Global Positioning System Reference
In-Depth Information
(
)
⎨
min
f
dd d
,
,
,
；
12
n
(22)
st
..
d
∈=
S
,
i
1,
,
n
⎩
i
i
( )
opt
( )
opt
2
( )
opt
and the final step is to find
n
optimal shape vectors
d
,
d
,…,
d
from UVS, which
1
n
correspond to the optimal local chip waveforms.
Note that usually at different processing stages, the optimization objects are difference. For
example, in acquisition, the optimization objects may be the maximum SNR or the widest
SCF main peak, while at tracking stage it may be the ability of multipath rejection, the greatest
slope or the widest linear range of the discriminator curve, or even some compromises
between them. In next two sections, we will give two examples of SC algorithm design
under the steps described above. The design process of an SC unambiguous acquisition
algorithm as well as an SC unambiguous tracking loop is described respectively.
5. GRASS technique
Under the analytic design framework described above, an unambiguous acquisition
technique named General Removing Ambiguity via Side-peak Suppression (GRASS)
technique is developed. This technique is suitable for generic sin-BOC(
kn
,
n
) signals and it is
convenient to implement. The detailed performance analysis of this technique can be found
in (Yao et al., 2010a). This section puts its emphasis on the design process of this technique.
5.1 Step 1 - SCF selection
Theoretically, when the number of local auxiliary signals is unlimited, SCF can be shaped
into any desired forms. However, from the view of engineering, the more local signals are
used, the more correlators are needed in a receiver which is directly related to the
complexity and power consumption. Moreover, the noncoherent combination of too much
correlator results may aggravate SNR deterioration. Therefore, in our design, the number of
local auxiliary signals should be as few as possible.
Since the signal acquisition is a process of searching pronounced energy peak in a 2-
dimentional space, the requirement to the shape of SCF in acquisition is relatively generous
compared to code tracking. A SCF having main peak without positive side peak is enough.
Therefore in GRASS technique only one local auxiliary SCS signal with a matched BOC
signal is employed to suppress the side peaks of BOC ACF in noncoherent mode. The SCF
used is as follow:
( )
( )
( )
2
2
R
Δ =Δ−
τ
R
τα τ
R
Δ
(23)
B
B/L
where
R
is the ACF of BOC signal,
B/
R
is the CCF between the received BOC signal and
the local SCS signal, and α is the weight coefficient. It can be seen that (23) is similar with
the SCF used in (Julien et al., 2007) in form. However, as shown later, GRASS technique is
not only suitable for BOC(
n
,
n
) signals but also for other BOC
(
)
kn n
signals.
,
5.2 Step 2 - CCF shape constraint
The objective is to keep the main peak of BOC ACF envelop while remove all the positive
side peaks (the negative side peaks do not interfere with the statistical test since only
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