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.

Iterated filters for bearing-only SLAM

Abstract: This paper discusses the importance of iteration when performing the measurement update step for the problem of bearing-only SLAM. We focus on an undelayed approach that initializes a landmark after only one bearing measurement. Traditionally, the extended Kalman filter (EKF) has been used for SLAM, but the EKF measurement update rule can often lead to a divergent state estimate due to its inconsistency in linearization. We discuss the flaws of the EKF in this paper, and show that even the well established inverse-depth parametrization for bearing-only SLAM can be affected. We then show that representing the bearing-only update as a numerical optimization problem (solved with an iterative approach such as Gauss-Newton minimization) prevents divergence of the Kalman filter state and produces accurate SLAM results for a bearing-only sensor. More specifically, we propose the use of an iterated Kalman filter to resolve the issues normally associated with the EKF measurement update. Two outdoor mobile robot experiments are discussed to compare algorithm performance.

A quaternion frequency estimator for three-phase power systems

Abstract: Motivated by the need for accurate frequency estimation in power systems, a novel algorithm for estimating the fundamental frequency of both balanced and unbalanced three-phase power systems, which is robust to noise and distortions, is developed. This is achieved through the use of quaternions in order to provide a unified framework for joint modeling of voltage measurements from all the phases of a three-phase system. Next, the recently introduced ℍℝ-calculus is employed to derive a state space estimator based on the quaternion extended Kalman filter (QEKF). The proposed algorithm is validated over a variety of case studies using both synthetic and real-world data.

Harmonic Analysis Based on Kalman Filtering and Prony's Method

Abstract: This work proposes a technique for harmonic analysis in electrical power systems. Towards this end, a frequency estimator, the Prony's method, has been matched to a Kalman filter. In the proposed technique, the sinusoid amplitudes of electrical power signals are estimated by the Kalman filter. The Kalman filter regressors are built up using the frequencies estimated by the Prony's method. The technique have been tested to both synthetical and experimental signals.

Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors

Abstract: We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.

Convergence Analysis of a Family of Robust Kalman Filters Based on the Contraction Principle

Abstract: In this paper, we analyze the convergence of a family of robust Kalman filters. For each filter of this family, the model uncertainty is tuned according to the so-called tolerance parameter. Assuming that the corresponding state-space model is reachable and observable, we show that the $N$-fold composition of the corresponding Riccati-like mapping is strictly contractive provided that the tolerance is sufficiently small and, accordingly, the filter converges.