Deep learning for HRRP-based target recognition in multistatic radar systems

Lundén, Jarmo; Koivunen, Visa

doi:10.1109/radar.2016.7485271

Cited by 65 publications

(29 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the deep methods, the concept of deep learning is adopted to automatically extract features and construct classifier using deep neural network (DNN). Various DNN architectures have been applied to HRRP recognition [44][45][46][47], including autoencoder (AE) [45], CNN and recurrent neural network (RNN) [44] models. An AE extracts latent features by minimizing the recovery loss, then AE-extracted features are used to discriminate target class with the help of the previously mentioned classifiers.…”

Section: A Radar Target Hrrp Recognitionmentioning

confidence: 99%

“…A CNN is a multiple-layer classifier that introduces the convolutional operator, and it can capture detailed features from the initial convolution layers and global features from the final layer [46]. RNN models have received attention due to their advantages for extracting features from sequential data [44], and HRRP can be regarded as a time series that can be input into the network.…”

Section: A Radar Target Hrrp Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

Song

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In radar high-resolution range profile (HRRP) recognition, the recognition accuracy will decline when the training samples in some classes (majority classes) greatly outnumbers other classes (minority classes). To alleviate the above imbalanced problem, an HRRP data augmentation framework is proposed. A onedimensional (1-D) deep convolutional generative adversarial network (DCGAN) is developed to generate artificial HRRPs. The fidelity of the generated HRRPs is evaluated subjectively in the raw data domain and quantitatively by the similarity in the feature domain. The experimental results show that the generated data are similar to the true HRRPs and demonstrate that the proposed framework outperforms the state-of-the-art oversampling methods when handling the imbalanced problem. INDEX TERMS High resolution range profile (HRRP), imbalanced problem, data augmentation, 1-D deep convolutional generative adversarial network (DCGAN)

show abstract

Section: A Radar Target Hrrp Recognitionmentioning

confidence: 99%

Section: A Radar Target Hrrp Recognitionmentioning

confidence: 99%

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

Song

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…[18] applied the CNN to HRRP from multiple monostatic and bistatic radar systems and significantly improved the reliability of the classification.…”

Section: Cnn For Feature Fusionmentioning

confidence: 99%

A Time-Frequency Feature Fusion Algorithm Based on Neural Network for HRRP

Yuan¹

2017

PIER M

View full text Add to dashboard Cite

Abstract-In this paper, a feature fusion algorithm is proposed for automatic target recognition based on High Resolution Range Profiles (HRRP). The proposed algorithm employs Convolution Neural Network (CNN) to extract fused feature from the time-frequency features of HRRP automatically. The time-frequency features used include linear transform and bilinear transform. The coding of the CNN's largest output node is the target category, and the output is compared with a threshold to decide whether the target is classified to a pre-known class or an unknown class. Simulations by four different aircraft models show that the proposed feature fusion algorithm has higher target recognition performance than single features.

show abstract

“…Compared with the traditional pattern-recognition method, the DL method has the advantages of the automatic extraction of deep features and high recognition accuracy, and it has good universality. [ 17 , 18 ]. In recent years, the deep-learning (DL) technique has become a research hotspot in various fields, such as object classification and segmentation [ 19 , 20 ], super-resolution [ 21 , 22 ], image denoising [ 23 , 24 ], medical image reconstruction [ 25 , 26 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Jiang

Zhang

Yang

et al. 2020

Sensors

View full text Add to dashboard Cite

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

show abstract

Deep learning for HRRP-based target recognition in multistatic radar systems

Cited by 65 publications

References 11 publications

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

A Time-Frequency Feature Fusion Algorithm Based on Neural Network for HRRP

Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Contact Info

Product

Resources

About