• Title/Summary/Keyword: and Kalman Filtering

Search Result 324, Processing Time 0.023 seconds

A STUDY ON INITIAL CONVERGENCE PROPERTIES OF THE KALMAN FILLTERING ALGORITHM

  • Park, Dong-Jo
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1988.10b
    • /
    • pp.1051-1054
    • /
    • 1988
  • In this paper we present initial convergence properties of the Kalman filtering algorithm, we put an arbitrary small positive correlation matrix as an initial condition in the recursive algorithm. This arbitrary small initial condition perturbs the Kalman filtering algorithm and may lead to initial instability. We derive a condition which insures the stable operation of the Kalman filtering algorithm from the stochastic Lyapunov difference equation.

  • PDF

Noise Reduction Approach of Nonlinear Function for a Range Image using 2-D Kalman Filtering Method

  • Katayama, Jun;Sekin, Yoshifumi
    • Proceedings of the IEEK Conference
    • /
    • 2000.07b
    • /
    • pp.898-901
    • /
    • 2000
  • A new 2-D block Kalman filtering method which uses a nonlinear function is presented to generate a more accurate filtered estimate of a range image that has been corrupted by additive noise. Novel 2-D block Kalman filtering method is constructed of the conventional method and nonlinear function which utilizes to control estimation error. We show that novel 2-D Kalman filtering method using a nonlinear function is effective at reducing the additive noise, not distorting shape edges.

  • PDF

A Study on the Safety Management of Streamflows by the Kalman Filtering Theory (Kalman Filtering 이론에 의한 하천 유출 안전관리에 관한 연구)

  • 박종권;박종구;이영섭
    • Journal of the Korean Society of Safety
    • /
    • v.11 no.2
    • /
    • pp.122-127
    • /
    • 1996
  • The purpose of this study has been studied and investigated to prediction algorithms of the Kalman Filtering theory which are based on the state-vector description, including system identification, model structure determination, parameter estimation. And the prediction algorithms applied of rainfall-runoff process, has been worked out. The analysis of runoff process and runoff prediction algorithms of the river-basin established, for the verification of prediction algorithms by the Kalman Filtering theory, the observed historical data of the hourly rainfall and streamflows were used for the algorithms. In consisted of the above, Kalman Filtering rainfall-runoff model applied and analysised to Wi-Stream basin in Nak-dong River(Basin area : $472.53km^2$).

  • PDF

A Study on the Digital Distance Relaying Techniques Using Kalman Filtering (칼만필터링에 의한 디지털 거리계전 기법에 관한 연구)

  • 김철환;박남옥;신명철
    • The Transactions of the Korean Institute of Electrical Engineers
    • /
    • v.41 no.3
    • /
    • pp.219-226
    • /
    • 1992
  • In this study, Kalman filtering theory is applied to the estimation of symmetrical components from fault voltage and current signal when it comes to faults with the power system. An algorithm for estimating fault location accurately and quickly by calculating the symmetrical components from the extracted fundamental voltage phasor and current phasor is presented. Also, to confirm the validity of digital distance relaying techniques using Kalman filtering, the experimental results obtained by using the digital simulation of power system is shown.

  • PDF

A Sequencial Adaptive Kalman Filtering for Video Codec Image Enhancement (Video Codec 화질 개선을 위한 순차적 적응형 칼만 필터링 연구)

  • 백원진;이종수;김수원;박진우
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.15 no.12
    • /
    • pp.1031-1043
    • /
    • 1990
  • A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

  • PDF

Robust Multiuser Detection Based on Least p-Norm State Space Filtering Model

  • Zha, Daifeng
    • Journal of Communications and Networks
    • /
    • v.9 no.2
    • /
    • pp.185-191
    • /
    • 2007
  • Alpha stable distribution is better for modeling impulsive noises than Gaussian distribution in signal processing. This class of process has no closed form of probability density function and finite second order moments. In general, Wiener filter theory is not meaningful in S$\alpha$SG environments because the expectations may be unbounded. We proposed a new adaptive recursive least p-norm Kalman filtering algorithm based on least p-norm of innovation process with infinite variances, and a new robust multiuser detection method based on least p-norm Kalman filtering. The simulation experiments show that the proposed new algorithm is more robust than the conventional Kalman filtering multiuser detection algorithm.

Advanced Kalman filter - a survey (칼만필터의 최근 동향 및 발전)

  • 이장규;이연석
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1987.10b
    • /
    • pp.464-469
    • /
    • 1987
  • The Kalman filter is an optimal linear estimator that has been an active research topic for the past three decades. The scheme has become the milestone of modern filtering, and it is applied to many areas including navigations and controls of free vehicle. The Kalman filter technique is matured. But some problems are still remained to be resolved. The prevention of divergence induced by digital implementation, nonoptimal application for nonlinear system, and application to non-Gaussian processes are some of the problems. This paper surveys the problems. The square root filtering is suggested to prevent the divergence. The extended Kalman filter is used for nonlinear systems. And, many other approaches to Kalman-like optimal estimators are also investigated.

  • PDF

Image Sequence Stabilization Scheme Using FIR Filtering

  • Kim, Pyung-Soo
    • International Journal of Control, Automation, and Systems
    • /
    • v.1 no.4
    • /
    • pp.515-519
    • /
    • 2003
  • This paper proposes a new image sequence stabilization (ISS) scheme based on filtering of absolute frame positions. The proposed ISS scheme removes undesired motion effects in real-time, while preserving desired gross camera displacements. The well-known finite impulse response (FIR) filter is adopted for filtering. The proposed ISS scheme provides a filtered position and velocity with fine inherent properties. It is demonstrated that the filtered position is not affected by the constant velocity. It is also shown that the filtered velocity is separated from the position. Via numerical simulations, the performance of the proposed scheme is shown to be superior to the existing Kalman filtering scheme.

Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations

  • Lee, Seong-Soo;Lee, Suk-Han;Kim, Dong-Sung
    • International Journal of Control, Automation, and Systems
    • /
    • v.4 no.6
    • /
    • pp.736-747
    • /
    • 2006
  • Simultaneous Localization and Map Building(SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter(EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.

Short-term Water Demand Forecasting Algorithm Based on Kalman Filtering with Data Mining (데이터 마이닝과 칼만필터링에 기반한 단기 물 수요예측 알고리즘)

  • Choi, Gee-Seon;Shin, Gang-Wook;Lim, Sang-Heui;Chun, Myung-Geun
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.15 no.10
    • /
    • pp.1056-1061
    • /
    • 2009
  • This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.