An adaptive filter using scanning observation with application to a DPS (original) (raw)

An Approach for Distributed Kalman Filtering

In this article we propose a parallel and distributed state estimation structure developed from an hierarchical estimation structure, optimal in the sense of Kalman filtering and that is based on the multiple projections method. We explore a duality that exists between two state space representations, derived from the application of an approach based on the coupling and noise terms of the original system. The algebraic structure developed is suboptimal, due to the fact that it does not take into account the corrections of the state predictions based on the multiple innovations. This approach contributes to the design of distributed Kalman filtering algorithms.

On existence, optimality and asymptotic stability of the Kalman filter with partially observed inputs

For linear stochastic time-varying systems, we investigate the properties of the Kalman filter with partially observed inputs. We first establish the existence condition of a general linear filter when the unknown inputs are partially observed. Then we examine the optimality of the Kalman filter with partially observed inputs. Finally, on the basis of the established existence condition and optimality result, we investigate asymptotic stability of the filter for the corresponding time-invariant systems. It is shown that the results on existence and asymptotic stability obtained in this paper provide a unified approach to accommodating a variety of filtering scenarios as its special cases, including the classical Kalman filter and state estimation with unknown inputs.

Stability of Consensus Extended Kalman Filtering for Distributed State Estimation

IFAC Proceedings Volumes, 2014

The paper addresses consensus-based networked estimation of the state of a nonlinear dynamical system. The focus is on a family of distributed state estimation algorithms which relies on the extended Kalman filter linearization paradigm. Consensus is exploited in order to fuse the information, both prior and novel, available in each network node. It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability. A simulation case-study concerning target tracking with a network of nonlinear (angle and range) position sensors is worked out in order to show the effectiveness of the considered nonlinear consensus filter.

Kalman Filters: Theory and Implementation

We focus primarily on the theory of Discrete Kalman Filters, and have implemented the algorithm in MATLAB using simulations technique. We also have applied the algorithm on a simpli ed model of the "navigation and control" problem.

A The Kalman Filter

2006

The Kalman Filter developed in the early sixties by R.E. Kalman is a recursive state estimator for partially observed non-stationary stochastic processes. It gives an optimal estimate in the least squares sense of the actual value of a state vector from noisy observations.

An interlaced extended Kalman filter

IEEE Transactions on Automatic Control, 1999

In this paper an estimation algorithm for a class of discretetime nonlinear systems is proposed. The system structure we deal with is partitionable into m subsystems, each affine w.r.t. the corresponding part of the state vector. The algorithm consists of a bank of m interlaced Kalman filters, and each of them estimates a part of the state, considering the remaining parts as known time-varying parameters whose values are evaluated by the other filters at the previous step. The procedure neglects the subsystem coupling terms in the covariance matrix of the state estimation error and counteracts the errors so introduced by suitably "increasing" the noise covariance matrices. Comparisons through numerical simulations with the extended Kalman filter and its modified versions proposed in the literature illustrate the good tradeoff provided by the algorithm between the reduction of the computational load and the estimation accuracy.

Spacecraft tracking using sampled-data Kalman filters

IEEE Control Systems Magazine, 2008

T he problem of estimating the state of a dynamical system based on limited measurements arises in many applications. For the case of a linear system with known dynamics and Gaussian noise, the classical Kalman filter KF) provides the optimal solution [1], [2]. However, state estimation for nonlinear systems remains a challenging problem of intense research interest. Optimal nonlinear filters [3] are often infinite dimensional and thus are difficult to implement [4]. Within a deterministic setting, nonlinear observers are available for systems of special structure [5], [6]. Except for systems of special structure, however, approximate filters are usually implemented in practice.

A modified extended kalman filter as a parameter estimator for linear discrete-time systems

1988

Title of Thesis: A Modified Extended Kalman Filter As A Parameter Estimator For Linear Discrete-Time Systems Bruno J. Schnekenburger Master of Science, 1988 Thesis directed by: Prof. Dr. Andrew U. Meyer Asst. Prof. Dr. B. Tank Oranc This thesis presents the derivation and implementation of a modified Extended Kalman Filter used for joint state and parameter estimation of linear discrete-time systems operating in a stochastic Gaussian environment. A novel derivation for the discrete-time Extended Kalman Filter is also presented. In order to eliminate the main deficiencies of the Extended Kalman Filter, which are divergence and biasedness of its estimates, the filter algorithm has been modified. The primary modifications are due to Ujung, who stated global convergence properties for the modified Extended Kalman Filter, when used as a parameter estimator for linear systems. Implementation of this filter is further complicated by the need to initialize the parameter estimate error covar...