Modulation Recognition of Overlapping Radar Signals Under Low SNR Based on Se-Incepatnet

Wang, Hao; Huo, Weibo; Lu, Yuchun; Pei, Jifang; Huang, Yulin

doi:10.1109/igarss46834.2022.9883083

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…To solve the abovementioned global segmentation problem, modulation type filtering is designed. Modulation type filtering uses the output of modulation recognition module [35], i.e. the recognition results, as prior information to help increase the segmentation accuracy.…”

Section: ) Modulation Type Filteringmentioning

confidence: 99%

“…The structure of the modulation recognition module is shown in Fig. 11 [35]. The recognition result is output by the modulation recognition module.…”

Section: E Modulation Recognition Modulementioning

confidence: 99%

“…In the case of parameter extraction of overlapping radar signals, it is necessary to first segment the overlapping radar signals before extracting parameters. Wang et al [35] propose a squeeze-and-excitation Inception net with adaptive thresholds to segment overlapping radar signals, which has a high recognition rate and improved performance compared to traditional methods. Chen et al [36] develop a joint semantic learning CNN to do semantic segmentation on low probability of intercept (LPI) radar signals, which has higher separation and recognition accuracy than traditional methods.…”

mentioning

confidence: 99%

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Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Huo,

Luo,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Parameter extraction of radar signals is an important but challenging task in electronic warfare. In the modern electromagnetic environment, the radiation sources greatly increase, causing different radar signals overlap, making the parameter extraction of radar signals difficult. Meanwhile, using radar signal parameter extraction methods that are not suitable for dealing with overlapping signals can lead to serious errors in this case. To address this, we propose a parameter extraction network for overlapping radar signals using modulation recognition-guided semantic segmentation. Specifically, we first design an encoder-decoder to segment overlapping radar signals, which uses channel rearrangement and modulation type filtering to increase the accuracy of segmentation. In this encoder-decoder, channel rearrangement is an optimization of convolution operation, aiming to increase perceptual field while reducing feature information loss. And modulation type filtering can convert the results of semantic segmentation into masks corresponding to each radar signal, increasing the accuracy of segmentation. After the encoder-decoder, signal segmentation masks are obtained. Then, we compress these segmentation masks in the time and frequency domains, and extract the span of them to achieve accurate extraction of the pulse-width and bandwidth of each radar signal. The experiments validate the feasibility of the proposed method.

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Section: ) Modulation Type Filteringmentioning

confidence: 99%

“…The structure of the modulation recognition module is shown in Fig. 11 [35]. The recognition result is output by the modulation recognition module.…”

Section: E Modulation Recognition Modulementioning

confidence: 99%

mentioning

confidence: 99%

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Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Huo,

Luo,

Wang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…SE attention blocks in the image field are often applied in structures such as Inception [26], prompting models to learn and model effective content. It is expected to energize the ability of models to extract features.…”

Section: Se Attention Blockmentioning

confidence: 99%

Environmental Sound Classification Framework Based on L-mHP Features and SE-ResNet50 Network Model

et al. 2023

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Environmental sound classification (ESC) tasks are attracting more and more attention. Due to the complexity of the scene and personnel mobility, there are some difficulties in understanding and generating environmental sound models for ESC tasks. To address these key issues, this paper proposes an audio classification framework based on L-mHP features and the SE-ResNet50 model and improves a dual-channel data enhancement scheme based on a symmetric structure for model training. Firstly, this paper proposes the L-mHP feature to characterize environmental sound. The L-mHP feature is a three-channel feature consisting of a Log-Mel spectrogram, a harmonic spectrogram, and a percussive spectrogram. The harmonic spectrogram and percussive spectrogram can be obtained by harmonic percussive source separation (HPSS) of a Log-Mel spectrogram. Then, an improved audio classification model SE-ResNet50 is proposed based on the ResNet-50 model. In this paper, a dual-channel data enhancement scheme based on a symmetric structure is promoted, which not only makes the audio variants more diversified, but also makes the model focus on learning the time–frequency mode in the acoustic features during the training process, so as to improve the generalization performance of the model. Finally, the audio classification experiment of the framework is carried out on public datasets. An experimental accuracy of 94.92%, 99.67%, and 90.75% was obtained on ESC-50, ESC-10 and UrbanSound8K datasest, respectively. In order to simulate the classification performance of the framework in the actual environment, the framework was also evaluated on a self-made sound dataset with different signal-to-noise ratios. The experimental results show that the proposed audio classification framework has good robustness and feasibility.

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Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR

Zhang,

Gao

2024

2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

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Modulation Recognition of Overlapping Radar Signals Under Low SNR Based on Se-Incepatnet

Cited by 4 publications

References 6 publications

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Parameter Extraction Method of Overlapping Radar Signals Using Modulation Recognition-Guided Semantic Segmentation

Environmental Sound Classification Framework Based on L-mHP Features and SE-ResNet50 Network Model

Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR

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