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Passive Filters

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff frequency...

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Updated: May 25, 2026

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Particle filtering for passive fathometer tracking.

Zoi-Heleni Michalopoulou¹, Caglar Yardim, Peter Gerstoft

¹Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA. michalop@njit.edu

The Journal of the Acoustical Society of America

|January 28, 2012

Summary

This summary is machine-generated.

This study introduces a novel multiple model particle filter to track seabed reflectors and their amplitudes, even with an unknown number of layers. The method successfully processes noisy fathometer data from a moving array, improving sub-bottom profiling accuracy.

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Area of Science:

Geophysics
Oceanography
Signal Processing

Background:

Accurate seabed characterization is crucial for offshore engineering and geological surveys.
Traditional methods struggle with dynamic sub-bottom reflector identification and varying fathometer responses.

Purpose of the Study:

To develop and validate a robust method for tracking seabed interface depths and fathometer amplitudes.
To address the challenge of an unknown and changing number of sub-bottom reflectors.

Main Methods:

Incorporation of conventional and adaptive fathometer processors with sequential Monte Carlo methods.
Application of a multiple model particle filter to handle an uncertain number of reflectors.
Comparison with a classical particle filter assuming a known number of reflectors.

Main Results:

Successful tracking of sediment layering information and time-varying fathometer amplitudes.
Demonstrated effectiveness on drifting array data from the Boundary 2003 experiment.
Robust performance of the multiple model particle filter even with noisy fathometer outputs.

Conclusions:

The multiple model particle filter provides a reliable approach for sub-bottom reflector tracking in dynamic marine environments.
This method enhances the accuracy of seabed interface depth and amplitude estimation.
The technique is particularly valuable for processing data from moving sensor arrays with unknown reflector configurations.