2026-09-18T20:11:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/370522022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Charles E. Rohrs.
Bland, Ross E. (Ross Edward)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2007-04-03T17:06:05Z
2007-04-03T17:06:05Z
2006
2006
http://hdl.handle.net/1721.1/37052
79629647
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 83-84).
The problem of detecting footsteps using acoustic and seismic sensors is approached from three different angles in this thesis. First, accelerometer data processing systems are designed to make footsteps more apparent to a human operator listening to accelerometer recordings. These systems work by modulating footstep signal energy into the ear's most sensitive frequency bands. Second, linear predictive modeling is shown to be an effective means to detect footsteps in accelerometer and microphone data. The time evolution of the third order linear prediction coefficients leads to the classical binary hypothesis testing framework. Lastly, a new method for blindly estimating the filters of a SIMO channel is presented. This method is attractive because it allows for a more tractable performance analysis.
by Ross E. Bland.
M.Eng.
84 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Acoustic and seismic signal processing for footsetp detection
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9b9ef07b-b930-412b-a09c-999b90372c4b">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Acoustic and seismic signal processing for footsetp detection</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2006</PublicationDate>
<Authors>
<Author>
<DisplayName>Bland, Ross E. (Ross Edward)</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>The problem of detecting footsteps using acoustic and seismic sensors is approached from three different angles in this thesis. First, accelerometer data processing systems are designed to make footsteps more apparent to a human operator listening to accelerometer recordings. These systems work by modulating footstep signal energy into the ear's most sensitive frequency bands. Second, linear predictive modeling is shown to be an effective means to detect footsteps in accelerometer and microphone data. The time evolution of the third order linear prediction coefficients leads to the classical binary hypothesis testing framework. Lastly, a new method for blindly estimating the filters of a SIMO channel is presented. This method is attractive because it allows for a more tractable performance analysis.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>