Particle Estimation Algorithm Using Angle between Observation Vectors for Nonlinear System State

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).

引用

页码：700 / 702

页数：3

共 6 条

[1] A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].

Arulampalam, MS ;

Maskell, S ;

Gordon, N ;

Clapp, T .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) :174-188

[2] NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION [J].

GORDON, NJ ;

SALMOND, DJ ;

SMITH, AFM .

IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING, 1993, 140 (02) :107-113

[3] Gaussian particle filtering [J].

Kotecha, JH ;

Djuric, PA .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2003, 51 (10) :2592-2601

[4]

Musso C, 2001, STAT ENG IN, P247

[5] Filtering via simulation: Auxiliary particle filters [J].

Pitt, MK ;

Shephard, N .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (446) :590-599

[6] BAYESIAN STATISTICS WITHOUT TEARS - A SAMPLING RESAMPLING PERSPECTIVE [J].

SMITH, AFM ;

GELFAND, AE .

AMERICAN STATISTICIAN, 1992, 46 (02) :84-88

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