# Full text: Proceedings International Workshop on Mobile Mapping Technology

```7B-5-2
conventional photogrammetric flight.
Introduction of OTF technique will enhance the
practicability -of GPS photogrammetry, all
limitations caused by GPS will be released The
aircraft can fly in the same manner as in
conventional photogrammetric flight. The
airborne GPS receiver can be turned on while
the aircraft is entering the photogrammetric area
to cut down unnecessary records.
In general, the various OTF techniques offer a
similar strategy: firstly, an ambiguity search
space is constructed, then searching for correct
integer ambiguities begins by applying certain
validation and rejection criteria. The search
process is stopped and the ambiguities are fixed
whenever certain assurance criteria are fulfilled.
This paper proposes a new OTF method based
on double difference Pseudorange/carrier phase
Kalman filter Firstly, search space is constructed
based on real value ambiguities and the associate
covariance matrix. Then, optimized Cholesky
decomposition searching process is executed. If
a certain ambiguity combination passes Ratio
test and OVT test, it will be the right ones. This
paper also presents the practice of this method in
processing the airborne GPS kinematic data of
the Beijing GPS photogrammetric project.
2 OTF METHOD BASED ON DOUBLE
DIFFERENCE PSEUDORANGE, CARRIER
PHASE KALMAN FILTER
2.1 Double difference pseudorange, carrier
phase Kalman fdter
The first step of this OTF method is to determine
real value ambiguities and associate covariance
matrix. This can be done by processing
pseudorange and carrier phase observations
using least square adjustment or kalman filter.
Kalman filter was adopted in this paper
considering pseudorange and carrier phase are
independent, the mathematic model of double
difference pseudorange/carrier phase Kalman
filter can be written as:
X t =<1>*-A- 1 +r t . l w tA w t ~N{0,Q„) (l)
^k, P ~H k ^ p X k +v k ' P v k 'p
(2)
■^k,<t> = + v k,t v k ,<i> "
- N(0,R k j)
(3)
\yhere:
X k is the system state vector,
X k = (dx,dy,dz,x,y,z,dn 0 ,dn l ,--- ,dn n )‘
O* is the state transition matrix;
H kp ,H k 0 are observation matrix for double
difference pseudorange and carrier phase
respectively;
T k is system driven noise matrix;
R k p.R-k <i> are covariance matrix for double
difference pseudorange and carrier phase
measurements respectively;
Q k is the covariance matrix for system noise.
Equation (1) is the dynamic model for aircraft,
which is a polynomial model of second or third
order; Equation (2), (3) express the linearized
observation models for double difference
pseudorange and carrier phase measurements
respectively. The sequential estimation formula
of Kalman filter is:
~ ^ k,k~\X k-\,k-1
(4)
Vi =
^ k^k-i^k-hk-fò k,k-\
■r^Qk-i r;_,
t (5)
^k,k,p '
= ('\Li + H T kp R- k l
Hk P r l
(6)
K k, p -
-- P k ,k, P H T kp R kp - x
(7)
x k , k ,p
— ¿*,-1 x k P [Z kp
-H kp x k ' k _
,1(8)
II
(Pkl P +H T t*R;}H
k *r l
(9)
--i\^H T k ,R k ; x
(10)
Xu* =
X k,k,p + x [Z kl/l
~ h k( p x kkp
](H)
Equation (4)~(11) process pseudorange, carrier
```

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