Particle Filter with Hybrid Proposal Distribution for Nonlinear State Estimation

نویسندگان

  • Fasheng Wang
  • Yuejin Lin
  • Tao Zhang
  • Jingbo Liu
چکیده

Particle filters have been widely used in solving nonlinear filtering problems. Proposal Distribution design is a key issue for these methods and has vital effects on simulation results. Various proposal distributions have been proposed to improve the performance of particle filters, but practical situations have encouraged the researchers to design better candidate for proposal distributions in order to gain better performance. This paper proposes a hybrid proposal distribution designed for particle filtering framework. The algorithm uses hybrid Kalman filter to generate the proposal distribution, which make efficient use of the latest observations and generates more close approximation of the posterior probability density. The yielded algorithm is named as hybrid Kalman particle filter. In the experiments, a scalar estimation model and a real world application problem are used to evaluate the new algorithm. The experimental results show that the new algorithm outperforms any other algorithm with different proposal distributions. In order to reduce the time consumption of the new algorithm, an improvement strategy, namely partition-conquer strategy, is proposed, in which the particles are partitioned into two parts, with one part drawn from the hybrid Kalman filter, the other part from the transition prior. The validity of the strategy is verified through an experiment.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A New Modified Particle Filter With Application in Target Tracking

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...

متن کامل

Constrained State Estimation Using Particle Filters

Recursive estimation of constrained nonlinear dynamical systems has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used. As pointed out by Daum (2005), particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approa...

متن کامل

Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters

The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...

متن کامل

Rotated Unscented Kalman Filter for Two State Nonlinear Systems

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

متن کامل

An Improved Particle Filter with Applications in Ballistic Target Tracking

In this paper, we present an improved particle filter algorithm for ballistic target tracking, the quadrature Kalman particle filter (QKPF). The proposed algorithm uses quadrature Kalman filter (QKF) for generating the proposal distribution. The QKF is a recursive, nonlinear filtering algorithm developed in the Kalman filtering framework. It linearizes the nonlinear functions using statistical ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • JCP

دوره 6  شماره 

صفحات  -

تاریخ انتشار 2011