Dual-Band Infrared Radiation Attitude Estimation Under Strong Noise Based on Mixture Kernel Correntropy Induced Cost Aided EKF

The dual-band infrared radiation (DBIR) attitude measurement technology is commonly applied to rotating flying projectiles (RFPs). However, RFPs are nonlinear complex systems characterized by multiple variables and strong coupling. Attitude information is easily disturbed by non-Gaussian strong noise, which reduces its reliability. Filtering algorithms based on minimum mean square error cannot effectively eliminate non-Gaussian strong noise, making it particularly challenging to obtain accurate and stable attitude information. To address this issue, we propose the mixture kernel correntropy induced cost for extend Kalman filter (MKCIC_EKF). The performance of filters based on the maximum correlation criterion (MCC) is related to the kernel type, size, and hyperparameters. 1) In terms of kernel type, the mixture kernel mechanism is selected as the induced cost function. It overcomes the defect of single-kernel, which is unable to filter out interference from different probability distributions. 2) In terms of kernel size, we develop an adaptive kernel size mechanism, overcoming the limitation of fixed kernel size. 3) In terms of kernel hyperparameters, the sine cosine algorithm is introduced to obtain optimal hyperparameters of MKCIC_EKF, thus achieving the best attitude estimation. A semiphysical experimental platform was established to verify the performance of MKCIC_EKF. Through experimental validation with real word data, compared with several state-of-the-art algorithms, the proposed MKCIC_EKF algorithm has significantly improved in terms of accuracy, achieving a pitch angle error of$\boldsymbol{\pm}$0.4${{}^{\boldsymbol{\circ}}}$and a roll angle error of$\boldsymbol{\pm}$0. 7${{}^{\boldsymbol{\circ}}}$.