a3x/gr-digitalhf
1
0
Fork 0
mirror of https://github.com/hb9fxq/gr-digitalhf synced 2024-12-21 23:09:59 +00:00
Code Issues Releases Wiki Activity

preamble doppler estimation for 110D mode added

Browse source
This commit is contained in:
cmayer 2019-05-20 15:03:44 +02:00
parent 1034b6045e
commit c3d2db12ac
1 changed files with 28 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

36
python/physical_layer/MIL_STD_188_110D.py
View file

@ -793,13 +793,13 @@ class PhysicalLayer(object):
if self._superframe_counter != 0:
self._state = 'FIXED'
return [self._fixed_s,MODE_BPSK,success,False]
else: ## TODO
else:
self._frame_counter = 0
if self._wid != MODE_WALSH:
self._scr_data.reset()
self._state = 'MP'
[mode,a] = self.get_next_data_frame(success)
return [a,mode,success,False]
return [a,mode,success,True]
else: ## WID0
self._frame_counter += 1
return [self._walsh_s,MODE_BPSK,success,True]
@ -808,10 +808,13 @@ class PhysicalLayer(object):
if self._wid != MODE_WALSH:
[mode,a] = self.get_next_data_frame(success)
self._frame_counter += (self._state == 'MP')
success = np.abs(np.real(np.mean(symbols[::2]))) > 0.7 if self._state == 'MP' else True
return [a,mode,success,success]
success = np.abs(np.real(np.mean(symbols[::2]))) > 0.5 if self._state == 'MP' else True
if success == False:
print('TEST: {} success={} {} {}'.format(self._state, success, symbols[::2], np.mean(symbols[::2])))
return [a,mode,success,True]
else: ## WID0
self._frame_counter += 1
## TODO: WALSH16 check for BW > 30kHz
m = len(symbols)//32
z = np.zeros(8*m, dtype=np.complex64)
for i,s in enumerate(symbols.reshape(m, 32)):
@ -831,9 +834,11 @@ class PhysicalLayer(object):
self._state = 'DATA'
a = np.zeros(self._known, common.SYMB_SCRAMBLE_DTYPE)
if (self._frame_counter % self._intl_frames) == self._intl_frames-1:
print('next_data_fram MP_shifted ', self._frame_counter, self._intl_frames)
a['symb'][:] = self._mp_shifted
a['scramble'][:] = self._mp_shifted
else:
print('next_data_frame MP_regular', self._frame_counter, self._intl_frames)
a['symb'][:] = self._mp
a['scramble'][:] = self._mp
return MODE_BPSK,a
@ -842,7 +847,7 @@ class PhysicalLayer(object):
a = np.zeros(self._unknown, common.SYMB_SCRAMBLE_DTYPE)
a['scramble'][:] = 1
self._scr_data.reset()
if self._wid_mode <= MODE_8PSK: ## not QAM
if self._data_mode <= MODE_8PSK: ## not QAM
for i in range(self._unknown):
a['scramble'][i] = np.exp(2j*np.pi*self._scr_data.next()/8)
else: ## QAM modes
@ -852,7 +857,22 @@ class PhysicalLayer(object):
def get_doppler(self, iq_samples):
"""quality check and doppler estimation for preamble"""
print('get_doppler', len(iq_samples))
success,doppler = True,0
sps = self._sps
wlen = self._wlen
_,zp = self.get_preamble_z()
cc = np.correlate(iq_samples, zp)
imax = np.argmax(np.abs(cc[0:wlen*sps]))
idx = np.arange(wlen*sps)
print('get_doppler ccmax: ', imax, np.abs(cc[imax]))
pks = [np.correlate(iq_samples[imax+i*wlen*sps+idx],
zp[i*wlen*sps+idx])[0]
for i in range((len(iq_samples)-imax) // (wlen*sps))]
print('get_doppler pks: ', pks, np.angle(pks))
doppler = common.freq_est(pks)/(wlen*sps)
print('get_doppler doppler: ', doppler)
## TODO
return success,doppler
@ -901,7 +921,7 @@ class PhysicalLayer(object):
success = np.all(b[0:3] == 0)
b = np.flip(b)
self._wid = wid = np.packbits(b[0:4])[0]>>4
self._intl_type = INTERLEAVERS[np.packbits(b[4:6])[0]>>6]
self._intl_type = 'L'# INTERLEAVERS[np.packbits(b[4:6])[0]>>6]
self._constraint_length = 'K=7' if b[6] == 0 else 'K=9'
self._data_mode = WID_MODE[self._wid]
print('WID:', self._wid, self._intl_type, self._constraint_length,self._data_mode)
@ -960,12 +980,12 @@ class PhysicalLayer(object):
def decode_soft_dec(self, soft_dec):
print('decode_soft_dec', len(soft_dec), soft_dec.dtype)
interleaver_is_full = False
if self._wid == MODE_WALSH: ## TODO
if self._wid == MODE_WALSH: ## TODO: WALSH16 decoding for BW > 30kHz
n = len(soft_dec) // 32
soft_bits = np.zeros(2*n, dtype=np.float32)
for i in range(n):
w = np.sum(soft_dec[32*i:32*(i+1)].reshape(4,8),0)
b = FROM_WALSH4[np.packbits(w[0:4]>0)[0]]
b = FROM_WALSH4[np.packbits(w[0:4]>0)[0]] ## TODO use 2nd half of WALSH bits
print('WALSH', i, w, b)
abs_soft_dec = np.mean(np.abs(w))
soft_bits[2*i] = abs_soft_dec*(2*(b>>1)-1)