97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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def wgn(signal, snr=0):
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signal_power = np.mean(signal ** 2)
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noise_power = signal_power / (10 ** (snr / 10))
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noise = np.random.normal(0, np.sqrt(noise_power), len(signal))
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return noise
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def hilbert(signal, num_hilbert=11):
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n = np.arange(-num_hilbert // 2, num_hilbert // 2 + 1)
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hilbert_transformer = np.where(n % 2 != 0, 2j / (np.pi * n), 0)
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hilbert_transformer[num_hilbert // 2] = 1
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return np.convolve(signal, hilbert_transformer, mode='same')
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def pulse_compress(signal_src, signal_ref):
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# 计算所需的FFT长度
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fft_len = 2 * len(signal_src) - 1
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fft_len = int(2 ** np.ceil(np.log2(fft_len)))
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# 对参考信号进行共轭翻转
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signal_ref = np.conj(np.flip(signal_ref))
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# 对信号进行Hamming窗加权
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window = np.hamming(len(signal_ref))
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signal_ref *= window
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# 对输入信号和参考信号进行零padding
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signal_src = np.pad(signal_src, (0, fft_len - len(signal_src)), 'constant')
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signal_ref = np.pad(signal_ref, (0, fft_len - len(signal_ref)), 'constant')
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# 进行FFT和卷积
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signal_src_fft = np.fft.fft(signal_src, fft_len)
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signal_ref_fft = np.fft.fft(signal_ref, fft_len)
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signal_dst_fft = signal_src_fft * signal_ref_fft
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# 执行IFFT得到脉冲压缩信号
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signal_dst = np.fft.ifft(signal_dst_fft, fft_len)
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return signal_dst
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def lfm(tau, fs, fl, bw):
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times = np.linspace(0, tau - 1 / fs, int(tau * fs))
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phase = 2 * np.pi * fl * times + np.pi * bw / tau * times ** 2
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signal = np.exp(1j * phase)
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return signal
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signal = np.real(lfm(7e-6, 20e6, 222e6, 6e6))
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noise_samples = []
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for _ in range(100):
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noise = wgn(signal, 0)
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noise = hilbert(noise)
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noise = pulse_compress(noise, noise)
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noise_samples.extend(noise)
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xpower = np.sum(signal ** 2) / len(signal)
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npower = np.sum(np.abs(noise_samples) ** 2) / len(noise_samples)
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noise_samples = np.abs(noise_samples)
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mean = np.mean(noise_samples)
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var = np.var(noise_samples)
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print(mean, var)
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print(xpower, npower)
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print(np.max(noise_samples))
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# 概率密度函数（PDF）图
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plt.figure()
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sns.kdeplot(noise_samples, bw_adjust=0.5)
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plt.title('Probability Density Function (PDF)')
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plt.savefig('./img/noise_pdf.png')
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# 累积分布函数（CDF）图
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plt.figure()
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sns.ecdfplot(noise_samples)
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plt.title('Cumulative Distribution Function (CDF)')
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plt.savefig('./img/noise_cdf.png')
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# 箱线图（Box Plot）
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plt.figure()
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sns.boxplot(x=noise_samples)
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plt.title('Box Plot')
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plt.savefig('./img/noise_boxplot.png')
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# 显示所有图像
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plt.show()
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