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Wind information on SAR images are essential to characterize a marine environment in offshore or coastal area. More and more applications require high resolution wind field estimation. In this article, classical wind wave direction estimation methods are reviewed as the spectral or gradient approaches. In addition, a way to enhance the spectral method with the Radon transform is proposed. The aim of this document is to determine which method provides greatest results when the resolution grid is finer. Therefore, the methods accuracy, fidelity and uncertainty are compared through a simulation study, a section with RadarSAT2 data in coastal area and another one with Sentinel-1 measurements in offshore area.
Alexandre Corazza; Ali Khenchaf; Fabrice Comblet. Assessment of Wind Direction Estimation Methods from SAR Images. Remote Sensing 2020, 12, 3631 .
AMA StyleAlexandre Corazza, Ali Khenchaf, Fabrice Comblet. Assessment of Wind Direction Estimation Methods from SAR Images. Remote Sensing. 2020; 12 (21):3631.
Chicago/Turabian StyleAlexandre Corazza; Ali Khenchaf; Fabrice Comblet. 2020. "Assessment of Wind Direction Estimation Methods from SAR Images." Remote Sensing 12, no. 21: 3631.
In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe . Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.
Truong-Ngoc Tan; Ali Khenchaf; Fabrice Comblet; Pierre Franck; Jean-Marc Champeyroux; Olivier Reichert. Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning. Applied Sciences 2020, 10, 4335 .
AMA StyleTruong-Ngoc Tan, Ali Khenchaf, Fabrice Comblet, Pierre Franck, Jean-Marc Champeyroux, Olivier Reichert. Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning. Applied Sciences. 2020; 10 (12):4335.
Chicago/Turabian StyleTruong-Ngoc Tan; Ali Khenchaf; Fabrice Comblet; Pierre Franck; Jean-Marc Champeyroux; Olivier Reichert. 2020. "Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning." Applied Sciences 10, no. 12: 4335.
In this study, the investigation of the electromagnetic scattering by modified targets is presented. A metallic notched plate and disc are considered. Two Gaussian beam (GB) techniques have been applied: GB launching (GBL) and GB summation (GBS). The theoretical formulations of GBL and GBS are established. The simulations of the radar cross-section (RCS) of the flat and notched targets (plate and disc) are presented. The application and evaluation of GBS and GBL methods are extended through the studied variations of the RCS in several scattering angles and different radar frequencies. The numerical RCS results are compared with those given by the physical optic model and the method of moments. Moreover, the numerical results are evaluated with measurements conducted in an anechoic chamber.
Helmi Ghanmi; Ali Khenchaf; Philippe Pouliguen. Investigation of RCS of modified targets using experimental measurements and GB methods. IET Radar, Sonar & Navigation 2019, 13, 1403 -1410.
AMA StyleHelmi Ghanmi, Ali Khenchaf, Philippe Pouliguen. Investigation of RCS of modified targets using experimental measurements and GB methods. IET Radar, Sonar & Navigation. 2019; 13 (9):1403-1410.
Chicago/Turabian StyleHelmi Ghanmi; Ali Khenchaf; Philippe Pouliguen. 2019. "Investigation of RCS of modified targets using experimental measurements and GB methods." IET Radar, Sonar & Navigation 13, no. 9: 1403-1410.
This paper addresses the estimation of the height of a point scatterer over a sea surface via multipath exploitation for a High Range Resolution radar that is using pulse range compression, such as Synthetic Aperture Radars. We first focus our attention on the physical model, in particular on the specular/diffuse reflection coefficients, this coefficients being derived from the empirical Miller Brown and Vegh model. The gravity waves are also simulated since they modify the acquisition geometry such as the local grazing angle. Secondly, the signal model is derived, thus allowing an easy derivation of the time delays (direct echo and replicas), these time delays being converted into a height estimation for possible automatic ship recognition applications. Our algorithm is a non-conventional radar signal processing, in other words it uses the backscattered pulse over before range compression and demodulation. The aim of the paper is to understand for which radar and sea parameters, as well as acquisition scenes, it is possible to extract the scatterer height information using the multipath of the backscattered electromagnetic wave.
