The Experts below are selected from a list of 120372 Experts worldwide ranked by ideXlab platform
Hongwei Liu - One of the best experts on this subject based on the ideXlab platform.
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Target Recognition in sar image via sparse representation in transformed domain
International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Ganggang Dong, Hongwei Liu, Bo Jiu, Jibin Zheng, Junkun YanAbstract:To solve Target Recognition under extended environments, this paper proposes sparse representation in the transformed domain. Since the signal energy in the frequency domain is mainly concentrated on a small portion of low frequencies, this part of spectrum therefore carry the vital information that distinguishes a class of Target from the other. We intend to define a frequency descriptor by the bag of low frequencies. The defined descriptor is used to build sparse signal modeling. The frequency descriptors of the training are concatenated to form an over-complete dictionary. It is used to encode the counterpart of query as a linear combination of themselves. Sparsity has been harnessed to generate the optimal representation, from which the inference can be reached.
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Target aware recurrent attentional network for radar hrrp Target Recognition
Signal Processing, 2019Co-Authors: Bo Chen, Jinwei Wan, Hongwei Liu, Lin JinAbstract:Abstract In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in Target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the Target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive Recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.
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Convolutional neural networks for radar HRRP Target Recognition and rejection
EURASIP Journal on Advances in Signal Processing, 2019Co-Authors: Jinwei Wan, Bo Chen, Bin Xu, Hongwei Liu, Lin JinAbstract:Robust and efficient feature extraction is critical for high-resolution range profile (HRRP)-based radar automatic Target Recognition (RATR). In order to explore the correlation between range cells and extract the structured discriminative features in HRRP, in this paper, we take advantage of the attractive properties of convolutional neural networks (CNNs) to address HRRP RATR and rejection problem. Compared with the time domain representations, the spectrogram of HRRP records the amplitude feature and characterizes the phase information among the range cells. Thus, besides using one-dimensional CNN to handle HRRP in time domain, we also devise a two-dimensional CNN model for the spectrogram feature. Furthermore, by adding a deconvolutional decoder, we integrate the Target Recognition with outlier rejection task together. Experimental results on measured HRRP data show that our CNN model outperforms many state-of-the-art methods for both Recognition and rejection tasks.
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sar automatic Target Recognition based on euclidean distance restricted autoencoder
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Sheng Deng, Jun Ding, Hongwei LiuAbstract:Deep learning algorithms have been introduced into Target Recognition of synthetic aperture radar (SAR) images for extracting deep features because of its accuracy on various Recognition problems with sufficient training samples. However, applying deep structures in recognizing SAR images may suffer lack of training samples. Therefore, a deep learning method is proposed in this study based on a multilayer autoencoder (AE) combined with a supervised constraint. We bind the original AE algorithm with a restriction based on Euclidean distance to use the limited training images well. Moreover, a dropout step is added to our algorithm, which is designed to prevent overfitting caused by supervised learning. Experimental results on the MSTAR dataset demonstrate the effectiveness of the proposed method on real SAR images.
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radar hrrp Target Recognition with deep networks
Pattern Recognition, 2017Co-Authors: Bo Feng, Bo Chen, Hongwei LiuAbstract:Abstract Feature extraction is the key technique for radar automatic Target Recognition (RATR) based on high-resolution range profile (HRRP). Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR. Aiming at those issues, in this paper deep networks are built up for HRRP Target Recognition by adopting multi-layered nonlinear networks for feature learning. To learn the stable structure and correlation of Targets from unlabeled data, a deep network called Stacked Corrective Autoencoders (SCAE) is further proposed via taking the advantage of the HRRP's properties. As an extension of deep autoencoders, SCAE is stacked by a series of Corrective Autoencoders (CAE) and employs the average profile of each HRRP frame as the correction term. The covariance matrix of each HRRP frame is considered for establishing an effective loss function under the Mahalanobis distance criterion. We use the measured HRRP data to show the effectiveness of our methods. Furthermore, we demonstrate that with the proper optimization procedure, our model is also effective even with a moderately incomplete training set.
Engin Avci - One of the best experts on this subject based on the ideXlab platform.
