In this paper, basic principles and relative parameters of evaporation duct were outlined. At the same time, the calculating methods for atmospheric modified refractivity were deduced basing on model A, which is very important for electromagnetic wave propagation loss in the atmosphere duct over the Sea. There is an integral relation between universal function and nondimensional Monin-Obukhov profile, which was general acquiescence in meteorology. However, through simulation and comparison, it is found that the integral relation is approximately correct when Monin-Obukhov length’s absolute value is bigger, and it is false when the absolute value is littler. According the analysis results, correct atmospheric modify refractivity profile model was deduced for different conditions basing on model A.
To solve the reconnaissance problem of Linear Frequency Modulation (LFM) pulse signal, this paper proposes an estimation method for the full set of parameters of frequency modulation slope, initial frequency, pulse repetition period, duty cycle, and pulse width. Firstly, the Fractional Fourier Transform (FRFT) is used to estimate the frequency modulation slope and the initial frequency of the signal. Secondly, the method of periodic accumulation is used to improve the signal-to-noise ratio, and a specific threshold value is set to estimate the duty cycle. Finally, the parameters of pulse repetition period and pulse width are estimated by autocorrelation function method. The simulation results show that the method can accurately detect the relevant parameters of LFM pulse signal.
KEYWORDS: Radar signal processing, Time-frequency analysis, Convolutional neural networks, Signal to noise ratio, Feature extraction, Windows, Signal processing, Signal detection, Modulation, Neural networks
To solve the problem of difficult feature extraction and low recognition rate of radar signal under low signal-to-noise ratio, this paper proposes a radar signal recognition method based on time-frequency feature extraction and convolutional neural network. This method uses short-term Fourier transform (STFT) to obtain two-dimensional time-frequency images of radar signals, and then sends the images to convolutional neural networks for deep feature extraction, and realizes the classification and recognition of radar signals through convolutional neural network classifiers. The simulation results show that for different intra-pulse modulated radar signals, when the signal-to-noise ratio is -5dB, the overall recognition accuracy of the proposed model can reach more than 93%, which effectively solves the problem of low radar signal recognition rate under low signal-to-noise ratio.
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