Jean-Marc Le Caillec; Jérôme Habonneau; Ali Khenchaf. Ship Profile Imaging Using Multipath Backscattering. Remote Sensing 2019, 11, 748 .
AMA StyleJean-Marc Le Caillec, Jérôme Habonneau, Ali Khenchaf. Ship Profile Imaging Using Multipath Backscattering. Remote Sensing. 2019; 11 (7):748.
Chicago/Turabian StyleJean-Marc Le Caillec; Jérôme Habonneau; Ali Khenchaf. 2019. "Ship Profile Imaging Using Multipath Backscattering." Remote Sensing 11, no. 7: 748.
This paper is devoted to investigating the electromagnetic (EM) backscattering from slick-free and slick-covered sea surfaces at various bands (L-band, C-band, X-band, and Ku-band) by using the second-order small slope approximation (SSA-2) and the measured synthetic aperture radar (SAR) data. It is known that the impact of slick on sea surface is mainly caused by two factors: the Marangoni damping effect and the reduction of friction velocity. In this work, the influences induced by these two factors on the sea curvature spectrum, the root mean square (RMS) height, the RMS slope, and the autocorrelation function of sea surfaces are studied in detail. Then, the slick-free and slick-covered sea surface profiles are simulated using the Elfouhaily spectrum and the Monte-Carlo model. The SSA-2 with the tapered incident wave is employed to simulate the normalized radar cross-sections (NRCSs) of sea surfaces. Furthermore, for slick-free sea surfaces, the NRCSs simulated with the SSA-2 at various bands are compared with those obtained by the first-order small slope approximation (SSA-1), the classic two-scale model (TSM), and the geophysical model functions (GMFs) at various bands, respectively. For slick-covered sea surfaces, the SSA-2-simulated NRCSs are compared with those obtained from C-band Radarsat-2 images and L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) images, respectively. The numerical simulations illustrate that the SSA-2 can be used to study the EM backscattering from slick-free and slick-covered sea surfaces, and it has more advantages than the SSA-1 and the TSM. The works presented in this paper are helpful for understanding the EM scattering from the sea surface covered with slick, in theory.
Honglei Zheng; Yanmin Zhang; Ali Khenchaf; Yunhua Wang; Helmi Ghanmi; Chaofang Zhao. Investigation of EM Backscattering from Slick-Free and Slick-Covered Sea Surfaces Using the SSA-2 and SAR Images. Remote Sensing 2018, 10, 1931 .
AMA StyleHonglei Zheng, Yanmin Zhang, Ali Khenchaf, Yunhua Wang, Helmi Ghanmi, Chaofang Zhao. Investigation of EM Backscattering from Slick-Free and Slick-Covered Sea Surfaces Using the SSA-2 and SAR Images. Remote Sensing. 2018; 10 (12):1931.
Chicago/Turabian StyleHonglei Zheng; Yanmin Zhang; Ali Khenchaf; Yunhua Wang; Helmi Ghanmi; Chaofang Zhao. 2018. "Investigation of EM Backscattering from Slick-Free and Slick-Covered Sea Surfaces Using the SSA-2 and SAR Images." Remote Sensing 10, no. 12: 1931.
The sea spectrum proposed by Elfouhaily et al. is widely employed in ocean remote sensing. In this paper, we find that there exists some disagreements between the results estimated using the Elfouhaily's angular spreading function and the empirical model NSCAT2 (which is regarded as a reliable reference in this paper). Firstly, the peak value of Δ(k) calculated with Elfouhaily's method monotone increases with wind speed. While the peak value of (k) estimated with the NSCAT2 first increases and then decreases with wind speed. Secondly, the wavenumber that corresponds to the peak value of Δ(k) isn't fixed at 363rad/m. To make the Elfouhaily's angular spreading function agrees well with the empirical model, some modifications are made on it with numerical fitting in this paper. The numerical simulations have shown that the modified angular spreading function could provide better predictions.
Honglei Zheng; Helmi Ghanmi; Ali Khenchaf; Chaofang Zhao; Yunhua Wang. A Modified Angular Spreading Function for Sea NRCS Estimation at Ku Band. 2018 IEEE Conference on Antenna Measurements & Applications (CAMA) 2018, 1 -4.
