A novel cubature Kalman filter for nonlinear state estimation

The cubature Kalman filter (CKF) is more preferred over the unscented Kalman filter (UKF) for its more stable performance. The CKF employs a third-degree spherical-radial cubature rule to numerically compute the integrals encountered in nonlinear filtering problems. The third-degree cubature rule-based filter, however, is not accurate enough in many real-life applications. Moreover, the spherical cubature formula that has been used to develop the CKF has some drawbacks in computation, most notably its inconvenient properties in high-dimensional state estimation problems. To tackle these problems, a new approach to nonlinear state estimation using only an embedded cubature rule, which we have named the square-root embedded cubature Kalman filter (SECKF) is proposed in this work. The experimental results, presented herein, demonstrate the superior performance of the SECKF over conventional nonlinear filters.

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