LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver

Chen, Tao; Liu, Lizhi; Huang, Xiangsong

doi:10.1109/access.2018.2845102

Cited by 18 publications

(27 citation statements)

References 34 publications

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“…The center frequency ranged from U(1/8) to U(1/4). The cycles per phase code cpp and code periods number were in the ranges of [1,5] and [100, 300], respectively. For LFM signals, the signal length was 500-1024, the initial frequency was set between U(1/16) and U(1/8), and the bandwidth ∆ f was also set between U(1/16) and U(1/ 8).…”

Section: Create Samplementioning

confidence: 99%

“…In recent years, low probability interception (LPI) radars have been widely used on the battlefield due to their difficulty in being intercepted by non-cooperative receivers. Unlike traditional radar signals, LPI radars have low power, large bandwidth, and frequency changes, giving them powerful combat capabilities and good survivability [1][2][3][4]. At present, how to accurately identify LPI radar waveforms at low SNR has become an important issue in the field of radar countermeasures.…”

Section: Introductionmentioning

confidence: 99%

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LPI Radar Waveform Recognition Based on CNN and TPOT

2019

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The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

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Section: Create Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on CNN and TPOT

2019

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“…Kong et al [4] proposed a convolution neural network with the sample averaging technique to identify LPI radar signals, which is accurate and robust to noise in simulations. Chen et al [5] proposed a recognition method for LPI radar signals based on the short‐time Fourier transform and spectrum energy focusing rate tests, which is very effective to recognise LPI signals in simulations when the signal‐to‐noise ratio (SNR) is >0 dB. Even for agile pulse‐compressed threat signals by airborne radar, Sachin et al [6] thought that the state‐of‐the‐art technologies and systems are able to classify them as well.…”

Section: Introductionmentioning

confidence: 99%

Reliable LPS features of multiple airborne radars against PRS

Wang

Zhang

Wang³

2019

IET Radar, Sonar & Navigation

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“…In current research, there have been some LPI waveform recognition technologies (LWRT), which use feature extraction and classification techniques. Time-frequency analysis (TFA) is widely used in the feature extraction since LPI radar signals are usually non-stationary signals, such as Smoothed Pseudo-Wigner Distribution (SPWD) [7], Wigner Ville Distribution (WVD) [8], Short-Time Fourier Transform (STFT) [9][10][11], and Choi-Williams Distribution (CWD) [6,[12][13][14][15][16]. Combined with deep learning in the field of computer vision [17] and models of neural network structures, researchers have obtained better recognition results from the time-frequency feature of signals [18].…”

mentioning

confidence: 99%

“…The radar signal is first time-frequency transformed into a two-dimensional time-frequency image (TFI), which then is preprocessed and sent to a neural network for training. In the area of classifier design, classification methods include multi-Layer Perceptron (MLP) [11], conditional decision for different features [11,15], Convolutional Neural Networks (CNN) [14], Elman Neural Networks (ENN) [6], and support vector machines (SVM) [6,16]. In addition, there have been hybrid classifiers designed that combine a few different multiple classifiers.…”

mentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Features from Multiple Images

Huang

Lin

2020

Sensors

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Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.Sensors 2020, 20, 526 2 of 23 some existing feature extraction methods are highly targeted, mainly for some specific radar emitter signals. For another thing, these methods rarely involve problems with noise effects and low SNR. In fact, radar signals, especially LPI radar signals, are inevitably subject to large amounts of noise during propagation and reception [5], and LPI radars usually have lower power and it is difficult to directly classify [6]. In current research, there have been some LPI waveform recognition technologies (LWRT), which use feature extraction and classification techniques. Time-frequency analysis (TFA) is widely used in the feature extraction since LPI radar signals are usually non-stationary signals, such as Smoothed Pseudo-Wigner Distribution (SPWD) [7], Wigner Ville Distribution (WVD) [8], Short-Time Fourier Transform (STFT) [9][10][11], and Choi-Williams Distribution (CWD) [6,[12][13][14][15][16]. Combined with deep learning in the field of computer vision [17] and models of neural network structures, researchers have obtained better recognition results from the time-frequency feature of signals [18]. The radar signal is first time-frequency transformed into a two-dimensional time-frequency image (TFI), which then is preprocessed and sent to a neural network for training. In the area of classifier design, classification methods include multi-Layer Perceptron (MLP) [11], conditional decision for different features [11,15], Convolutional Neural Networks (CNN) [14], Elman Neural Networks (ENN) [6], and support vector machines (SVM) [6,16]. In addition, there have been hy...

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LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver

Cited by 18 publications

References 34 publications

LPI Radar Waveform Recognition Based on CNN and TPOT

LPI Radar Waveform Recognition Based on CNN and TPOT

Reliable LPS features of multiple airborne radars against PRS

LPI Radar Waveform Recognition Based on Features from Multiple Images

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