SDR/.history/algo/anti_jamming_signal_algo_20250421164110.py

import numpy as np
from scipy import signal, fft, linalg
from typing import Tuple, List


class AntiJammingSignalAlgo:
    @staticmethod
    def time_frequency_filter(rx_signal, sample_rate):
        """时频域联合滤波算法"""
        # 输入信号维度检查
        if rx_signal.size == 0:
            return np.zeros_like(rx_signal)
            
        # 确保信号为1维数组
        rx_signal = np.atleast_1d(rx_signal).ravel()
        
        spectrum = np.fft.fft(rx_signal)
        filtered_spectrum = spectrum * np.hamming(len(spectrum))
        return np.fft.ifft(filtered_spectrum).real

    @staticmethod
    def adaptive_filter(rx_signal, reference_signal):
        """自适应滤波算法"""
        # 实现基于LMS的自适应滤波
        lms_step = 0.01
        weights = np.zeros(10)
        for i in range(len(rx_signal)-10):
            x = rx_signal[i:i+10]
            error = reference_signal[i] - np.dot(weights, x)
            weights += lms_step * error * x
        return np.convolve(weights, rx_signal, mode='valid')

    @staticmethod
    def polarization_filter(rx_signal, sample_rate, polarization_params):
        """极化滤波算法"""
        # 应用极化参数
        angle_rad = np.deg2rad(polarization_params['angle'])
        ellipticity = polarization_params['ellipticity']
        
        # 构建极化滤波矩阵
        filter_matrix = np.array([
            [np.cos(angle_rad), -ellipticity * np.sin(angle_rad)],
            [np.sin(angle_rad), ellipticity * np.cos(angle_rad)]
        ])
        
        # 应用极化滤波
        polarized_signal = filter_matrix @ rx_signal.reshape(2, -1)
        return polarized_signal[0].real