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.

Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering

Abstract: An unscented Kalman filter-based constant modulus adaptation algorithm (UKF-CMA) is proposed for blind uniform linear beamforming. The proposed algorithm is obtained by first developing a model of the constant modulus (CM) criterion and then fitting that model into the Kalman filter-style state space model by using an auxiliary parameter. The proposed algorithm does not require a priori information about the process noise and measurement noise covariance matrices and hence it can be applied readily. Simulation results demonstrate that the proposed algorithm offers improved performance compared to the recursive least square-based CM (RLS-CMA) and least-mean square-based CM (LMS-CMA) algorithms for adaptive blind beamforming.

Distributed Kalman consensus filter with event-triggered communication: Formulation and stability analysis

Abstract: This paper deals with the distributed estimation problem for networked sensing system with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel. Firstly, an optimal event-triggered Kalman consensus filter (KCF) is derived by minimizing the mean squared error of each estimator based on the send-on-delta triggered protocol. Then, the suboptimal event-triggered KCF is proposed in order to reduce the computational complexity in covariance propagation. Moreover, the formal stability analysis of the estimation error is provided by using the Lyapunov-based approach. Finally, simulation results are presented to demonstrate the effectiveness of the proposed filter.

Historical data-driven state estimation for electric power systems

Abstract: This paper is motivated by major needs for accurate on-line state estimation (SE) in the emerging electrical energy systems; accurate state and topology are needed to support operator's decisions as system conditions vary both during normal conditions for enhanced efficiency and during contingency conditions to ensure reliable operations. We propose a new SE method which is based on a combined use of informative historical data with the extended state space formulation for managing the nonlinear nature of AC power flow equations and related numerical problems. Specifically, the approach comprises two stages. First, based on historical data maximum-likelihood parameter estimation is conducted to update model parameters. The second stage utilizes these estimated model parameters and on-line measurements to estimate the state. Instead of using the extended Kalman Filter we are using a Kalman Filter in a model-based physically meaningful kernel feature space. This leads to ax two-stage Kalman Filter which can overcome problems created by the occasional missing data or data available at different rates (SCADA and PMU data); therefore, we claim that its performance is highly robust. This claim is confirmed by the simulation results performed for several IEEE test systems which show significant improvements over the performance of both the static SE with Newton's method and the extended Kalman Filter SE approach; once the parameters are learned, the computational time is smaller than the currently used SE, making it feasible in operations.

A New Positioning Filter: Phase Smoothing in the Position Domain

Abstract: Motivated by a requirement to provide real-time meter-level positioning of a NASCAR racing car, a modification of the standard Kalman filter was devised. This paper describes an approach that incorporates previous as well as current position states in a Kalman filter to take advantage of phase measurements differenced over time. In this formulation, the phase measurement difference is a measure of the difference in position in the line-of-sight direction to the satellite, so it can act as a relative position constraint of the current position with respect to the previous one. The formulation of the delta-phase observation equation is described, as well as the modifications made to the Kalman filter to incorporate it. An example used to illustrate the effectiveness of the delta-phase measurements in controlling position error growth is included. Test results in various urban environments are presented.

Kalman filtering for matrix estimation

Abstract: A general discrete-time Kalman filter (KF) for state matrix estimation using matrix measurements is presented. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical KF, and includes as special cases other matrix filters that were developed in the past. Beyond the analytical value of the matrix filter, it is shown through various examples arising in engineering problems that this filter can be computationally more efficient than its vectorized version.