A new state estimation approach-Adaptive Fading Cubature Kalman filter

TL;DR

This paper introduces an adaptive fading cubature Kalman filter that dynamically estimates noise covariances, improving state estimation accuracy in scenarios with unknown noise statistics, demonstrated through target tracking benchmarks.

Contribution

The paper proposes a novel AFCKF with a two-stage adaptive algorithm for estimating noise covariances, enhancing robustness over existing methods.

Findings

Better estimation accuracy in target tracking scenarios.

Improved robustness to unknown noise statistics.

Outperforms existing adaptive filtering approaches.

Abstract

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.