给我设计一个用于DOA估计的CNN仿真代码

在设计用于DOA (Direction of Arrival) 估计的CNN (Convolutional Neural Network) 仿真代码时，我们需要考虑几个关键步骤。以下是一个简化的Python示例，使用Keras库作为框架：

# 导入所需的库
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, Flatten, Dense, Dropout
from sklearn.datasets import make_blobs
from scipy.signal import fftconvolve

# 假设我们有MIMO雷达数据生成函数
def generate_MIMORadarData(M, N, K):
    # ... (这里编写数据生成代码)
    return X_train, y_train, X_test, y_test

# 定义CNN模型
def DOA_CNN_model(input_shape, num_classes):
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))  # 输出层用于分类
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# 参数设定
input_shape = (data_width, data_height, channels)  # 根据你的数据调整
num_classes = K  # DOA类别数
epochs = 50
batch_size = 32

# 数据预处理（假设X_train是二维数组）
X_train = X_train.reshape(-1, *input_shape)
X_test = X_test.reshape(-1, *input_shape)

# 生成类别标签
y_train_cat = to_categorical(y_train, num_classes)
y_test_cat = to_categorical(y_test, num_classes)

# 训练模型
model = DOA_CNN_model(input_shape, num_classes)
history = model.fit(X_train, y_train_cat, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test_cat))

# 评估模型性能
loss, accuracy = model.evaluate(X_test, y_test_cat)
print(f"Test Accuracy: {accuracy}")