Statistically linearized recursive least squares

This article proposes a new interpretation of the sigma-point kalman filter (SPKF) for parameter estimation as being a statistically linearized recursive least-squares algorithm. This gives new insight on the SPKF for parameter estimation and particularly this provides an alternative proof for a result of Van der Merwe. On the other hand, it legitimates the use of statistical linearization and suggests many ways to use it for parameter estimation, not necessarily in a least-squares sens.