Two unbiased converted measurement Kalman filtering algorithms with range rate

Sequential unbiased converted measurement non‐linear filter with range rate in ECEF coordinates

“…Hence, the E‐SUCMNFwR can certainly improve the estimation accuracy under the correlated condition. However, the gain is very limited, indicating that the correlation has little effect on the tracking performance, which is consistent with the conclusion given in [19, 28].…”

“…The RMSE values of position and velocity estimates at time step k are defined as and , respectively. The PCRLB is a commonly used lower bound on the optimal accuracy of target state estimation [28, 29], given by the inverse of the posterior Fisher information matrix. The derivation of the PCRLB for the discrete‐time non‐linear filtering problem is discussed in [30].…”

De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

“…When the range rate measurement is available, the pseudo measurement produced by the product of range and range rate can be used to reduce the nonlinearity, which can be processed sequentially to improve the tracking accuracy [13,14]. The pseudo state related to the range rate was introduced in the statically fused (SF) method [15,16], where the position state estimation and pseudo state estimation were combined by a static minimum mean squared error estimator (MMSE). To improve the performance of the converted measurement method, a novel multiplicative unbiased converted measurement Kalman filter algorithm with range rate (UCMKF-R) was developed in [17].…”

Unbiased converted measurement manoeuvering target tracking under maximum correntropy criterion

“…On this basis, with the help of extended Kalman filter (EKF), unscented Kalman filter (UKF) and other non‐linear filtering methods, the target states were estimated in various target‐tracking scenarios [5–8]. (ii) Convert the radial distance and the pitch angle of the radar measurement into the measurement of the position of the target, then the target tracking problems are constructed as linear filtering problems [9–12]. In this context, the famous Kalman filter was utilised to estimate the state of the target.…”