelopes. The orthogonal properties and the mathematical expressions of the isolation are derived as a function of the chirp design diversity, and specifically for diverse frequency slopes and frequency offsets. The analytical expressions are validated with a set of simulations as well as with experiments at C-band using a rotating target.
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    Numerical characterisation of quasi-orthogonal piecewise linear frequency modulated waveforms
    (IEEE, 2019-07-01) Kocjancic, Leon; Balleri, Alessio; Merlet, Thomas
    This paper presents an analysis of the Doppler tolerance and isolation properties of five different sets of piecewise linear frequency modulated (PLFM) waveform triplets consisting of a combination of LFM subchirps. Different combinations of PLFM signals are used to produce waveforms with the same time-bandwidth product and optimise them with respect to isolation. The performance of the proposed waveforms are numerically investigated and a comparison between sets is presented. Results confirm that the waveforms have quasi-orthogonal properties and exhibit a degree of Doppler tolerance.
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    Pose-informed deep learning method for SAR ATR
    (The institution of Engineering and Technology (IET), 2020-03-30) Belloni, Carole; Aouf, Nabil; Balleri, Alessio; Le Caillec, Jean-Marc; Merlet, Thomas
    Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.
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    SAR image dataset of military ground targets with multiple poses for ATR
    (SPIE, 2017-10-05) Belloni, Carole; Balleri, Alessio; Aouf, Nabil; Merlet, Thomas; Le Caillec, Jean-Marc
    Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability.Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques arecurrently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms.