Radar HRRP Target Recognition Based on t-SNE Segmentation and Discriminant Deep Belief Network

Pan, Mian; Jiang, Jie; Kong, Qingpeng; Shi, Jianguang; Sheng, Qinghua; Zhou, Tao

doi:10.1109/lgrs.2017.2726098

Cited by 81 publications

(38 citation statements)

References 16 publications

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“…The HRRP of the target is the projection of the target scattering center in the direction of sight. HRRP, which reflects the geometric and structural features of the target, is among the main methods of wideband radar target recognition [117,[123][124][125][126]. The echo of each resolution cell of the target's HRRP is formed by superposing the echoes of multiple scattering centers in the resolution cell.…”

Section: Hrrp Attitude Sensitivity Problemmentioning

confidence: 99%

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Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Liang

Liu

2019

Sci. China Inf. Sci.

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In recent years, the performances of radar resolution, coverage, and detection accuracy have been significantly improved through the use of ultra-wideband, synthetic aperture and digital signal processing technologies. High-resolution radars (HRRs) utilize wideband signals and synthetic apertures to enhance the range and angular resolutions of tracking, respectively. They also generate one-, two-, and even threedimensional high-resolution images containing the feature information of targets, from which the targets can be precisely classified and identified. Advanced signal processing algorithms in HRRs obtain important information such as range-Doppler imaging, phase-derived ranging, and micro-motion features. However, the advantages and applications of HRRs are restricted by factors such as the reduced signal-to-noise ratio (SNR) of multi-scatter point targets, decreased tracking accuracy of multi-scatter point targets, high demands of motion compensation, and low sensitivity of the target attitude. Focusing on these problems, this paper systematically introduces the novel technologies of HRRs and discusses the issues and solutions relevant to detection, tracking, imaging, and recognition. Finally, it reviews the latest progress and representative results of HRR-based research, and suggests the future development of HRRs.

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Section: Hrrp Attitude Sensitivity Problemmentioning

confidence: 99%

“…Feature extraction isolates a set of features representing the essential attributes of the target from HRRP. Feature classification maps the feature set to the corresponding class of the target by machine learning [117][118][119][120][121][122][123][124][125][126][127][128][129][130].…”

Section: Hierarchical Target Recognition Algorithmmentioning

confidence: 99%

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Liang

Liu

2019

Sci. China Inf. Sci.

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“…By using t-SNE, the pairwise distance is transformed into the probabilities to measure the similarities between data [37,38]. In the raw space, the pairwise similarities are described as…”

Section: Parametric T-stochastic Neighbor Embedding (Parametric T-sne)mentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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Abstract:In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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“…The essence of deep learning is to construct a neural network containing multiple hidden layers to map the data in order to obtain the deep essential characteristics [ 17 ]. A deep belief network is used in [ 18 ] to solve the non-cooperative target recognition with an imbalanced training dataset. As an important component of the deep learning structure, the stacked autoencoder (SAE) plays an important role in unsupervised learning and nonlinear feature extraction and it has also been applied in many fields [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the shallow learning algorithms such as PCA [ 3 ], MTL TSB-HMMs [ 6 ], ELM [ 26 ], and so on, the proposed algorithm can extract the inherent characteristics of the target. Since the network is not required to be fine-tuned, the proposed algorithm is faster than the other deep learning models [ 18 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

Zhao

Liu

Huang

et al. 2018

Sensors

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A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

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Radar HRRP Target Recognition Based on t-SNE Segmentation and Discriminant Deep Belief Network

Cited by 81 publications

References 16 publications

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

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