國立虎尾科技大學 |

Aspect Diversity for Bistatic Synthetic Aperture Radar.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Aspect Diversity for Bistatic Synthetic Aperture Radar./
作者:	Laubie, Ellen E.
面頁冊數:	1 online resource (116 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369839258

Aspect Diversity for Bistatic Synthetic Aperture Radar.
Laubie, Ellen E.

Aspect Diversity for Bistatic Synthetic Aperture Radar. - 1 online resource (116 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Dr.Ph.)--University of Dayton, 2017.

Includes bibliographical references

This dissertation presents a method to improve automatic target recognition by utilizing bistatic synthetic aperture radar (SAR) observations to augment a monostatic SAR observation of the same target with a single, stationary transmitter for improved automatic target recognition (ATR). We investigate the information gain of bistatic perspectives with respect to a monostatic perspective by calculating the correlation coefficient between the monostatic image of a target and the bistatic image of a target for increasing bistatic angles and find a significant information gain as the bistatic angle is increased. Following our information content analysis, we implement decision-level fusion of multiple aspects using majority voting and template matching. Results show improved classification for decision-level fusion. We also investigate image registration using bistatic observations to assess the feasibility of a full aspect-diverse bistatic SAR ATR system. Bistatic images are registered to a monostatic image of the same target. Results yield significant error --- indicating that traditional registration methods are not sufficient for bistatic SAR systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369839258Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Aspect Diversity for Bistatic Synthetic Aperture Radar.
LDR:03372ntm a2200349Ki 4500 001 917812
005 20181022132248.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369839258
035 $a (MiAaPQ)AAI10610192
035 $a (MiAaPQ)OhioLINK:dayton1492420649395159
035 $a AAI10610192
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Laubie, Ellen E. $3 1191985
245 1 0 $a Aspect Diversity for Bistatic Synthetic Aperture Radar.
264 0 $c 2017
300 $a 1 online resource (116 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Advisers: Robert P. Penno, Brian D. Rigling.
502 $a Thesis (Dr.Ph.)--University of Dayton, 2017.
504 $a Includes bibliographical references
520 $a This dissertation presents a method to improve automatic target recognition by utilizing bistatic synthetic aperture radar (SAR) observations to augment a monostatic SAR observation of the same target with a single, stationary transmitter for improved automatic target recognition (ATR). We investigate the information gain of bistatic perspectives with respect to a monostatic perspective by calculating the correlation coefficient between the monostatic image of a target and the bistatic image of a target for increasing bistatic angles and find a significant information gain as the bistatic angle is increased. Following our information content analysis, we implement decision-level fusion of multiple aspects using majority voting and template matching. Results show improved classification for decision-level fusion. We also investigate image registration using bistatic observations to assess the feasibility of a full aspect-diverse bistatic SAR ATR system. Bistatic images are registered to a monostatic image of the same target. Results yield significant error --- indicating that traditional registration methods are not sufficient for bistatic SAR systems.
520 $a In addition to our empirical studies, we also develop an analytical expression that relates the probability of error for a two-class multiple-aspect template-matching classifier to the number of perspectives fused at the image level. This expression allows investigation of the effect of various parameters, such as cross-target correlation and noise variance, on classification performance. We verify our error expression empirically and demonstrate significant improvements in classification for aspect-diverse bistatic SAR ATR.
520 $a Finally, we investigate bistatic perspectives with respect to bistatic angle, and the correlation between opposing targets. We find that the correlation between two targets fluctuates extensively with respect to bistatic angle for a single transmitter location. This makes it difficult to predict "good" perspectives, but simultaneously ensures a high probability that a good perspective will be selected randomly.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Dayton. $b Electrical and Computer Engineering. $3 1184426
773 0 $t Dissertation Abstracts International $g 78-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10610192 $z click for full text (PQDT)