Altitude measurement based on characteristics reversal by deep neural network for VHF radar

Xiang, Houhong; Chen, Baixiao; Yang, Minglei; Li, Cunxu

doi:10.1049/iet-rsn.2018.5121

Cited by 28 publications

(14 citation statements)

References 23 publications

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“…Assuming that the antenna height difference is much smaller than the radar range resolution. In the far-field condition, the signal emitted by the k-th transmitting antenna is received by the k-th receiving antenna after reflection, and the signal model is formula (8).…”

Section: Figure 2 Beam Splitting Diagram Of Main Elementmentioning

confidence: 99%

“…This algorithm takes into account the influence of complex terrain and reduces the complexity of the algorithm. Paper [8] applies neural network technology in the field of conventional phased array meter wave radar, and obtained good angle measurement accuracy. Paper [9] proposes a low-elevation estimation method for wideband radar based on super-resolution algorithm, which is more suitable for complex terrain than the low-elevation estimation method of narrowband radar.…”

Section: Introductionmentioning

confidence: 99%

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Beam Split Algorithm for Height Measurement With Meter-Wave MIMO Radar

2021

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Section: Figure 2 Beam Splitting Diagram Of Main Elementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beam Split Algorithm for Height Measurement With Meter-Wave MIMO Radar

2021

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“…For example, Patel et al [17] investigated the effects of jamming on the micro-Doppler classification performance and explored a potential deep topology based on DNNs enabling low-bandwidth data fusion between nodes in a multistatic radar network. Xiang et al [18] designed a novel direction of arrival estimation method based on DNN for very high-frequency radar under strong multipath effect and complex terrain environment, which can learn the received data's characteristics from a different elevation.…”

Section: Introductionmentioning

confidence: 99%

Deep Fusion for Radar Jamming Signal Classification Based on CNN

2020

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The accurate classification of radar jamming signal is a core step of anti-jamming. Recently, convolutional neural network (CNN) based methods have shown their powerfulness in signal processing. In this paper, a deep fusion method based on CNN is proposed to classify jamming signal acting on pulse compression radar. The proposed method consists of three subnetworks (i.e., 1D-CNN, 2D-CNN, and fusion network). 1D-CNN is used to extract deep features of original radar jamming signal. Meanwhile, in order to extract the time-frequency features, short time Fourier transform (STFT) is applied to jamming signal to obtain time-frequency spectrograms. Then, 2D-CNN is used to extract deep time-frequency features, which are useful for further features fusion processing. Fusion network is used to deeply fuse the extracted features of the aforementioned CNNs and softmax is used to finish the task of radar jamming signal classification. In addition, in order to alleviate the problem of overfitting and improve the generalization ability of proposed model, soft label smoothing is proposed. The experimental results reveal that the proposed method provides competitive results in term of classification accuracy. INDEX TERMS Radar jamming signal, classification, convolutional neural network (CNN), deep fusion.

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“…Obviously, on the one hand, the size of bin determines the resolution; on the other hand, the problem of model mismatch exists. Recently, two new neural networks [6]- [8] are used for solving DOA estimation. In [6], a new method called feature reversal is introduced.…”

Section: Introductionmentioning

confidence: 99%

A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning

et al. 2019

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In low-altitude target situation, the multi-path signals cause the amplitude-phase distortion of direct signal from targets and degrade the performance of existing methods. Hence, in this paper, we propose a phase enhancement method for low-angle estimation using supervised deep neural network (DNN) to mitigate the phase distortion, thus to improve direction of arrival (DOA) estimation accuracy. The mapping relationship between the original phase difference distribution of the received signal and desired phase difference distribution is learned by DNN during training. The phase of test data is enhanced by trained DNN, and the enhanced phase is used for DOA estimation. We explain the significance of enhancing phase instead of amplitude by discussing the sensitivity of amplitude and phase on DOA estimation. Moreover, we prove the effectiveness and superiority of the proposed method by simulation experiments. The results demonstrate that the proposed technique has a better performance in terms of estimation error and goodness of fit (GoF) than the physics-driven DOA estimation methods and state-of-the-art methods including feature reversal and the support vector regression (SVR). INDEX TERMS Phase enhancement, supervised deep neural network, DOA estimation.

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Altitude measurement based on characteristics reversal by deep neural network for VHF radar

Cited by 28 publications

References 23 publications

Beam Split Algorithm for Height Measurement With Meter-Wave MIMO Radar

Beam Split Algorithm for Height Measurement With Meter-Wave MIMO Radar

Deep Fusion for Radar Jamming Signal Classification Based on CNN

A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning

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