AMA StyleHonglei Zheng, Helmi Ghanmi, Ali Khenchaf, Chaofang Zhao, Yunhua Wang. A Modified Angular Spreading Function for Sea NRCS Estimation at Ku Band. 2018 IEEE Conference on Antenna Measurements & Applications (CAMA). 2018; ():1-4.
Chicago/Turabian StyleHonglei Zheng; Helmi Ghanmi; Ali Khenchaf; Chaofang Zhao; Yunhua Wang. 2018. "A Modified Angular Spreading Function for Sea NRCS Estimation at Ku Band." 2018 IEEE Conference on Antenna Measurements & Applications (CAMA) , no. : 1-4.
This paper presents numerical simulations and analyses of EM (electromagnetic) scattering from oil-free and oil-covered sea surface. First, the influences caused by slicks on clean sea are studied and analyzed with the action balance function. Slicks on sea surface make significant impacts on the wind input, the nonlinear wave-wave interactions and the viscous dissipation. A damping model based on the action balance equation is introduced. And then, simulations are made by assuming the surface height spectra proposed by Elfouhaily et al. and Hwang, respectively. The two scale model (TSM) is used to calculate the normalized radar cross sections (NRCS) of oil-free and oil-covered sea surfaces. Additionally, an UAVSAR image which was collected during the Deep Water Horizon oil spill accident occurred in the Gulf of Mexico is served as a reference. The numerical comparisons between simulated results and measured data have shown that, for clean sea surface, the VV polarized NRCS simulated with Elfouhaily spectrum agree well with UA VSAR data, the HV polarized NRCS simulated with Hwang spectrum agree well with UA VSAR data. For polluted sea surface, the VV polarized NRCS simulated with Elfouhaily spectrum matches well with measured data. Overall, numerical simulations with Elfouhaily spectrum seem better than Hwang spectrum in our simulations.
Honglei Zheng; Ali Khenchaf; Yunhua Wang; Ghanmi Helmi; Chaofang Zhao. Estimation of NRCS of Oil-Free and Oil-Covered Sea Surfaces at L-Band. Assessment with UAVSAR Data. 2018 International Conference on Radar (RADAR) 2018, 1 -4.
AMA StyleHonglei Zheng, Ali Khenchaf, Yunhua Wang, Ghanmi Helmi, Chaofang Zhao. Estimation of NRCS of Oil-Free and Oil-Covered Sea Surfaces at L-Band. Assessment with UAVSAR Data. 2018 International Conference on Radar (RADAR). 2018; ():1-4.
Chicago/Turabian StyleHonglei Zheng; Ali Khenchaf; Yunhua Wang; Ghanmi Helmi; Chaofang Zhao. 2018. "Estimation of NRCS of Oil-Free and Oil-Covered Sea Surfaces at L-Band. Assessment with UAVSAR Data." 2018 International Conference on Radar (RADAR) , no. : 1-4.
The microwave scatterometer is one of the most effective instruments in ocean remote sensing, which urges the need for some theoretical models to accurately estimate the scattering coefficient of the sea surface. For the simulation of the scattering from an ocean surface, the sea spectrum, or its inverse Fourier transform, autocorrelation function is essential. Currently, many sea spectral models have been proposed for describing sea waves. However, which spectrum should be adopted during electromagnetic (EM) computations? A systematic comparison of these models is needed to evaluate their accuracies. In this paper, we focus on numerical simulations of scattering from a rough sea surface in monostatic and bistatic configurations by using six different sea spectral models and the first-order small slope approximation (SSA-1). First, sea spectral models proposed by Elfouhaily et al., Hwang et al., Romeiser et al., Apel et al., Fung et al., and Pierson et al., are compared with each other from different points of view, e.g., the omnidirectional parts, the angular spreading functions, the autocorrelation functions, and the slope variances. We find that the spectra given by Elfouhaily and Hwang could reflect realistic wind sea waves more accurately. Then, the scattering coefficients are simulated in fully monostatic and bistatic configurations. Regarding the monostatic scattering, the results simulated using EM scattering models are compared with those obtained from the measured UAVSAR data in the L band and the empirical model CMOD5 in the C band. Comparisons are made for various incident angles, wind speeds, and wind directions. Meanwhile, special attention is paid to low to moderate incident angles. The comparisons show that, it is difficult to find one certain spectral model to simulate scattering coefficient accurately under all wind speeds or wind directions. Accurate estimations will be obtained using different methods according to different situations.
