针对传统红外目标检测算法易受目标和背景先验样本质量、目标姿态和视角及噪声等的影响, 提出了一种新的基于稀疏编码的数据驱动二次相关滤波器目标检测算法, 其中给出了目标自相关矩阵基字典的概念, 该数据驱动滤波器模型能包容多种姿态和视角的目标, 并能抑制噪声和样本质量的影响, 同时可以舍弃对无规律背景样本的依赖, 通过对行人和车辆的实验验证了该算法的有效性．所提算法的设计思想对诸多滤波器算法的改进具有很好的借鉴意义．

The traditional target detection methods suffer from the quality of target and background training samples, attitude of target, visual angle of target and noise, etc. In order to overcome these limits, a novel method of data-driven quadratic correlation filter based on sparse coding was proposed, in which the dictionary of target autocorrelation matrix is built. This model not only detects target with multiple attitudes and visual angles, but also is insensitive to noise and the quality of training samples. This model is independent of the randomness in different backgrounds. The experimental results on pedestrian and vehicle show that the proposed algorithm is effective. The idea of proposed algorithm is a good reference for improving the methods of filtering.