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

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Using a multiple analytical distribution filter for underwater localization

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

Proceedings of SPIE, Vol.7480(1), pp.74800T-74800T-8

Unmanned/Unattended Sensors and Sensor Networks VI

09/17/2009

DOI: https://doi.org/10.1117/12.834893

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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Title: Using a multiple analytical distribution filter for underwater localization
Creators: Dov Kruger - Stevens Institute of Technology
Hongyuan Shi - Stevens Institute of Technology
Yingying Chen - Stevens Institute of Technology
Hongbo Liu - Stevens Institute of Technology
Jie Yang - Stevens Institute of Technology
Len Imas - Stevens Institute of Technology
Publication Details: Proceedings of SPIE, Vol.7480(1), pp.74800T-74800T-8
Conference: Unmanned/Unattended Sensors and Sensor Networks VI
Date published: 09/17/2009
Academic Unit: Electrical and Computer Engineering (SOE)
Language: English
Resource Type: Conference proceeding
Identifiers: 991031758045104646