a3x/gr-digitalhf
1
0
Fork 0
mirror of https://github.com/hb9fxq/gr-digitalhf synced 2024-11-05 05:55:53 +00:00
Code Issues Releases Wiki Activity

data-aided frequency offset estimation

Browse source
This commit is contained in:
cmayer 2018-10-27 10:05:19 +02:00
parent 0fb4b1336f
commit f73b7567d3
1 changed files with 19 additions and 12 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

31
python/physical_layer/STANAG_4285.py
View file

@ -39,19 +39,9 @@ class PhysicalLayer(object):
print('-------------------- get_doppler --------------------',self._counter)
doppler = 0
if self._counter == 0: ## preamble
corr = s*np.conj(self._preamble[0]['symb'])
self._preamble_phases.extend([np.angle(np.sum(corr))])
if len(self._preamble_phases) == 2 and False:
doppler = 2*(self._preamble_phases[1] - self._preamble_phases[0])/256
print('preamble_phases', self._preamble_phases, 'doppler', doppler)
self._preamble_phases = self._preamble_phases[1:]
else:
phases = np.unwrap(np.angle(corr))
doppler = 2*(np.median(phases[-20:]) - np.median(phases[:20]))/80
print('doppler', doppler,self._preamble_phases)
doppler = PhysicalLayer.data_aided_frequency_estimation(s, self._preamble[0]['symb'])
self._counter = (self._counter+1)&1
return [True, doppler]
return [True, 2*doppler]
@staticmethod
def get_preamble():
@ -94,3 +84,20 @@ class PhysicalLayer(object):
c['points'] = np.exp(2*np.pi*1j*np.array(range(n))/n)
c['symbols'] = gray_code
return c
@staticmethod
def data_aided_frequency_estimation(x,c):
"""Data-Aided Frequency Estimation for Burst Digital Transmission,
Umberto Mengali and M. Morelli, IEEE TRANSACTIONS ON COMMUNICATIONS,
VOL. 45, NO. 1, JANUARY 1997"""
z = x*np.conj(c) ## eq (2)
L0 = len(z)
N = L0//2
R = np.zeros(N, dtype=np.complex64)
for i in range(N):
R[i] = 1.0/(L0-i)*np.sum(z[i:]*np.conj(z[0:L0-i])) ## eq (3)
m = np.array(range(N), dtype=np.float)
w = 3*((L0-m)*(L0-m+1)-N*(L0-N))/(N*(4*N*N - 6*N*L0 + 3*L0*L0-1)) ## eq (9)
mod_2pi = lambda x : np.mod(x-np.pi, 2*np.pi) - np.pi
fd = np.sum(w[1:] * mod_2pi(np.diff(np.angle(R)))) ## eq (8)
return fd