A novel application of the unscented transformation to the debiased CMKF

This paper presents a modification to the original debiased converted-measurement Kalman filter (CMKF), a modern estimation algorithm used for long-range radar and sonar target tracking. The modification improves the original debiased CMKF by approximating the Cartesian-prediction-conditioned means of the converted-measurement error's true bias and covariance using a novel application of the unscented transformation (UT). Simulations show the improved performance of the modified debiased CMKF over that of the original debiased CMKF.