Honglei Zheng; Ali Khenchaf; Yunhua Wang; Helmi Ghanmi; Yanmin Zhang; Chaofang Zhao. Sea Surface Monostatic and Bistatic EM Scattering Using SSA-1 and UAVSAR Data: Numerical Evaluation and Comparison Using Different Sea Spectra. Remote Sensing 2018, 10, 1084 .
AMA StyleHonglei Zheng, Ali Khenchaf, Yunhua Wang, Helmi Ghanmi, Yanmin Zhang, Chaofang Zhao. Sea Surface Monostatic and Bistatic EM Scattering Using SSA-1 and UAVSAR Data: Numerical Evaluation and Comparison Using Different Sea Spectra. Remote Sensing. 2018; 10 (7):1084.
Chicago/Turabian StyleHonglei Zheng; Ali Khenchaf; Yunhua Wang; Helmi Ghanmi; Yanmin Zhang; Chaofang Zhao. 2018. "Sea Surface Monostatic and Bistatic EM Scattering Using SSA-1 and UAVSAR Data: Numerical Evaluation and Comparison Using Different Sea Spectra." Remote Sensing 10, no. 7: 1084.
Modern military targets as aircraft are able to perform high maneuvers due to their complex design. This ability of maneuvers includes sudden changes in acceleration and high-G turns that are not achievable from traditional military targets. Moreover, recent military targets generally have a low radar cross-section or low signal-to-noise ratio (SNR) profile, which makes the detection and tracking of those maneuvering targets a complicated dynamic state estimation problem. In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements is considered as an effective method for tracking and detecting a single maneuvering target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected by severe loss because of the mismatching of target model during maneuver. To resolve the target maneuvers, we propose an application of particle filtering, which depends on TBD. We employ the constant acceleration model and coordinate turn model. Our simulation results show that the detection and tracking of maneuvering target performance of TBDF has been improved using the proposed algorithm.
Naima Amrouche; Ali Khenchaf; Daoud Berkani. Tracking and detecting highs maneuvering weak targets. Journal of Applied Remote Sensing 2018, 12, 035001 .
AMA StyleNaima Amrouche, Ali Khenchaf, Daoud Berkani. Tracking and detecting highs maneuvering weak targets. Journal of Applied Remote Sensing. 2018; 12 (4):035001.
Chicago/Turabian StyleNaima Amrouche; Ali Khenchaf; Daoud Berkani. 2018. "Tracking and detecting highs maneuvering weak targets." Journal of Applied Remote Sensing 12, no. 4: 035001.
C band microwave radars have been widely used in ocean observations, e.g. oil spill monitoring, ship detection, wind speed retrieval, etc. All these applications rely on the measured normalized radar cross section (NRCS) of sea surface. Therefore, it is necessary to develop accurate models to predict the radar cross sections of sea surface. In this paper, based on the six commonly used sea spectra models, i.e. Elfouhaily, Hwang, Romeiser, Apel, Fung and Pierson spectra, the normalized radar cross sections are calculated by Small Perturbation Method (SPM), Two Scale Model (TSM) and the first order Small Slope Approximation (SSA-1), respectively. Meanwhile, to better evaluate the accuracy of different sea spectra, comparisons between numerical calculations and the empirical CMOD5 model are made for various incident angles, wind speeds, and wind directions. The comparisons show that the normalized radar cross sections calculated based on Romeiser spectrum agree better with CMOD5 in some specific cases. The works presented in this paper will be helpful for applications of C band microwave radars in ocean observations.
Honglei Zheng; Ali Khenchaf; Helmi Ghanmi; Yunhua Wang; Chaofang Zhao. Normalized Radar Cross Sections of Sea Surface Estimated using Asymptotic and Semi-Empirical Methods Inc Band. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 53 -56.
AMA StyleHonglei Zheng, Ali Khenchaf, Helmi Ghanmi, Yunhua Wang, Chaofang Zhao. Normalized Radar Cross Sections of Sea Surface Estimated using Asymptotic and Semi-Empirical Methods Inc Band. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():53-56.
