Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Si, Weijian; Luo, Jiaji; Deng, Zhongliang

doi:10.1155/2022/9970879

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…The waveforms of LPI radar signals are challenging for standard electronic reconnaissance methods to differentiate precisely due to the characteristics of low power, wide bandwidth, high resolution, frequency change, etc. Identification of LPI radar signals and improving the recognition ability of reconnaissance equipment is the difficult task in electronic warfare [3].…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Chilukuri

Kakarla

Rao

2023

Int. j. electr. comput. eng. syst. (Online)

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Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.

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Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Chilukuri

Kakarla

Rao

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Radar, as a result of the continual advancement of radar technology, is widely used on the contemporary battlefield and has gradually risen to the position of becoming the primary technology in modern combat [1]. Systems used in military equipment, such as radar and communication systems, play crucial roles in modern warfare.…”

Section: Introductionmentioning

confidence: 99%

“…However, the study focused on a specific type of frequency agile radar signal, and further research is needed to evaluate the method's performance with different types of agile radar signals. From the author of [1]ResXNet was proposed, with a novel multiscale lightweight attention model, the model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively.…”

Section: Introductionmentioning

confidence: 99%

Profiling radar signals based of pulse-to-pulse frequency agility

Ashraf A. Ahmad,

Mustapha M. Aji,

Yusuf Abdulmumin

et al. 2023

Global J. Eng. Technol. Adv.

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It is well known that the application of radar is becoming more and more popular with the development of signal technology progress. Therefore, this paper presents a first-stage process for radar signals analysis involving four different radar signals based on pulse-to-pulse frequency Agility. The radar signals include a normal radar signal (NRS), frequency hopping radar signal (FHRS), 2-frequency shift keying radar signal (2FSKRS), and a combination of frequency hopping radar signal (FHRS) and 2-frequency shift keying radar signal (2FSKRS). The process of modeling and generating the radar signals is presented and thereafter, results on the outcome of this process and their implications are discussed. It is observed from the obtained results of an accurate depiction of key parameters of pulse width (PW) of 1 µs and frequency of 10 MHz of the radar signals among others, that the developed models of the radar signals are feasible for further analysis using robust model signal processing tools such as time-frequency analysis can be used. Hence, these models can be used in practical radar signal analysis such as electronic intelligence (ELINT) and electronic warfare support (ES).

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“…Tese methods improve the realtime response rate and require no priori knowledge on the motion parameters. However, these methods need to manually extract the features of the echo signal, which is difcult to extract the deep-level features with high discrimination [6]. As an artifcial intelligent method, deep learning (DL) can automatically quarry abstract features from the input data without complicated manual feature extraction and has excellent image classifcation and recognition ability [7].…”

Section: Introductionmentioning

confidence: 99%

Radar Moving Target Detection Method Based on SET2 and AlexNet

Guo

Yang

2022

Mathematical Problems in Engineering

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Aiming at the nonstationary characteristics of echo signal for a high-speed maneuvering target, a signal feature extraction method is proposed by combining the time-frequency analysis and convolution neural network, and then the automatic detection of radar moving target in a noisy environment is realized. Firstly, the echo signal is modelled as a more accurate Gaussian modulation-linear frequency modulation (GM-LFM) signal and converted into the time-frequency image by a second-order synchroextracting transform (SET2). Then, ridge extraction is applied to extract the maximum energy ridge from the time-frequency distribution, and the data set is constructed by the maximum energy ridge. Finally, the data set is input into AlexNet for training, and the deep-level features of echo signal are extracted to realize the automatic moving targets detection. Simulation results show that SET2 and RE can effectively enhance the time-frequency characteristics of echo signal under the noisy environment, and the detection accuracy and noise robustness of the proposed method are better than that of SET1 and smooth pseudo-Wigner–Ville distribution (SPWVD).

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Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Cited by 4 publications

References 29 publications

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Profiling radar signals based of pulse-to-pulse frequency agility

Radar Moving Target Detection Method Based on SET2 and AlexNet

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