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

24 September 2009 Using a multiple analytical distribution filter for underwater localization

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

Author Affiliations +

Proceedings Volume 7480, Unmanned/Unattended Sensors and Sensor Networks VI; 74800T (2009) https://doi.org/10.1117/12.834893
Event: SPIE Security + Defence, 2009, Berlin, Germany

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.

Citation Download Citation

Dov Kruger, Hongyuan Shi, Yingying Chen, Hongbo Liu, Jie Yang, and Len Imas "Using a multiple analytical distribution filter for underwater localization", Proc. SPIE 7480, Unmanned/Unattended Sensors and Sensor Networks VI, 74800T (24 September 2009); https://doi.org/10.1117/12.834893

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