Chicago/Turabian StyleHonglei Zheng; Ali Khenchaf; Helmi Ghanmi; Yunhua Wang; Chaofang Zhao. 2018. "Normalized Radar Cross Sections of Sea Surface Estimated using Asymptotic and Semi-Empirical Methods Inc Band." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 53-56.
In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.
Ayoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation. Remote Sensing 2018, 10, 843 .
AMA StyleAyoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation. Remote Sensing. 2018; 10 (6):843.
Chicago/Turabian StyleAyoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. 2018. "Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation." Remote Sensing 10, no. 6: 843.
Surface wind speed estimation from synthetic aperture radar (SAR) data is principally based on empirical (EP) approaches, e.g., CMOD functions. However, it is necessary and significant to compare radar backscattering modeling based on EP and electromagnetic (EM) approaches for enhancing the understanding of the physical processes between radar signal and sea surface, which is important for the design of radar sensors (e.g., cyclone global navigation satellite system). Indeed, through comparisons, it is worth noticing that the scattering of wave breaking is not taken into account in the physical modeling of radar backscattering. Surface wind speed is selected here as a reference parameter for investigating the difference between EP and EM models, due to its important role in radar backscattering modeling. In addition, wind speed estimates can be easily compared to in situ measurements. For EP approach, CMOD5.N and Komarov's model are selected for wind speed estimation from Sentinel-1 images. The CMOD5.N can offer wind speed estimates up to 25-35 m/s, while wind speed estimation based on Komarov's model does not require wind direction input. For EM approach, the asymptotic models, i.e., composite two-scale model, small-slope approximation (SSA), and resonant curvature approximation (RCA), are investigated for wind speed retrieval. They are studied with two models of surface roughness spectrum: semi-EP spectrum and EP model. In general, normalized radar cross section (NRCS) calculated by CMOD5.N and SSA/RCA is quite similar for incidence angles below 40° in vertical polarized and below 30° in horizontal polarized. For larger ones, significant NRCS deviations between two approaches are demonstrated, due to the lack of wave breaking scattering in EM models. As a result, wind speed estimates by CMOD5.N and SSA/RCA are very close for low and moderate incidence angles, while SSA-/RCA-based wind speeds are overestimated for larger ones.
Tran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 4075 -4087.
AMA StyleTran Vu La, Ali Khenchaf, Fabrice Comblet, Carole Nahum. Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (7):4075-4087.
Chicago/Turabian StyleTran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. 2018. "Assessment of Wind Speed Estimation From C-Band Sentinel-1 Images Using Empirical and Electromagnetic Models." IEEE Transactions on Geoscience and Remote Sensing 56, no. 7: 4075-4087.
Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature. This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (Convolutional neural network). In the second time, we use the CAE (Convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.
Sarra Zaied; Abdelmalek Toumi; Ali Khenchaf. Target classification using convolutional deep learning and auto-encoder models. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2018, 1 -6.
AMA StyleSarra Zaied, Abdelmalek Toumi, Ali Khenchaf. Target classification using convolutional deep learning and auto-encoder models. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 2018; ():1-6.
Chicago/Turabian StyleSarra Zaied; Abdelmalek Toumi; Ali Khenchaf. 2018. "Target classification using convolutional deep learning and auto-encoder models." 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) , no. : 1-6.
The objective of this paper is to analyze the effect of the pollutant such as petrol-emulsion on the electromagnetic scattered filed by a maritime surface. Firstly, we investigate the pollutant influence on the maritime surface and physical properties of seawater. Then, we simulate the variation of electromagnetic scattering of the clean maritime surface and also the polluted surface. In this study, the clean and polluted seas are modeled using a semi-empirical spectrum. The simulations of scattering coefficients are realized by using the Forward-Backward Method (FBM), the Composite Two Scale-Model (CTSM) and the Small Slope Approximation (SSA) in monostatic and bistatic configurations. The effect of the pollutant on the electromagnetic scattering of a maritime surface has been studied as function of oil percentage, angles, frequency values and polarization state.
Helmi Ghanmi; Ali Khenchaf; Comblet Fabrice. Electromagnetic characterization of a polluted maritime surface. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2018, 1 -6.
