Abstract
In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.
Highlights
There are considerable interests in radar systems that "to see and not be seen" can commonly called low probability of intercept (LPI) radar [1]
Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data
Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of –2 dB
Summary
There are considerable interests in radar systems that "to see and not be seen" can commonly called low probability of intercept (LPI) radar [1]. Choi–Williams time-frequency distribution (CWD) image processing to extract signal features, and the RSR is more than 80% at the SNR of 0 dB [9,10]. Our major contributions are summarized as follows: (1) modifying the type of network of classifier (using Elman neural network instead of traditional neural network), CWD image feature extraction (using skeleton extraction instead of Wigner–Ville distribution, data driven and peak search) provided in Lundén’s approaches and improving the experimental results effectively (see Section 6); (2) creating two original features, θmax and âmax , which increase the recognition ability; (3) the proposed approaches being classified into eight kinds of waveforms without prior knowledge; and (4) proposing a new classifier structure, with two three-layer Elman neural networks.
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