Improved Particle Filter

Tian Wang

doi:10.4028/www.scientific.net/AMM.687-691.4072

Paper Titles

Study on the Multi-Tag Identification Anti-Collision Algorithm of RFID in Automobile Assembly Line
p.4056

Research on Modeling Methods of Power Amplifier with Memory Effect in Broadband Communication
p.4060

Wideband DOA Estimation Method Based on Khatri-Rao Subspace with Uniform Focusing
p.4064

The Application Prospect of 4G Communication Technology in Smart Grids
p.4068

Improved Particle Filter
p.4072

A Simplified Receiver Design for SOQPSK-TG
p.4076

Remote Data Acquisition System Design and Application Based on GPRS
p.4080

The Design of Radar Intermediate Frequency Echo Simulator
p.4084

Human Physiological Signal Recognition Based on Piezoelectric Impedance Technique
p.4089

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Improved Particle Filter

Improved Particle Filter

Abstract:

For the particle filter, the paper proposes an approximate algorithm for the case of unknown measurement noise and make a comparison between EKF algorithm and the approximate particle filter for estimating trajectory in a bistatic radar system. Simulation results show that the advantage of the particle filter and theavailability of the approximate particle filter.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

4072-4075

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.4072

Citation:

Cite this paper

Online since:

November 2014

Authors:

Tian Wang*

Keywords:

EKF, Non-Linear Filter, Particle Filter, Target Tracking

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Arulampalam M S, MaskellS, GordonN， A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking[J]，IEEE Trans on signal proc, 2002, 50(2): 174-188.

DOI: 10.1109/78.978374

Google Scholar

[2] Kotecha J H, Djuric P M, Gaussian sum particle filtering[J], IEEE trans signal processing, 2003, 51(10): 2602-2611.

DOI: 10.1109/tsp.2003.816754

Google Scholar

[3] Kotecha J H, Djuric P M, Gaussian particle filtering[J], IEEE trans signal processing, 2003, 51(10): 2592-2601.

DOI: 10.1109/tsp.2003.816758

Google Scholar

[4] Djuric P M, Bugallo M F and Miguez J, Density assisted particle filters for state and parameter estimation[C], IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, 2: 701-704.

DOI: 10.1109/icassp.2004.1326354

Google Scholar

[5] Schiler T L, Erdogmus D and Principe J C, Parzen particle filters[C], IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, 5: 781-784.

DOI: 10.1109/icassp.2004.1327227

Google Scholar

[6] Uosaki K, Kimura Y and Hatanaka T, Evolution strategies based particle filters for state and parameter estimation of nonlinear models[C], IEEE Evolutionary Computation Congress 2004, 1: 884-890.

DOI: 10.1109/cec.2004.1330954

Google Scholar