Space-time registration of radar and ESM using unscented Kalman filter

Celestial Navigation Methods for Space Explorers

“…EKF is computationally simple, and optimal for application to linear and Gaussian systems, but may introduce large estimation errors in celestial navigation system, which is nonlinear and non-Gaussian. The Unscented Kalman Filter (UKF) [12,13] has been proven to have a better performance than EKF in nonlinear system state estimation [14,15] , which uses the true nonlinear model and a set of sigma sample points produced by the unscented transformation to capture the mean and covariance of state. However, UKF also assumes that the true distribution is Gaussian.…”

A new autonomous celestial navigation method for the lunar rover

“…To the complex nonlinear equations like (9), it is hard to linearize by Taylor series expansion. The Unscented Kalman Filter (UKF) [21,22] has been proven to have a better performance than EKF in nonlinear system state estimation [23,24], which uses the true nonlinear model and a set of sigma sample points produced by the unscented transformation to capture the mean and covariance of the state. But UKF also assumes that the true distribution is Gaussian.…”

Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

“…In [7], Okello, et al formulated the joint registration and fusion at the track level as a Bayesian estimation problem, and proposed the extended Kalman filter (EKF) by augmenting the state vector with sensor bias. In [8], the augmented Kalman filter was proposed to perform the sensor registration.…”

“…Combining them with the expectation-maximization (EM) algorithm, states and parameters are formulated via Recursive Variational Bayesian Expectation Maximization (RVBEM). Compared with the augmented Kalman filter [8], the proposed method has a better performance.…”