卡尔曼滤波器
叠加原理
估计员
计算机科学
先验与后验
红外线的
噪音(视频)
算法
人工智能
数学
光学
统计
物理
认识论
图像(数学)
数学分析
哲学
作者
Jorge E. Pezoa,Majeed M. Hayat,Sergio N. Torres,Md. Saifur Rahman
标识
DOI:10.1364/josaa.23.001282
摘要
We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.
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