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a new method for expert Target Recognition system genetic wavelet extreme learning machine gawelm
Expert Systems With Applications, 2013Co-Authors: Engin AvciAbstract:In last year's, the expert Target Recognition has been become very important topic in radar literature. In this study, a Target Recognition system is introduced for expert Target Recognition (ATR) using radar Target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar Target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert Target Recognition. The GAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar Target Recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahern, Delisle, et al., 1989; Al-Otum & Al-Sowayan, 2011; Avci, Turkoglu, & Poyraz, 2005a, 2005b; Biswal, Dash, & Panigrahi, 2009; Frigui et al., in press; Cao, Lin, & Huang, 2010; Guo, Rivero, Dorado, Munteanu, & Pazos, 2011; Famili, Shen, Weber, & Simoudis, 1997; Han & Huang, 2006; Huang, Cai, Chen, & Liu, 2011; Huang, Chen, & Siew, 2006; Huang & Siew, 2005; Huang, Liu, Gao, & Guo, 2009; Jiang, Liu, Li, & Tang, 2011; Kubrusly & Levan, 2009; Le, Tamura, & Matsumoto, 2011; Lhermitte et al., 2011; Martinez-Martinez et al., 2011; Matlab, 2011; Nelson, Starzyk, & Ensley, 2002; Nejad & Zakeri, 2011; Tabib, Sathe, Deshpande, & Joshi, 2009; Tang, Sun, Tang, Zhou, & Wei, 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert Target Recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform-short-time Fourier transform (DWT-STFT), discrete wavelet transform-Born-Jordan time-frequency transform (DWT-BJTFT), and discrete wavelet transform-Choi-Williams time-frequency transform (DWT-CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time-frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar Targets. The performance of the developed GAWELM expert radar Target Recognition system is examined by using noisy real radar Target echo signals. The applications results of the developed GAWELM expert radar Target Recognition system show that this GAWELM system is effective in rating real radar Target echo signals. The correct classification rate of this GAWELM system is about 90% for radar Target types used in this study.
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a new automatic Target Recognition system based on wavelet extreme learning machine
Expert Systems With Applications, 2012Co-Authors: Engin Avci, Resul CoteliAbstract:In this paper, an automatic system is presented for Target Recognition using Target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real Target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar Target Recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005). To resolve these disadvantages of feedforward neural networks for automatic Target Recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct Recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.
Bo Chen - One of the best experts on this subject based on the ideXlab platform.
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Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Guo Dandan, Bo Chen, Chen Wenchao, Wang Chaojie, Liu Hongwei, Zhou MingyuanAbstract:We develop a recurrent gamma belief network (rGBN) for radar automatic Target Recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP. The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. For scalable training and fast out-of-sample prediction, we propose the hybrid of a stochastic-gradient Markov chain Monte Carlo (MCMC) and a recurrent variational inference model to perform posterior inference. To utilize the label information to extract more discriminative latent representations, we further propose supervised rGBN to jointly model the HRRP samples and their corresponding labels. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and generalization ability, and provide highly interpretable multi-stochastic-layer latent structure
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Target aware recurrent attentional network for radar hrrp Target Recognition
Signal Processing, 2019Co-Authors: Bo Chen, Jinwei Wan, Hongwei Liu, Lin JinAbstract:Abstract In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in Target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the Target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive Recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.
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Convolutional neural networks for radar HRRP Target Recognition and rejection
EURASIP Journal on Advances in Signal Processing, 2019Co-Authors: Jinwei Wan, Bo Chen, Bin Xu, Hongwei Liu, Lin JinAbstract:Robust and efficient feature extraction is critical for high-resolution range profile (HRRP)-based radar automatic Target Recognition (RATR). In order to explore the correlation between range cells and extract the structured discriminative features in HRRP, in this paper, we take advantage of the attractive properties of convolutional neural networks (CNNs) to address HRRP RATR and rejection problem. Compared with the time domain representations, the spectrogram of HRRP records the amplitude feature and characterizes the phase information among the range cells. Thus, besides using one-dimensional CNN to handle HRRP in time domain, we also devise a two-dimensional CNN model for the spectrogram feature. Furthermore, by adding a deconvolutional decoder, we integrate the Target Recognition with outlier rejection task together. Experimental results on measured HRRP data show that our CNN model outperforms many state-of-the-art methods for both Recognition and rejection tasks.
