云层背景下基于混沌理论的红外小目标检测研究

针对云层背景下红外小目标检测难、可用数据集少的问题，提出了一种基于混沌预测的检测方法。首先从云层背景的空间混沌特征出发，采用径向基函数神经网络设计了混沌序列的预测模型，并利用遗传算法优化网络参数，提高预测精度。然后利用预测模型对图像像素序列的预测值与实际值之间的预测误差，实现了小目标检测。最后通过实验验证了上述算法的有效性，对测试样本的检测率为86.7%,虚警率为0.86%。

Research on Infrared Small Target Detection Based on Chaos Theory in Cloudy Background

Aiming at the problems of difficult detection of infrared small targets in cloudy background with few available data sets, a detection method based on chaos prediction was proposed. Based on the spatial chaotic characteristics of cloudy background, the prediction model of chaotic sequence was designed by using radial basis function neural network, and the network parameters were optimized by genetic algorithm to improve the prediction accuracy. Then, the small target detection was realized by the prediction error between the predicted and actual values of the image pixel sequence by the prediction model. Experiments verify the effectiveness of the above algorithm. The detection rate of test samples is 86.7% and the false alarm rate is 0.86%.