The Unscented Kalman Filter

  title={The Unscented Kalman Filter},
  author={Eric A. Wan and Rudolph van der Merwe},
All Source Sensor Integration Using an Extended Kalman Filter
Abstract : The global positioning system (GPS) has become an ubiquitous source for navigation in the modern age, especially since the removal of selective availability at the beginning of this
Tracking multiple maneuvering point targets using multiple filter bank in infrared image sequence
  • M. Zaveri, U. Desai, S. Merchant
  • Engineering
    2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)
  • 2003
The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds using single-step decision logic to switch over between filters.
Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters
This work investigates the performance of derivative-free sigma-point Kalman filters for root trajectory tracking over time using conventional gradient-based recursive methods and proposes a framework that is applied to real data to examine the time-frequency characteristics of raw ultrasonic signals from medical ultrasound images.
The aim of this paper is to investigate is the filtering approaches as a way of reducing the amount of data in simultaneous localization and mapping problem.
Unscented Rauch-Tung-Striebel smoothing for nonlinear descriptor systems
This paper extends the application of the unscented Rauch-Tung-Striebel smoother to nonlinear descriptor systems, where the mean and covariance estimates of the algebraic states can also be computed.
Geometric attitude estimation & orbit modelling
Most satellites are equipped with a gyroscope, allowing it to be used in dynamics replacement mode for attitude estimation. However, gyroscopes have been known to fail and not all satellites, such as
A Comparison of JPDA and Belief Propagation for Data Association in SSA
This paper compares association performance on a set of deep-space objects with CAR-MHF using JPDA and BP, and shows that by using the BP algorithm there are significant gains in computational load, with negligible loss in accuracy in the calculation of association probabilities.
A study on Unscented SLAM with path planning algorithm integration
  • H. Nguyen, M. Wongsaisuwan
  • Computer Science
    2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
  • 2014
A framework of Unscented SLAM in integration with A* algorithm to build a map of an unknown environment and guide the robot to reach a prescribed destination so that the robot is able to become truly autonomous.
The "Blob" Filter: Gaussian mixture nonlinear filtering with re-sampling for mixand narrowing
  • M. Psiaki
  • Engineering
    2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014
  • 2014
A new Gaussian mixture filter has been developed, one that uses a re-sampling step in order to limit the covariances of its individual Gaussian components. The new filter has been designed to produce


Stochastic simulation Bayesian approach to multitarget tracking
A stochastic simulation Bayesian method for multitarget tracking is developed. This method uses a random sample in state space to represent the posterior state estimate distribution. The method is
The jackknife, the bootstrap, and other resampling plans
The Jackknife Estimate of Bias The Jackknife Estimate of Variance Bias of the Jackknife Variance Estimate The Bootstrap The Infinitesimal Jackknife The Delta Method and the Influence Function
Stochastic Processes and Filtering Theory
This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
A new algorithm based on a Monte Carlo method that can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low.
Bayesian Statistics 3
Monte carlo filter using the genetic algorithm operators
This study tries to replace the step of the prediction by the mutation and crossover operators in the GA, and proposes a smoothing algorithm in which a massively simple parallel procedure plays an important role.
Abstract. An approach to smoothing and forecasting for time series with missing observations is proposed. For an underlying state-space model, the EM algorithm is used in conjunction with the
An aircraft model for the AIAA Controls Design Challenge
This paper describes a generic, state-of-the-art, high-performan ce aircraft model, including detailed, full-envelope, nonlinear aerodynamics, and full-envelope thrust and first-order engine response
A new approach for filtering nonlinear systems
A new recursive linear estimator for filtering systems with nonlinear process and observation models which can be transformed directly by the system equations to give predictions of the transformed mean and covariance is described.