NEW RESULTS IN NONLINEAR STATE ESTIMATION USING EXTENDED UNBIASED FIR FILTERING (original) (raw)
Related papers
Algorithmic Innovations in Extended Unbiased FIR Filtering of Nonlinear Models
Two algorithms of extended unbiased FIR (EFIR) filtering are proposed for nonlinear state estimation. The first algorithm is basic and the second one employs the nonlinear-to-linear observation conversion obtained by the batch EFIR filter with minimum memory. Unlike the extended Kalman filter (EKF), both EFIR algorithms ignore the noise statistics and demonstrate better robustness, but require the optimal horizon. Applications are given for robot indoor self-localization utilizing radio frequency identification tags.
Iterative algorithms for extended unbiased FIR filtering of nonlinear models over sensor networks
Efficient iterative extended finite impulse response (EFIR) filtering algorithms are developed for nonlinear discrete-time state-space estimation. An advantage of the EFIR approach is that the noise statistics are not required on a horizon of N opt points and zero mean noise is allowed to have any distribution and covariance. The EFIR algorithm is developed for nonlinear estimation over sensor networks that implies time-varying matrix structures. A modified EFIR algorithm employs the nonlinear-to-linear observation conversion. Applications are given to robot indoor self-localization over radio frequency identification tag grid excess channels.
AN APPROACH TO NONLINEAR STATE ESTIMATION USING EXTENDED FIR FILTERING
A new technique called extended finite impulse response (EFIR) filtering is developed to nonlinear state estimation in discrete time state space. The EFIR filter belongs to a family of unbiased FIR filters which completely ignore the noise statistics. An optimal averaging horizon of Nopt points required by the EFIR filter can be determined via measurements with much smaller efforts and cost than for the noise statistics. These properties of EFIR filtering are distinctive advantages against the extended Kalman filter (EKF). A payment for this is an Nopt −1 times longer operation which, however, can be reduced to that of the EKF by using parallel computing. Based on extensive simulations of diverse nonlinear models, we show that EFIR filtering is more successful in accuracy and more robust than EKF under the unknown noisestatistics and model uncertainties.
Triangulation-Based Indoor Robot Localization Using Extended FIR/Kalman Filtering
A combined extended finite impulse response (EFIR) and Kalman (EFIR/Kalman) algorithm is proposed for mobile robot localization via triangulation. A distinctive advantage of the EFIR algorithm is that it completely ignores the noise statistics which are typically not well known to the engineer. Instead, it requires an optimal averaging interval of Nopt points. To run this algorithm, several initial Kalman estimates are used for the roughly set noise covariances. We consider a mobile robot travelling on an indoor floorspace and localized via triangulation with three nodes in a view. We show that the EFIR/Kalman filter is more accurate than the extended Kalman filter under the uncertain noise statistics and initial state.
A Generalized Algorithm for Nonlinear State Estimation Using Extended UFIR Filtering
The unbiased finite impulse response (UFIR) filter provides better accuracy when the noise statistics are not fully known. Based on the UFIR approach, a generalized algorithm is developed for extended UFIR (EFIR) filtering of nonlinear models in discrete time state space. As well as the UFIR filter, the EFIR filter completely ignore the noise statistics and requires an optimal averaging horizon of Nopt points. The optimal horizon can be determined via measurements with much smaller efforts and cost than for the noise statistics. These properties of EFIR filtering are distinctive advantages against the extended Kalman filter (EKF). Extensive simulations confirm that the proposed iterative EFIR filtering algorithm is more successful in accuracy and more robust than EKF under the unknown noise statistics and model uncertainties.
A basic requirement for an autonomous mobile robot is to localize itself with respect to a given coordinate system. In this regard two different op-erating conditions exist: structured and unstructured environment. The relative methods and algorithms are strongly influenced by the a priori knowledge on the environment where the robot operates. If the environ-ment is only partially known the localization algorithm needs a preliminary definition of a suitable environ-ment map. In this paper the localization problem is formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and laser scanner measures. As gyroscopic measures are much more re-liable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF. I. INTRODUCTION Two different kinds of mobile robot localization exist: relative and absolute. The first one...
Trends in Sciences, 2022
This paper describes a method in an indoor environment for the estimation and position, using an Unscented Kalman Filter (UKF). The UKF algorithm applied for the position estimation proposing a new measurement uncertainty model that fixes the error covariance according to the distance measurement. In addition, this approach sets the non-diagonal component of the error covariance matrix for the uncertainty of the speed information and the measurement uncertainty to a value other than zero. This method is evaluated through an experiment using a wheel-type mobile robot with an LRF sensor in an indoor environment. In this experiment, we differentiate the estimation execution of the proposed approach with a conventional method that does not employ an adaptive uncertainty model. Moreover, the results improved the estimation performance by setting the non-diagonal component of the error covariance to a value other than zero. The main emphasis of this paper is to implement a practical UKF m...
State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment
Computers, 2020
Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments,...
Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation
Procedia Technology, 2014
For nonlinear state space models to resolve the state estimation problem is difficult or these problems usually do not admit analytic solution. The Extended Kalman Filter (EKF) algorithm is the widely used method for solving nonlinear state estimation applications. This method applies the standard linear Kalman filter algorithm with linearization of the nonlinear system. This algorithm requires that the process and observation noises are Gaussian distributed. The Unscented Kalman Filter (UKF) is a derivative-free alternative method, and it is using one statistical linearization technique. The Particle Filter (PF) methods are recursive implementations of Monte-Carlo based statistical signal processing. The PF algorithm does not require either of the noises to be Gaussian and the posterior probabilities are represented by a set of randomly chosen weighted samples.
The unscented Kalman filter for nonlinear estimation
2000
The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously.