LPI Radar Waveform Recognition Based on Time-Frequency Distribution

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.

Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar

“…The antennas of the transmit array and the receive array are both half-wavelength spaced, and their numbers are $M=6$ and $N=6$, respectively. The transmitted signals are assumed to be BPSK modulated [27,30], moreover, they are mutually orthogonal and different from each other. The noise is white Gaussian with the covariance matrix $\mathrm{boldR}={\mathbf{I}}_{MN}$, or colored Gaussian with the elements in the covariance matrix being $\mathrm{boldR}(k_1,k_2)=0.{75}^{|k_1-k_2|}{e}^{j\pi (k_1-k_2)/2}$.…”

Radar Waveform Recognition Based on Time-Frequency Analysis and Artificial Bee Colony-Support Vector Machine

“…In the scout bees stage, if the solution x i is not improved within limit, it will be abandoned. The employed bees of that position will turn into the scouts and a new solution is produced by Formula (8). When the number of cycles reaches the maximum cycle number (MCN), the best solution will be obtained.…”

“…However, the algorithm requires prior information and the RSR is 98% at SNR of 6 dB. In [8], Zhang proposes the system to recognize eight radar waveforms. The RSR is 94.7% at 94.7% at SNR of −2 dB and the number of features is 23.…”

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

“…However, with more and more types of radar waveforms, the classification process of decision tree theory also increases, and the decision tree becomes more and more complex. Pattern recognition theory and artificial neural network can effectively solve these problems [27,28]. Taking the support vector machine (SVM) and extreme learning machine (ELM) classifiers as example, SVM and ELM construct feature space according to feature dimension, and establish classification hyperplane between features of different categories [29,30].…”