Tài liệu Tracking and Kalman filtering made easy P5 ppt
Tài liệu Tracking and Kalman filtering made easy P5 ppt
... insight into the Legendre orthogonal polynomial fit approach will be
obtained.
205
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John Wiley & Sons, Inc.
ISBNs: 0-471-18407-1 ... matrix and their derivatives are small in the observation interval and
increase monotonically outside; see Figures 5.8-2 and 5.8-3. Because the
systematic errors and thei...
Tài liệu Tracking and Kalman filtering made easy P6 ppt
... obtained
when using a short memory for the fading-memory filter discussed in
Chapter 7.
233
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John Wiley & Sons, Inc.
ISBNs: 0-471-18407-1 ... disadvantages are the
expanding-memory filter and the fading memory filter. The expanding
memory filter is, as discussed in Section 1.2.10 and later in Section 7.6,
sui...
Tài liệu Tracking and Kalman filtering made easy P8 ppt
... d and the
transition matrix is a generalized form of (5.4-7). Note that in this text d
represents the true degree of the target dynamics while m is the degree used by
252
Tracking and Kalman Filtering ... degree of the target dynamics while m is the degree used by
252
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John Wiley & Sons, Inc.
ISBNs:...
Tài liệu Tracking and Kalman filtering made easy P10 pptx
... and also from (4.3-16) and (4.3-17) and reference 79
(p. 57), that
F
T
F ¼ I ¼ FF
T
ð10:2-1Þ
and
F
À1
¼ F
T
ð10:2-2Þ
Also
kFYk¼kYkð10:2-3Þ
where kk is the Euclidean norm defined by (4.2-40) and ... space of F. From (10.2-8) and (10.2-9), and
272
VOLTAGE LEAST-SQUARES ALGORITHMS REVISITED
Define F
1
and F
2
as the matirces formed by, respectively, the first m
0
rows of
F and re...
Tài liệu Tracking and Kalman filtering made easy P14 pptx
... normal
equations and the excellent results obtained using the modified Gram–Schmidt
algorithm. For this example (obtained from reference 82)
339
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright ... In this case T and in turn U are not known in
advance, the drop out of data points being random events.
14.5 SQUARE-ROOT KALMAN FILTERS
The Kalman filters discussed...
Tài liệu Tracking and Kalman filtering made easy P15 ppt
... the dynamic model differential equation and show how it can be
numerically integrated to obtain Èðt
n
; t
nÀi
Þ.
354
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John ... M, È, and T replaced by M
n
,
Èðt
n
; t
nÀi
Þ, and T
n
, respectively; see (4.1-5) through (4.1-31). Accordingly, the
least-squares and minimum-variance weight estimates given by...
Tài liệu Tracking and Kalman filtering made easy P16 pptx
... variables.
Specifically,
GðX
n
Þ¼
g
1
ðX
n
Þ
g
2
ðX
n
Þ
.
.
.
g
n
ðX
n
Þ
2
6
6
6
4
3
7
7
7
5
ð16:2-2Þ
357
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John Wiley & Sons, Inc.
ISBNs: 0-471-18407-1 (Hardback); 0-471-22419-7 (Electronic)
Hence (16.3-8) and (16.3-9) ... ¼
R
s
2
4
3
5
ð16:2-4Þ
The nonlinear equation relating R
s
, , and to x, y,...
Tài liệu Tracking and Kalman filtering made easy P17 ppt
... of
"
XðtÞ
367
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 1998 John Wiley & Sons, Inc.
ISBNs: 0-471-18407-1 (Hardback); 0-471-22419-7 (Electronic)
where M is defined by (16.2-10) and ... to
update the target trajectory estimate as done in Section 9.5 for the Bayes
and Kalman filters when the observation scheme and target dynamics model
are linea...
Tài liệu Tracking and Kalman filtering made easy P18 pptx
... need not be square and as a result UðtÞ need not have the
same dimension as XðtÞ. The solution to the above linear differential equation is
375
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright ... S
Ã
n;nÀ1
nonsingular.
382 KALMAN FILTER REVISITED
Kolmogorov [125] and Wiener [126] in 1941 and 1942, respectively. Next, the
Kalman filter [19, 20] was developed...
Tài liệu Tracking and Kalman filtering made easy P1 doc
... g
n
Þþy
n
g
n
ð1:2-12Þ
Figure 1.2-3 Filtering problem. Esti-
mate of x
Ã
n;n
based on measurement y
n
and prediction x
Ã
n;nÀ1
.
22
g–h AND g–h–k FILTERS
1
g–h AND g–h–k FILTERS
1.1 WHY TRACKING AND PREDICTION ARE ... proper
association of the echo from target 1 on the second scan with the echo from
3
Tracking and Kalman Filtering Made Easy. Eli Brookner
Copyright # 199...
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