AMA StyleHelmi Ghanmi, Ali Khenchaf, Comblet Fabrice. Electromagnetic characterization of a polluted maritime surface. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 2018; ():1-6.
Chicago/Turabian StyleHelmi Ghanmi; Ali Khenchaf; Comblet Fabrice. 2018. "Electromagnetic characterization of a polluted maritime surface." 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) , no. : 1-6.
In this paper, we present a novel approach for radar automatic target recognition on inverse synthetic aperture radar (ISAR). This approach is composed by two complementary steps: feature extraction and recognition. For the feature extraction step, we adopt a statistical modeling of the ISAR image in the complex wavelet domain. For doing so, we apply the dual-tree complex wavelet transform (DT-CWT) for each ISAR image in the database. After that, the relative phases of the resulting complex coefficients are computed. These relative phases are after statistically modeled using the Von Mises distribution. The estimated statistical parameters compose the feature vector of the ISAR images. Regarding to the recognition rate, the constructed feature vector is fed into the sparse representation based classification (SRC). More precisely, the training feature vectors are used as the atoms of a dictionary. The test feature vector is recognized according to its sparse linear combination with the dictionary. Extensive experiments and comparisons with other methods on ISAR images database demonstrate the effectiveness of the proposed approach.
Ayoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) 2018, 1 -5.
AMA StyleAyoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 2018; ():1-5.
Chicago/Turabian StyleAyoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. 2018. "Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification." 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) , no. : 1-5.
This paper deals with a radar track before detect application in a multi target setting. We propose to use the multiple-mode multiple targets track before detect (MM-MT-TBD) algorithm based on estimating the posterior probability density of the target states under different modes in order to consider all possible target existence combination at each time step. We implement the multi-mode multi-target track before detect (MM MT-PF-TBD) recursively using particle filtering to also incorporate nonlinear tracking models for moving targets. The performance of this algorithm is demonstrated using a simulated two moving targets according a coordinate turn model as an example for radar measurements.
Naima Amrouche; Ali Khenchaf; Daoud Berkani. Multiple Mode Multi-Target Tracking in High Noise Environment Using Radar Measurements. 2017 Sensor Signal Processing for Defence Conference (SSPD) 2017, 1 -5.
AMA StyleNaima Amrouche, Ali Khenchaf, Daoud Berkani. Multiple Mode Multi-Target Tracking in High Noise Environment Using Radar Measurements. 2017 Sensor Signal Processing for Defence Conference (SSPD). 2017; ():1-5.
Chicago/Turabian StyleNaima Amrouche; Ali Khenchaf; Daoud Berkani. 2017. "Multiple Mode Multi-Target Tracking in High Noise Environment Using Radar Measurements." 2017 Sensor Signal Processing for Defence Conference (SSPD) , no. : 1-5.
This paper presents a numerical and experimental study of RCS of canonical and complex targets using Gaussian Beam Summation (GBS) method. The purposed GBS method has several advantages over ray method, mainly on caustic problem. To test and evaluate the performance of the chosen method, we start the analysis of the RCS using GBS, the asymptotic models Physical Optic (PO), Geometrical Theory of Diffraction (GTD) and the rigorous Method of Moment (MoM). Then, we show the experimental validation of the numerical results using well calibrate measurements of radar targets. These experimental measurements have been carried out in our anechoic chamber (at ENSTA Bretagne). The numerical and experimental results of the RCS are studied and given as a function of various parameters: polarization type, target size, Gaussian beams number and Gaussian beams width.
H. Ghanmi; Ali Khenchaf; P. O. Leye; P. Pouliguen. Study of RCS of complex target: Experimental measurements and Gaussian beam summation method. 2017 IEEE Conference on Antenna Measurements & Applications (CAMA) 2017, 196 -199.
AMA StyleH. Ghanmi, Ali Khenchaf, P. O. Leye, P. Pouliguen. Study of RCS of complex target: Experimental measurements and Gaussian beam summation method. 2017 IEEE Conference on Antenna Measurements & Applications (CAMA). 2017; ():196-199.
Chicago/Turabian StyleH. Ghanmi; Ali Khenchaf; P. O. Leye; P. Pouliguen. 2017. "Study of RCS of complex target: Experimental measurements and Gaussian beam summation method." 2017 IEEE Conference on Antenna Measurements & Applications (CAMA) , no. : 196-199.
