提出了一种基于模糊最小二乘支持向量机(FLS-SVM)进行背景预测、利用模糊Tsallis-Havrda-Charvat熵实现阈值分割的红外弱小目标检测方法。首先采用FLS-SVM对训练样本进行学习得到回归函数，并以此预测红外图像中的背景；然后将原始图像与预测图像相减得到残差图像，并提出基于模糊Tsallis-Havrda-Charvat熵的阈值选取算法分割残差图像，将小目标和噪声从残差背景中分割出来；最后利用目标灰度的平稳性和运动轨迹的连续性进一步检测出真实的小目标。给出了实验结果及分析，并与基于最小二乘支持向量机(LS-SVM)以及基于最小二乘的背景预测方法的检测结果进行了比较。结果表明，该方法具有更高的检测概率和信噪比增益，优于上述基于背景预测的红外小目标检测方法。

A detection method of small target in infrared image is proposed, which is based on the background predication by fuzzy least squares support vector machine (FLS-SVM) and threshold segmentation by fuzzy Tsallis-Havrda-Charvat entropy. Firstly, the fitting function is obtained from the training samples by using FLS-SVM and the background in infrared image is predicted. Then, the predicted image subtracted from the source image gives the residual-error image. The residual-error image is segmented by the proposed threshold selection method based on fuzzy Tsallis-Havrda-Charvat entropy so as to separate small target and noise from the residual background. Finally, the true small target is further detected based on the stability of the target gray and the consistency of target trajectory. The experimental results are given and analyzed. They are compared with the detection results of the background predication methods based on LS-SVM or least squares. The results show that the proposed method has higher detection probability and the gain of signal-to-noise ratio (GSNR) and it is superior to the above-mentioned methods.