Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Konopko, Krzysztof; Grishin, Yu. A.; Janczak, Dariusz

doi:10.1109/sps.2015.7168292

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…However the STFT transform can be also used in parameter estimation of higher order polynomial phase signal (PPS) with polynomial order

[ 19 ]. Next, the problem of detection and estimation of LFM signals can be solved using image processing methods, so it is reduced to the detection of line in an image, which is an easy-solved problem in pattern recognition [ 20 , 21 , 22 ]. The Radon-Wigner transform (RWT), the Wigner Hough transform (WHT) and the Radon-ambiguity transform (RAT) detect LFM signals in a time-frequency image by incoherent energy integration of Wigner-Ville distribution or ambiguity function (AF) in the image.…”

Section: Problem Statementmentioning

confidence: 99%

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution

Swiercz

Janczak

Konopko

2021

Sensors

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Linear frequency-modulated (LFM) signals are the most significant example of waveform used in low probability of intercept (LPI) radars, synthetic aperture radars and modern communication systems. Thus, interception and parameter estimation of the signals is one of the challenges in Electronic Support (ES) systems. The methods, which are widely used to accomplish this task are mainly based on transformations from time to time-frequency domain, which concentrate the energy of signals along an instantaneous frequency (IF) line. The most popular examples of such transforms are the short time Fourier transform (STFT) and Wigner-Ville distribution (WVD). However, for LFM waveforms, methods that concentrate signal energy along a line in the time-frequency rate domain may allow to obtain better detection and estimation performance. This type of transformation can be obtained using the cubic phase (CP) function (CPF). In the paper, the detection of LFM waveform and its chirp rate (CR) parameter estimation based on the extended forms of the standard CPF is proposed. The CPF was originally introduced for instantaneous frequency rate (IFR) estimation for quadratic frequency modulated (QFM) signals i.e., cubic phase signals. Summation or multiplication operations on time cross-sections of the CPF allow to formulate the extended forms of the CPF. Based on these forms, detection test statistics and the estimation procedure of LFM signal parameters have been proposed. The widely known estimation methods assure satisfying accuracy for high SNR levels, but for low SNRs the reliable estimation is a challenge. The proposed approach based on joint analysis of detection and estimation characteristics allows to increase the reliability of chirp rate estimates for low SNRs. The results of Monte-Carlo simulation investigations on LFM signal detection and chirp rate estimation evaluated by the mean squared error (MSE) obtained by the proposed methods with comparisons to the Cramer-Rao lower bound (CRLB) are presented.

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“…However the STFT transform can be also used in parameter estimation of higher order polynomial phase signal (PPS) with polynomial order

Section: Problem Statementmentioning

confidence: 99%

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution

Swiercz

Janczak

Konopko

2021

Sensors

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“…Wenqiang Zhang et al [ 1 ] designed a TPOT-LIME algorithm, which can recognize radar signals from multiple aspects. Krzysztof Konopko et al [ 2 ] used Wigner–Ville distribution to perform time-frequency analysis on the signal, then used a probability density function estimator to extract feature vectors, and finally used a statistical classifier to recognize radar signals. The recognition accuracy is better, but the recognized signal classes are less.…”

Section: Introductionmentioning

confidence: 99%

A Multipulse Radar Signal Recognition Approach via HRF‐Net Deep Learning Models

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.

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“…The accuracy rate of the traditional radar signal recognition mainly relies on the feature extraction algorithm, but the artificial feature extraction relies mostly on the experience of the researchers, the extracted features are targeted to specific types of radar signals, and new features need to be extracted when identifying other signals [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…In the environment of low SNR, the time-frequency image of the signal will be seriously interfered. The approach proposed in [3] adopts a two-dimensional smoothing filter to remove the noise of this, the false recognition rate of the P4-coded signal is reduced from 0.1697 to 0.045 when the SNR is −2 dB. The approach in [5] applies 23×23 size filter for smoothing processing, this operation helps to improve the radar signal recognition accuracy from 76% to 84% when the SNR is −8 dB, but the overall recognition rate still does not exceed 90%.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

Wang

Hou

et al. 2019

IEEE Access

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Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen's time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB. INDEX TERMS Radar signal recognition, Cohen class time frequency distribution, convolutional denoising autoencoder, deep convolutional neural network.

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Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator

Cited by 21 publications

References 11 publications

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution

Detection of LFM Radar Signals and Chirp Rate Estimation Based on Time-Frequency Rate Distribution

A Multipulse Radar Signal Recognition Approach via HRF‐Net Deep Learning Models

Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

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