29.01.2013 Views

Pseudo-Noise (PN) Ranging Systems - CCSDS

Pseudo-Noise (PN) Ranging Systems - CCSDS

Pseudo-Noise (PN) Ranging Systems - CCSDS

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

<strong>CCSDS</strong> INFORMATIONAL REPORT CONCERNING PSEUDO-NOISE RANGING SYSTEMS<br />

where:<br />

ρ<br />

TCOR<br />

Ec<br />

ik = ⋅ s<br />

T ∫<br />

c 0<br />

* T<br />

( t)<br />

⋅Ci<br />

( t − kTc<br />

) dt = EcTc<br />

⋅ ρik<br />

⋅<br />

T<br />

Ec<br />

– S(<br />

t)<br />

= ⋅ s(<br />

t)<br />

and s( t)<br />

= sk<br />

for ( k −1)<br />

TC<br />

< t ≤ kTC<br />

are the binary (±1) ranging-<br />

T<br />

c<br />

sequence waveform with chip values sk ∈ {+1, –1};<br />

– TC is the chip duration;<br />

– Tr = LTc is one sequence length;<br />

– ρ * ik are the normalized correlation coefficients (i.e., unit amplitude and correlation<br />

time equal to one sequence length Tr = LTc).<br />

As indicated in table 2-10, the normalized correlation coefficients are related to the in-phase<br />

(ξ) and out-of-phase (ψ ) fractional correlation coefficient as defined in 2.4.3<br />

Table 2-10: Normalized Correlation Coefficients (Unit amplitude and Correlation<br />

Time Equal to One Sequence Length Tr)<br />

ρ * 10=Lξ1 ρ * 11=Lψ1 ρ * 20=Lξ2<br />

ρ * 2k=Lψ2<br />

(k =1..6)<br />

ρ * 30=Lξ3<br />

ρ * 3k=Lψ3<br />

( k=1..10)<br />

T2B 633306 - 633306 247020 - 41404 250404 - 24900<br />

T4B 947566 - 947566 61904 - 10368 61904 - 6160<br />

ρ * 40=Lξ4<br />

ρ * 4k=Lψ4<br />

k = 1..14<br />

ρ * 50=Lξ5<br />

ρ * 5k Lψ5<br />

(k =1..18)<br />

ρ * 6=Lξ6<br />

ρ * 6k =Lψ6<br />

(k = 1..22)<br />

T2B 251332 - 17852 251604 - 14056 251940 - 11388<br />

T4B 61904 - 4400 61904 - 3456 61904 - 2800<br />

For code C1, assuming that k = 0 is the true phase, the probability of correct decision is<br />

simply 8 :<br />

1 ⎛ * ⎜<br />

T<br />

P(<br />

C1)<br />

= P(<br />

χ 10 > 0)<br />

= P(<br />

ρ10<br />

+ η10<br />

> 0)<br />

= 1−<br />

erfc<br />

⎜<br />

ρ10<br />

⋅<br />

2 ⎝ T<br />

being η10 Gaussian with zero mean and variance<br />

N0TCOR<br />

2<br />

8 Noting that C1 is antipodal and that both k = 0 and k = 1 are equally probable.<br />

9 The erfc(x) function is defined in section 2.4.3.2.<br />

<strong>CCSDS</strong> 414.0-G-1 Page 2-39 March 2010<br />

9 .<br />

COR<br />

r<br />

COR<br />

r<br />

⋅<br />

EcT<br />

N T<br />

0<br />

c<br />

COR<br />

⎞<br />

⎟<br />

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!