Abstract

The limitation of the conventional signal-to-noise ratio as a performance measure in matched-filter-based optical pattern recognition for input-scene noise that is disjoint (or nonoverlapping) with the target is investigated.

© 1992 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. J. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theory, IT-6, 311–329 (1960).
    [CrossRef]
  2. B. Javidi, “Generalization of the linear matched filter concept to nonlinear matched filters,” Appl. Opt. 29, 1215–1224 (1990).
    [CrossRef] [PubMed]
  3. A. VanderLugt, “Signal detection by complex filters,” IEEE Trans. Inf. Theory IT-10, 139–145 (1964).
    [CrossRef]
  4. A. Kozma, D. L. Kelly, “Spatial filtering for detection of signals submerged in noise,” Appl. Opt. 4, 387–392 (1965).
    [CrossRef]
  5. J. L. Horner, P. D. Gianino, “Phase-only matched filtering,” Appl. Opt. 23, 812–816 (1984).
    [CrossRef] [PubMed]
  6. J. L. Horner, “Metrics for assessing pattern recognition performance,” Appl. Opt. 31, 165–166 (1992). In personal communications, Horner suggested to us the term PNR to replace PCE”.
    [CrossRef] [PubMed]
  7. B. V. K. Kumar, L. Hasserbrook, “Performance measures for correlation filters,” Appl. Opt. 29, 2997–3006 (1990).
    [CrossRef] [PubMed]

1992 (1)

1990 (2)

1984 (1)

1965 (1)

1964 (1)

A. VanderLugt, “Signal detection by complex filters,” IEEE Trans. Inf. Theory IT-10, 139–145 (1964).
[CrossRef]

1960 (1)

J. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theory, IT-6, 311–329 (1960).
[CrossRef]

Gianino, P. D.

Hasserbrook, L.

Horner, J. L.

Javidi, B.

Kelly, D. L.

Kozma, A.

Kumar, B. V. K.

Turin, J. L.

J. L. Turin, “An introduction to matched filters,” IRE Trans. Inf. Theory, IT-6, 311–329 (1960).
[CrossRef]

VanderLugt, A.

A. VanderLugt, “Signal detection by complex filters,” IEEE Trans. Inf. Theory IT-10, 139–145 (1964).
[CrossRef]

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (3)

Fig. 1
Fig. 1

Target (tank) in the (a) disjoint (nonoverlapping) additive input-scene noise used in the correlation tests and (b) in the overlapping case. The input-noise statistics used in (b) are the same as those used in (a).

Fig. 2
Fig. 2

Examples of the output correlation intensity for the reference signal in the presence of the additive disjoint noise as shown in Fig. 1(a): correlation output obtained by (a) a conventional matched filter and (b) a phase-only filter.

Fig. 3
Fig. 3

Same as Fig. 2 but for the additive overlapping noise as shown in Fig. 1(b).

Tables (1)

Tables Icon

Table 1 Correlation Resultsa

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

s ( x , y ) = r ( x , y ) + n ( x , y ) ,
S ( α , β ) exp [ j ϕ S ( α , β ) ] = R ( α , β ) exp [ j ϕ R ( α , β ) ] + N ( α , β ) exp [ j ϕ N ( α , β ) ] .
H k 1 ( α , β ) = [ R ( α , β ) ] k exp { j [ x 0 α - ϕ R ( α , β ) ] } .
C 1 ( α , β ) = [ R ( α , β ) ] k exp { j [ x 0 α - ϕ R ( α , β ) ] } × R ( α , β ) exp [ j ϕ R ( α , β ) ] + [ R ( α , β ) ] k × exp { j [ x 0 α - ϕ R ( α , β ) ] } × N ( α , β ) exp [ J ϕ N ( α , β ) ] .
SNR = E 2 { s o } Var { s o } .
s o = R ( α , β ) k + 1 d α d β + R ( α , β ) k N ( α β ) exp { j [ ϕ N ( α , β ) - ϕ R ( α , β ) ] } d α d β .
n o = ( x , y ) S 1 S 2 n ( x , y ) r k ( x , y ) d x d y = ( x , y ) S 2 n ( x , y ) r k ( x , y ) d x d y ,
SNR = { R ( α , β ) k + 1 d α d β + ( x , y ) S 2 E [ n ( x , y ) ] r k ( x , y ) d x d y } 2 E ( { ( x , y ) S 2 n ( x , y ) r k ( x , y ) d x d y - ( x , y ) S 2 E [ n ( x , y ) ] r k ( x , y ) d x d y } 2 ) .
PNR s o 2 { j j [ n ( x i , y j ) ] - s o 2 } / N ,

Metrics