  This paper presents a novel adaptive fading cubature Kalman filter (AFCKF)
based on double transitive factors. The developed adaptive algorithm is
explained in two stages; stage (i) a single transitive factor is used to update
the predicted state error covariance, ${\bf \hat P_{k}}^{-}$ based on
innovation or residual vector, whereas, in stage (ii), the measurement noise
covariance matrix, ${\bf \hat R_{k}^{*}}$ is scaled by another transitive
factor. Furthermore, showing the proof concept for estimation of the process
noise, ${\bf \hat Q_{k}^{*}}$ and measurement noise covariance matrices by
combining the innovation and residual vector in the AFCKF algorithm. It can
provide reliable state estimation in the presence of unknown noise statistics.
Bench-marking target tracking example is considered to show the performance
improvement of the developed algorithms. As compared with existing adaptive
approaches, the proposed fading algorithm can provide better estimation
results.
