Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Zhu, Mingzhe; Feng, Zhenpeng; Zhou, Xianda; Xiao, R.; Qi, Yue; Zhang, Chi

doi:10.3390/electronics9040658

Cited by 11 publications

(9 citation statements)

References 19 publications

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“…AF is the inverse Fourier transform of the instantaneous autocorrelation function of a signal over the time variable. Different from STFT that transforms signals in joint time-frequency domain (TFD), AF transforms signals to time lag-Doppler lag domain [7]. The AF of signal x(t) is defined by:…”

Section: Ambiguity Functionmentioning

confidence: 99%

“…It is necessary to note that there are still some small flaws that can be improved on AF-RS [7], mainly in: (1) a sole slice at a fixed frequency may neglect other slices which contribute to identification in other Doppler-lag. (2) The selection of representative slice must by implemented by complex feature fusion optimization, which probably makes it unfeasible to achieve real-time optimization.…”

Section: Representative Slice Of Ambiguity Functionmentioning

confidence: 99%

“…Zhu, M. and Zhang, X. propose compressive sensing mask (CS-MASK) in [6] which is a low-dimensional feature with good representation by constraining the reconstruction error between inverse transformation of the selected ambiguity function and the WVD of original signal. Reference [7] performs synchrosqueezing transform on STFT, obtaining a more concentrated energy band and more accurate instantaneous frequency trajectory which are merged into a new feature matrix. These are all excellent handcrafted features that can achieve satisfying recognition rate in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Few references analyze these questions from the perspective of machine cognition. Linear relevance propagation (LRP) is used in [7] to create a heat map, endeavoring to demonstrate the validation of SST feature, which is a heuristic spirit, even though the interpretation in [7] is quite simple and conjectural.…”

Section: Introductionmentioning

confidence: 99%

“…Simonyan, K. and Vedaldi, A proposed an image specific class saliency visualization in [12] where a saliency map is created to reflect how different pixels in each grid impacts the final classification result. Inspired by [7,12], a novel data-driven feature that is based on machine cognition (MC-feature) is proposed in this paper combined with specific class saliency visualisation to "tell" us where the network "looks" at and where the network "disinterest", and then manipulate this information into a feature. MC-feature has extraordinary representative capability of intra-class fingerprint information of signals in SEI.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

2020

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Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD), and etc., usually show limited recognition effects when only a slight difference exists in radar signals. Numerous two-dimensional features on transform domain, like various time-frequency representation and ambiguity function are used to augment information abundance, whereas the unacceptable computational burden usually emerges. To solve this problem, some artfully handcrafted features in transformed domain are proposed, like representative slice of ambiguity function (AF-RS) and compressed sensing mask (CS-MASK), to extract representative information that contributes to machine recognition task. However, most handcrafted features only utilizing neural network as a classifier, few of them focus on mining deep informative features from the perspective of machine cognition. Such feature extraction that is based on human cognition instead of machine cognition may probably miss some seemingly nominal texture information which actually contributes greatly to recognition, or collect too much redundant information. In this paper, a novel data-driven feature extraction is proposed based on machine cognition (MC-Feature) resort to saliency detection. Saliency detection exhibits positive contributions and suppresses irrelevant contributions in a transform domain with the help of a saliency map calculated from the accumulated gradients of each neuron to input data. Finally, positive and irrelevant contributions in the saliency map are merged into a new feature. Numerous experimental results demonstrate that the MC-feature can greatly strengthen the slight intra-class difference in SEI and provides a possibility of interpretation of CNN.

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Section: Ambiguity Functionmentioning

confidence: 99%

Section: Representative Slice Of Ambiguity Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

2020

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A probe-feature for specific emitter identification using axiom-based grad-CAM

et al. 2022

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Joint Disparity Estimation and Pseudo NIR Generation From Cross Spectral Image Pairs

Duan

Jung

2022

IEEE Access

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Cross spectral stereo matching is a challenging task due to different spectral properties causing unreliable results in correspondence estimation. In this paper, we propose joint disparity estimation and pseudo near infrared (NIR) generation from cross spectral image pairs. To bridge the spectral gap between paired images, we adopt differential map operations and non-local blocks to improve the local attention and global attention of the network. The proposed network is based on unsupervised learning that consists of one encoder and two decoders, which performs both spectral translation and disparity estimation. For cooperative learning, we use difference map operation to connect two decoders, thus improving the inference ability of the decoder in regions even with large spectral differences. Experimental results show that the proposed network achieves good performance in cross spectral stereo matching for unreliable regions such as shadows and glasses. Moreover, the proposed network generates pseudo NIR images nearly the same as the ground truth even in the regions with large spectral difference. Besides, we achieve real-time speed of 27 FPS for 582×429 image pairs on RTX 2060 6G GPU due to the low computational complexity.

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Specific Emitter Identification Based on Synchrosqueezing Transform for Civil Radar

Cited by 11 publications

References 19 publications

A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

A probe-feature for specific emitter identification using axiom-based grad-CAM

Joint Disparity Estimation and Pseudo NIR Generation From Cross Spectral Image Pairs

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