Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors.
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.
Pezoa
Jorge E
JE
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque 87131-1356, USA. jpezoa@eece.unm.edu
Hayat
Majeed M
MM
Torres
Sergio N
SN
Rahman
Md Saifur
MS