Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection

Jun Zhang; Antonia Papandreou-Suppappola

doi:10.1109/CAMSAP.2007.4498017

Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection

Jun Zhang, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.

Original language	English (US)
Title of host publication	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages	269-272
Number of pages	4
DOIs	https://doi.org/10.1109/CAMSAP.2007.4498017
State	Published - 2007
Event	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S. Duration: Dec 12 2007 → Dec 14 2007

Publication series

Name	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Other

Other	2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Country/Territory	Virgin Islands, U.S.
City	St. Thomas
Period	12/12/07 → 12/14/07

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/CAMSAP.2007.4498017

Cite this

Zhang, J., & Papandreou-Suppappola, A. (2007). Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP (pp. 269-272). Article 4498017 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP). https://doi.org/10.1109/CAMSAP.2007.4498017

Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection. / Zhang, Jun; Papandreou-Suppappola, Antonia.
2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. p. 269-272 4498017 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, J & Papandreou-Suppappola, A 2007, Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection. in 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP., 4498017, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, pp. 269-272, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP, St. Thomas, Virgin Islands, U.S., 12/12/07. https://doi.org/10.1109/CAMSAP.2007.4498017

Zhang J, Papandreou-Suppappola A. Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection. In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. p. 269-272. 4498017. (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP). doi: 10.1109/CAMSAP.2007.4498017

Zhang, Jun ; Papandreou-Suppappola, Antonia. / Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection. 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP. 2007. pp. 269-272 (2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP).

@inproceedings{80111653938244aa9b75fdc10f5bd7ca,

title = "Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection",

abstract = "We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.",

author = "Jun Zhang and Antonia Papandreou-Suppappola",

year = "2007",

doi = "10.1109/CAMSAP.2007.4498017",

language = "English (US)",

isbn = "9781424417148",

series = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

pages = "269--272",

booktitle = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP",

note = "2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP ; Conference date: 12-12-2007 Through 14-12-2007",

}

TY - GEN

T1 - Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection

AU - Zhang, Jun

AU - Papandreou-Suppappola, Antonia

PY - 2007

Y1 - 2007

N2 - We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.

AB - We propose a matched-field processing framework for tracking problems in shallow water environments where the conventional plane-wave assumptions do not hold. Multiple passive acoustic sensors are employed to collect observation data, and sequential Monte Carlo techniques are used for tracking due to the high nonlinearity in the dynamic state formulation. In order to enhance the tracking performance, we design a frequency selection algorithm which adaptively chooses the optimal observation frequency for the sensors at each time instant. The improved tracking performance is demonstrated using simulations.

UR - http://www.scopus.com/inward/record.url?scp=50249120639&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50249120639&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2007.4498017

DO - 10.1109/CAMSAP.2007.4498017

M3 - Conference contribution

AN - SCOPUS:50249120639

SN - 9781424417148

T3 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

SP - 269

EP - 272

BT - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

T2 - 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Y2 - 12 December 2007 through 14 December 2007

ER -

Sequential Monte Carlo methods for shallow water tracking using multiple sensors with adaptive frequency selection

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this