In this letter, we present a novel generic approach for radar automatic target recognition in either inverse synthetic aperture radar (ISAR) or synthetic aperture radar (SAR) images. For this purpose, the radar image is described by a statistical modeling in the complex wavelet domain. Thus, the radar image is transformed into a complex wavelet domain using the dual-tree complex wavelet transform. Afterward, the magnitudes of the complex sub-bands are modeled by Weibull or Gamma distributions. The estimated parameters of these models are stacked together to create a statistical dictionary in training step. For the recognition task, we use the weighted sparse representation-based classification method that captures the linearity and locality information of image features. In this context, we propose to use the Kullback–Leibler divergence between the parametric statistical models of training and test sets in order to assign a weight for each training sample. Experiments conducted on both ISAR and SAR images’ databases demonstrate that the proposed approach leads to an improvement in the recognition rate.
Ayoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation. IEEE Geoscience and Remote Sensing Letters 2017, 14, 2403 -2407.
AMA StyleAyoub Karine, Abdelmalek Toumi, Ali Khenchaf, Mohammed El Hassouni. Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation. IEEE Geoscience and Remote Sensing Letters. 2017; 14 (12):2403-2407.
Chicago/Turabian StyleAyoub Karine; Abdelmalek Toumi; Ali Khenchaf; Mohammed El Hassouni. 2017. "Target Recognition in Radar Images Using Weighted Statistical Dictionary-Based Sparse Representation." IEEE Geoscience and Remote Sensing Letters 14, no. 12: 2403-2407.
Synthetic aperture radar (SAR) is one of the favorite tools for earth observation applications, i.e., oceanography, land use mapping, climate change since this device can offer the data at a high spatial resolution and in most meteorological conditions. This is more significant when the data acquired by the Sentinel-1, a new C-band satellite, are exploited. For high-resolution wind field extraction, two different approaches are proposed. In the scatterometry-based approach, wind direction is first extracted by the local gradient method at different scales, i.e., 1–5-km wind resolutions. It is then applied to the empirical geophysical model functions, i.e., CMOD (C-band), for surface wind speed estimation. The advantage of this approach is to deliver accurate wind speed estimates in the range of 2–25 m/s from different SAR data. However, it requires wind direction as an input parameter. This can lead to errors in wind speed estimation due to uncertain wind directions. Therefore, for comparison, in the second approach, we propose the use of the model without wind direction input proposed by Komarov et al. In general, the obtained wind fields based on two proposed approaches are quite similar, and they have good agreement with in situ measurements from the meteorological stations along the Iroise coast.
Tran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 10, 5458 -5471.
AMA StyleTran Vu La, Ali Khenchaf, Fabrice Comblet, Carole Nahum. Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 10 (12):5458-5471.
Chicago/Turabian StyleTran Vu La; Ali Khenchaf; Fabrice Comblet; Carole Nahum. 2017. "Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France)." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 12: 5458-5471.
In this paper, we adapted the recursive TBD algorithm to track multiple targets in low SNR. This algorithm is important, for example, in closely spaced targets moving along the same direction. The different modes in the multiple target case correspond to the different number of target combination that may be present in the scene at each time. Thus, the multiple-mode multiple targets track before detect (MM-MT-TBD) algorithm is based on estimating the posterior probability density of the target states under different modes in order to consider all possible target existence combination at each time step. We implement the multi-mode multi-target track before detect (MM MT-PF-TBD) recursively using particle filtering to also incorporate nonlinear tracking models for moving targets. The performance of this algorithm is demonstrated using a simulated two targets as an example for image measurements.
Naima Amrouche; Ali Khenchaf; Daoud Berkani. Multiple target tracking using track before detect algorithm. 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA) 2017, 692 -695.
AMA StyleNaima Amrouche, Ali Khenchaf, Daoud Berkani. Multiple target tracking using track before detect algorithm. 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA). 2017; ():692-695.
Chicago/Turabian StyleNaima Amrouche; Ali Khenchaf; Daoud Berkani. 2017. "Multiple target tracking using track before detect algorithm." 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA) , no. : 692-695.