This paper suggests a few novel Cholesky-based square-root algorithms for the maximum correntropy criterion Kalman filtering. In contrast to the previously obtained results, new algorithms are developed in the so-called {\it condensed} form that corresponds to the {\it a priori} filtering. Square-root filter implementations are known to possess a better conditioning and improved numerical robustness when solving ill-conditioned estimation problems. Additionally, the new algorithms permit easier propagation of the state estimate and do not require a back-substitution for computing the estimate. Performance of novel filtering methods is examined by using a fourth order benchmark navigation system example.

翻译：本文提出几种基于乔列斯基分解的新型平方根算法，用于实现最大相关熵准则卡尔曼滤波。与已有研究成果不同，新算法采用所谓的"压缩"形式，对应于先验滤波。已知平方根滤波器实现方法在解决病态估计问题时具有更好的条件数和数值鲁棒性。此外，新算法能够更简便地传播状态估计量，且无需通过回代过程即可计算估计值。通过一个四阶基准导航系统实例，对所提新型滤波方法的性能进行了检验。