Page
paragraph
46
4 and 5
48
158
164
3
3. (para 2)
Section
4.1.4.1
Line
6
3
2
IS
Should be/Suggested
CSSs
NCSSs
in the process
(index or time)
CSSs-ISS
NCSSs-ISS
through the process
-index or time
a)Most accurate track data  I - EOTS
b)Reliability  II - Skin Radar
c) Trajectory coverage aspects  III Telemetry - Skin Radar
 IV C- band - Skin Radar
175
195
207
last
4.5.2
3
5,6
Ref. 23
Ref. 25
Ref. 38
230
232
. This
(see Part 5.2)
32 (2): 5-27
Example 6.1
6.1.4.2
, this
Please delete this
32 (1-2): 5-27
2003.
Y. Bar-Shalom and X. R. Li, 1995. MultitargetMultisensor Tracking: Principles and Techniques,
YBS, Storrs, CT.
Example 6.1: Evaluation of T-norm operations
4/5
AXB or AxB should
not matter as long as
it is understood as a
Cartesian product
233
Example 6.2
Example 6.2: Approximate Equal
233
Example 6.3
239
240
Example 6.4
Example 6.5
Example 6.3: Evaluation and Comparison of
T-Norms
Example 6.4: Evaluation of S-Norms
Example 6.5: Evaluation and Comparison of SNorms
Generalized Modus Ponens (GMP) and
Generalized Modus Tollens (GMT)
Example 6.6: Evaluation of several FIFs
Example 6.7: Evaluation of Norms of FIFs
Methods
GMT)
THEN v is B
Propositional calculus bounded sum algebraic
product (See Figure 6.29)
255
258
264
264
265
266
275
Section 6.2
9
6.2.2
4
6.3.1
2
title
2
Premise 2
1
Example 6.6
Example 6.7
Methods s
GMP)
THEN v IS B
PCBSAP
3
1
PCSUAP
PC standard union algebraic product
(c) if [ Ai ]i1
291
Equation
(6.113)
297
299
302
7.3
TABLE 7.2
Equations
(7.11), (7.12)
7.4.3
2,3
2
Ref. 6
as well as about as
as well as
Journal of Systems Science and Engineering,
System Society of India, Vol. 16, No.1, pp 26-33.
10.2.2.5
10.2.2.6
TABLE 10.10
4
2
The filtered
The filtered
CORR
The filtering
The filtering
correlation
312
352
374
399
which provide
guass
Obtained by taking
two letters at a time
and searching from
Figure 6.29
--DO---/similarly for
all such
combinations of new
FIFs.
…
provide
Ignore the 2nd horizontal line.
gauss
The values of the
metrics in bold face
in Tables 10.10 and
10.11 are better
among others,
showing that the
SFA outperforms
other algorithms.
400
TABLE 10.11
452
3
6,7
473
361
370
376
III.46
1
10.2/1
1
3
3
3
10.2.4
10.3
2
10
8
9
2
4
8
1
4
9
1, 11
2
2
1
1
1
1
1
1
1
1
2
1
11
9
5
8,10
2
1
2
1
3
1
3
6
5
3
2
10
3
2
3
4
12
3
8
2
10
1,10
2
2
9
15
377
380
381
383
385
386
392
393
394
396
397
398
400
401
402
403
404
406
408
417
419
430
431
439
448
449
450
451
452
453
455
460
471
1
10.3.2
1
7. MI
8.
2.
3.
4.
5.
6.
7.
10.3.4
1
1
1
10.4
1
10.4.2
10.5
2
10.5.1
10.5.2
10.5.2.2
2
11.1.1.2
1
11.2.1
11.2.1
11.2.2
11.2.4.2
11.3.1
2
2
11.3.2
1
11.3.3
3
11.3.4
11.3.4.2
1
1
QI
SSIM
Quality index
measure of structural similarity
He
FMI
FSM
FQI
uses a steering
vector model of
filters
[7-10]
[7,11]
[8,9]
[8,9]
[11]
[8,9]
[7,9,11]
[10]
[12]
[9]
[13,14]
[14]
[16]
[16]
[15]
[17]
[17]
[18]
[17]
[13,14]
[9,19,20]
[19]
[19,20]
[19,20]
[9]
[21]
[21]
[21]
[22]
[22]
[22]
[22]
[23]
[23]
[25-27]
[24]
[28,29]
[28]
[28,29]
[29]
[30]
[31,32]
[33,34]
[30-33,35]
[29]
[31]
[29]
[35]
[33]
[36]
[13,37]
[39-41]
Entropy
Fusion Mutual information
Fusion similarity metric
Fusion quality index
uses a model (steering vector) of
processes
[7-11]
[7,12]
[8,10]
[8,10]
[12]
[8,10]
[7,10,12]
[11]
[13]
[10]
[14,15]
[15]
[17]
[17]
[16]
[18]
[18]
[19]
[18]
[14,15]
[10,20,21]
[20]
[20,21]
[20,21]
[10]
[22]
[22]
[22]
[23]
[23]
[23]
[23]
[24]
[24]
[26-28]
[25]
[29,30]
[29]
[29,30]
[30]
[31]
[32,33]
[34,35]
[31-34,36]
[30]
[32]
[30]
[36]
[34]
[37]
[14,38]
[39-42]
1
Chapter
2
Table or Figure
Ch 3
Table 3.12
Ch4
Figure 4.12, 4.13,
and 4.14
Tables 8.3, 8.4, 8.5
Ch 8
3
4
5
Should be/Suggested
Remarks
Adapted from: Challa, S., R. J. Evans, and X.
Wang. 2003. A Bayesian solution and its
approximations to out-of-sequence
measurement problems. Journal of
Information Fusion (3):185–199.
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