根据高斯噪音高于二阶的累积量为零的特性，提出基于四阶累计量的红外焦平面固定噪音乘性参量估计算法，完成从图象场景中自适应估计阵列单元的增益．利用泰勒级数展开给出乘性参量估计均值和方差的统计特性，证明算法给出乘性增益的估计为渐进无偏估计．提出的算法用蒙特卡罗仿真给予验证，证明了算法的有效性．并将提出的算法应用于实际图象的处理，有效降低了固定图案噪音，达到了较高的非均匀校正水平．

According to the property that for Gaussian signals only,all cumulant spectral of order greater than two are identically zero,a Four-Order-Cumulant based Parameter Estimation (HOS-PE) approach was undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The statistical property of mean and variance for the gain parameter was given by mean of Taylor expansion and the analysis proves the estimation was approximately unbiased. The proposed method has been tested by the Monte Carlo simulation and verified. At last,HOS-PE method was tested through video sequences of real infrared data,reducing the fixed pattern noise and reaching high correction levels.