Using a multiple analytical distribution filter for underwater localization

Dov Kruger; Hongyuan Shi; Yingying Chen; Hongbo Liu; Jie Yang; Len Imas

doi:10.1117/12.834893

Using a multiple analytical distribution filter for underwater localization

Dov Kruger, Hongyuan Shi, Yingying Chen, Hongbo Liu, Jie Yang, Len Imas

School of Engineering, Electrical & Computer Engineering

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

Abstract

This paper presents a high efficiency algorithm, Multiple Analytical Distribution Filter (MADF), to estimate location for underwater navigation. Using small grid sampling around candidate areas of high probability, MADF computes probabilities directly from the known analytical distributions of each beacon. The algorithm is deterministic and achieves similar results to particle filters, but at a lower computational cost in our tests. MADF and particle filters represent improvements over Kalman Filters for environments characterized by non-Gaussian noise distribution.

Original language	English (US)
Title of host publication	Unmanned/Unattended Sensors and Sensor Networks VI
DOIs	https://doi.org/10.1117/12.834893
State	Published - 2009
Event	Unmanned/Unattended Sensors and Sensor Networks VI Conference - Berlin, Germany Duration: Sep 1 2009 → Sep 3 2009

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7480
ISSN (Print)	0277-786X

Other

Other	Unmanned/Unattended Sensors and Sensor Networks VI Conference
Country	Germany
City	Berlin
Period	9/1/09 → 9/3/09

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Keywords

Localization
MADF
Particle filter
Underwater navigation

Access to Document

10.1117/12.834893

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Cite this

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title = "Using a multiple analytical distribution filter for underwater localization",

abstract = "This paper presents a high efficiency algorithm, Multiple Analytical Distribution Filter (MADF), to estimate location for underwater navigation. Using small grid sampling around candidate areas of high probability, MADF computes probabilities directly from the known analytical distributions of each beacon. The algorithm is deterministic and achieves similar results to particle filters, but at a lower computational cost in our tests. MADF and particle filters represent improvements over Kalman Filters for environments characterized by non-Gaussian noise distribution.",

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Kruger, D, Shi, H, Chen, Y, Liu, H, Yang, J & Imas, L 2009, Using a multiple analytical distribution filter for underwater localization. in Unmanned/Unattended Sensors and Sensor Networks VI., 74800T, Proceedings of SPIE - The International Society for Optical Engineering, vol. 7480, Unmanned/Unattended Sensors and Sensor Networks VI Conference, Berlin, Germany, 9/1/09. https://doi.org/10.1117/12.834893

Using a multiple analytical distribution filter for underwater localization. / Kruger, Dov; Shi, Hongyuan; Chen, Yingying; Liu, Hongbo; Yang, Jie; Imas, Len.

Unmanned/Unattended Sensors and Sensor Networks VI. 2009. 74800T (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 7480).

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

TY - GEN

T1 - Using a multiple analytical distribution filter for underwater localization

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AU - Yang, Jie

AU - Imas, Len

PY - 2009

Y1 - 2009

N2 - This paper presents a high efficiency algorithm, Multiple Analytical Distribution Filter (MADF), to estimate location for underwater navigation. Using small grid sampling around candidate areas of high probability, MADF computes probabilities directly from the known analytical distributions of each beacon. The algorithm is deterministic and achieves similar results to particle filters, but at a lower computational cost in our tests. MADF and particle filters represent improvements over Kalman Filters for environments characterized by non-Gaussian noise distribution.

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KW - Underwater navigation

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