Optical Society of America
Journal of the Optical Society of America A
This paper presents 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 dynamical-model parameters, which underly the statistics of the scene and the nonuniformity as well as the temporal drift in the nonuniformity. 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 obtained according to the a posteriori -likelihood principle, applied to the family of models by considering the output residual errors associated with each filter. These weights are updated iteratively between blocks of data, providing the estimator the means to follow the dynamics of the scenes and the sensor. The performance of the proposed estimator and its ability to compensate for fixed-pattern noise are tested using both real and simulated data. The real data is obtained using two cameras operating in the mid- and long-wave infrared regime.
Pezoa, Jorge E.; Hayat, Majeed M.; Torres, Sergio N.; and Rahman, Md. Saifur, "Multi-Model Kalman Filtering for Adaptive Nonuniformity: Correction in Infrared Sensors" (2006). Electrical and Computer Engineering Faculty Research and Publications. 627.
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