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radar hrrp Target Recognition with deep networks
Pattern Recognition, 2017Co-Authors: Bo Feng, Bo Chen, Hongwei LiuAbstract:Abstract Feature extraction is the key technique for radar automatic Target Recognition (RATR) based on high-resolution range profile (HRRP). Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR. Aiming at those issues, in this paper deep networks are built up for HRRP Target Recognition by adopting multi-layered nonlinear networks for feature learning. To learn the stable structure and correlation of Targets from unlabeled data, a deep network called Stacked Corrective Autoencoders (SCAE) is further proposed via taking the advantage of the HRRP's properties. As an extension of deep autoencoders, SCAE is stacked by a series of Corrective Autoencoders (CAE) and employs the average profile of each HRRP frame as the correction term. The covariance matrix of each HRRP frame is considered for establishing an effective loss function under the Mahalanobis distance criterion. We use the measured HRRP data to show the effectiveness of our methods. Furthermore, we demonstrate that with the proper optimization procedure, our model is also effective even with a moderately incomplete training set.
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radar high resolution range profiles Target Recognition based on stable dictionary learning
Iet Radar Sonar and Navigation, 2016Co-Authors: Bo Feng, Bo Chen, Lan DuAbstract:Sparse representation models based on dictionary learning have led to interesting results in signal restoration and Target Recognition. However, due to the redundancy defined by overcomplete dictionary atoms in new space, finding sparse representations from inaccurate measurements may cause uncertainty and ambiguity. Especially for radar automatic Target Recognition using high-resolution range profiles (HRRP), the Target-aspect sensitivity, amplitude fluctuation and outliers in HRRPs could result in mismatch among the sparse representations of the same class and thus deteriorate the Recognition performance. This article proposes a novel stable dictionary learning method to deal with this problem and improve the pattern Recognition performance. The proposed method relies on the constraints that the sparse representations of adjacent HRRPs without scatterers' motion through range cells should have the same support and lower variance. The structured sparse regularisation is then used to automatically select the optimal dictionary basis vectors for stable sparse coding. Experiments based on the measured HRRP dataset validate the performance of the proposed method. Moreover, encouraging results are reported with small training data size and under different signal-to-noise ratio conditions.
Fangqi Zhu - One of the best experts on this subject based on the ideXlab platform.
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radar hrrp Target Recognition based on concatenated deep neural networks
IEEE Access, 2018Co-Authors: Kuo Liao, Fangqi ZhuAbstract:In this paper, a deep neural network with concatenated structure is created for the Recognition of flight Targets. Compared with the traditional Recognition method, the deep network model automatically gets deeper structure information that is more useful for the classification, and the better performance of Target Recognition is also obtained when using high-resolution range profile for radar automatic Target Recognition. First, the framework is expanded and cascaded by multiple shallow neural sub-networks. Then, a secondary-label coding method is proposed to solve the Target-aspect angle sensitivity problem. The samples are divided into sub-classes based on aspect angle, each of which is assigned a separate encoding bit in category label. Finally, the Recognition results of multiple samples are fused by a multi-evidence fusion strategy for the improvement of Recognition rate. Furthermore, the effectiveness of the proposed algorithm is demonstrated on the measured and simulated data.
Resul Coteli - One of the best experts on this subject based on the ideXlab platform.
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a new automatic Target Recognition system based on wavelet extreme learning machine
Expert Systems With Applications, 2012Co-Authors: Engin Avci, Resul CoteliAbstract:In this paper, an automatic system is presented for Target Recognition using Target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real Target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar Target Recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005). To resolve these disadvantages of feedforward neural networks for automatic Target Recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) (Feng, Huang, Lin, & Gay, 2009; Huang & Siew, 2004, 2005; Huang & Chen, 2007, 2008; Huang, Chen, & Siew, 2006; Huang, Ding, & Zhou, 2010; Huang, Zhu, & Siew, 2004; Huang, Liang, Rong, Saratchandran, & Sundararajan, 2005; Huang, Zhou, Ding, & Zhang, 2012; Huang, Li, Chen, & Siew, 2008; Huang, Wang, & Lan, 2011; Huang et al., 2006; Huang, Zhu, & Siew, 2006a, 2006b; Lan, Soh, & Huang, 2009; Li, Huang, Saratchandran, & Sundararajan, 2005; Liang, Huang, Saratchandran, & Sundararajan, 2006; Liang, Saratchandran, Huang, & Sundararajan, 2006; Rong, Huang, Saratchandran, & Sundararajan, 2009; Wang & Huang, 2005; Wang, Cao, & Yuan, 2011; Yeu, Lim, Huang, Agarwal, & Ong, 2006; Zhang, Huang, Sundararajan, & Saratchandran, 2007; Zhu, Qin, Suganthan, & Huang, 2005) which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct Recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.
