Invariant extended Kalman filter

About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.

A Robust Technique for Frequency Estimation of Distorted Signals in Power Systems

Abstract: This paper presents a technique comprising a robust extended complex Kalman filter and a sliding-surface-enhanced fuzzy adaptive controller (RECKF-FAC) for frequency and amplitude estimations of distorted signals in a power system. With the aid of fuzzy theory, the proposed approach is more effective for solving the uncertainty of frequency estimation. The robust extended complex Kalman filter (RECKF) is employed to suppress the abnormalities from abnormal data of measurements for promoting the efficiency in frequency estimation, whereas the sliding-surface-enhanced fuzzy adaptive controller (FAC) is used to adjust the Kalman gain and covariance for solving heuristic choices of a hysteresis type of decision. Three cases, including a single sinusoid, harmonic signals, and an actual signal from a stainless-steel factory, are examined to verify the feasibility of the proposed approach. As a result, the proposed approach cannot only perform the extended complex Kalman filter (ECKF) without changing any form but can also enhance the estimation accuracy and reduce the computation time. Results of comparative studies of the technique proposed with the ECKF and RECKF are presented in this paper.

Performance of the shifted Rayleigh filter in single-sensor bearings-only tracking

Abstract: The problem of single-sensor bearings-only tracking continues to present challenges to tracking algorithms, particularly in certain difficult scenarios such as ones with high bearing rates. In such scenarios, the performance of the recently introduced shifted Rayleigh filter (SRF) is compared with that of other techniques such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF). The results are also compared with the theoretical Cramer-Rao Lower Bound (CRLB). The SRF is a moment matching algorithm, and its key feature is that it generates the exact conditional distribution of target motion, given normal approximation to the prior. Simulations show that the SRF is superior to other moment matching algorithms such as EKF and UKF and is able to achieve comparable performance to PF while being orders of magnitude faster.

Robust Kalman filtering via Krein space estimation

Abstract: A robust Kalman filter is proposed for the discrete-time system with norm-bounded parametric uncertainties. The uncertainties are described by the energy bound constraint, i.e. the sum quadratic constraint (SQC). It is shown that the SQC can be converted into an indefinite quadratic cost function to be minimised in the Krein space, and it is found that the Krein space Kalman filter is a solution of the minimisation problem. After introducing a Krein space state-space model, which includes the uncertainty, one can easily write a robust version of the Krein space Kalman filter by modifying the measurement matrix and the variance of measurement noises in the original Krein space Kalman filter. Since the resulting robust Kalman filter has the same recursive structure as a conventional Kalman filter, a robust filtering scheme can be readily designed using the proposed method. A numerical example demonstrates that the proposed filter achieves robustness against parameter variation and improvement in performance when compared with a conventional Kalman filter and an existing robust Kalman filter, respectively.