a3x
/
gr-digitalhf
mirror of https://github.com/hb9fxq/gr-digitalhf
1
0
Fork 0
Code Issues Releases Wiki Activity

bug fixed 

* the big switch statement in lib/adaptive_dfe_impl.cc needs to be improved
This commit is contained in:
cmayer 2018-11-03 21:21:05 +01:00
parent 64096f2d97
commit 1cc5e64256
5 changed files with 5137 additions and 123 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
examples/.gitignore vendored Normal file
View File

@ -0,0 +1,2 @@
top_block.py
*.wav

2385
examples/test_188-110A.grc Normal file
View File

File diff suppressed because it is too large Load Diff

2385
examples/test_188-110C.grc Normal file
View File

File diff suppressed because it is too large Load Diff

256
lib/adaptive_dfe_impl.cc
View File

@ -159,68 +159,93 @@ adaptive_dfe_impl::general_work(int noutput_items,
gr_complex const* in = (gr_complex const *)input_items[0];
gr_complex *out = (gr_complex *)output_items[0];
int nout = 0;
int i = 0;
int nout = 0; // counter for produced output items
int i = 0; // counter for consumed input items
for (; i<ninput_items[0] && nout < noutput_items;) {
assert(nout < noutput_items);
if (_state == WAIT_FOR_PREAMBLE) {
insert_sample(in[i++]);
uint64_t offset = 0;
float phase_est = 0;
if (get_correlation_tag(i, offset, phase_est)) {
_state = INITIAL_DOPPLER_ESTIMATE;
_sample_counter = 0;
_symbol_counter = 0;
// _symbols.clear();
// _scramble.clear();
_descrambled_symbols.clear();
// _hist_sample_index = 0;
_hist_symbol_index = 0;
_ignore_filter_updates = 0;
_saved_samples.clear();
std::fill_n(_hist_symbols, 2*_nW, gr_complex(0));
std::fill_n(_taps_samples, _nB+_nF+1, gr_complex(0));
std::fill_n(_taps_symbols, _nW, gr_complex(0));
_samples.clear();
_phase = -phase_est;
_taps_samples[_nB+1] = 0.01;
_taps_symbols[0] = 1;
GILLock gil_lock;
try {
update_frame_information(_physicalLayer.attr("get_frame")());
} catch (boost::python::error_already_set const&) {
PyErr_Print();
}
}
} // WAIT_FOR_PREAMBLE
if (_state == INITIAL_DOPPLER_ESTIMATE) {
// buffer samples and replay them later once the initial doppler estimate is there
if (_samples.size() == _sps * _symbols.size()) {
GILLock gil_lock;
try {
std::vector<gr_complex> const empty_vec;
// initial doppler estimate
if (!update_doppler_information(_physicalLayer.attr("get_doppler")
(complex_vector_to_ndarray(empty_vec),
complex_vector_to_ndarray(_samples)))) {
_state = WAIT_FOR_PREAMBLE;
continue;
switch (_state) {
case WAIT_FOR_PREAMBLE: {
insert_sample(in[i++]);
uint64_t offset = 0;
float phase_est = 0;
if (get_correlation_tag(i, offset, phase_est)) {
GR_LOG_DEBUG(d_logger, "next state > INITIAL_DOPPLER_ESTIMATE");
_state = INITIAL_DOPPLER_ESTIMATE;
_sample_counter = 0;
_symbol_counter = 0;
// _symbols.clear();
// _scramble.clear();
_descrambled_symbols.clear();
// _hist_sample_index = 0;
_hist_symbol_index = 0;
_ignore_filter_updates = 0;
_saved_samples.clear();
std::fill_n(_hist_symbols, 2*_nW, gr_complex(0));
std::fill_n(_taps_samples, _nB+_nF+1, gr_complex(0));
std::fill_n(_taps_symbols, _nW, gr_complex(0));
_samples.clear();
_phase = -phase_est;
_taps_samples[_nB+1] = 0.01;
_taps_symbols[0] = 1;
GILLock gil_lock;
try {
update_frame_information(_physicalLayer.attr("get_frame")());
} catch (boost::python::error_already_set const&) {
PyErr_Print();
}
} catch (boost::python::error_already_set const&) {
PyErr_Print();
}
// (1) correct all samples in the circular buffer with the inital doppler estimate
for (int j=_nB+1; j<_nB+_nF+1; ++j) {
assert(_hist_sample_index+j < 2*(_nB+_nF+1));
_hist_samples[_hist_sample_index+j] *= gr_expj(-_phase);
update_local_oscillator();
break;
} // WAIT_FOR_PREAMBLE
case INITIAL_DOPPLER_ESTIMATE: {
_samples.push_back(in[i++]);
// buffer samples and replay them later once the initial doppler estimate is there
if (_samples.size() == _sps * _symbols.size()) {
GILLock gil_lock;
try {
std::vector<gr_complex> const empty_vec;
// initial doppler estimate
if (!update_doppler_information(_physicalLayer.attr("get_doppler")
(complex_vector_to_ndarray(empty_vec),
complex_vector_to_ndarray(_samples)))) {
GR_LOG_DEBUG(d_logger, "next state > WAIT_FOR_PREAMBLE");
_state = WAIT_FOR_PREAMBLE;
break;
}
} catch (boost::python::error_already_set const&) {
PyErr_Print();
}
// (1) correct all samples in the circular buffer with the inital doppler estimate
for (int j=_nB+1; j<_nB+_nF+1; ++j) {
assert(_hist_sample_index+j < 2*(_nB+_nF+1));
_hist_samples[_hist_sample_index+j] *= gr_expj(-_phase);
update_local_oscillator();
}
// (2) insert all buffered samples and run the adaptive filter for them
// instead of pop_front() we first reverse _samples and then insert back() + pop_back()
// O(N) instead of O(N^2)
std::reverse(_samples.begin(), _samples.end());
while (!_samples.empty() && nout < noutput_items) {
insert_sample(_samples.back());
_sample_counter += 1;
_samples.pop_back();
if ((_sample_counter%_sps) == 0)
out[nout++] = filter();
}
if (_samples.empty()) {
GR_LOG_DEBUG(d_logger,"next state > DO_FILTER");
_state = DO_FILTER;
break;
} else {
GR_LOG_DEBUG(d_logger, "next state > INITIAL_DOPPLER_ESTIMATE_CONTINUE");
_state = INITIAL_DOPPLER_ESTIMATE_CONTINUE;
break;
}
}
// (2) insert all buffered samples and run the adaptive filter for them
// instead of pop_front() we first reverse _samples and then insert back() + pop_back()
// O(N) instead of O(N^2)
std::reverse(_samples.begin(), _samples.end());
} // INITIAL_DOPPLER_ESTIMATE_CONTINUE
case INITIAL_DOPPLER_ESTIMATE_CONTINUE: {
GR_LOG_DEBUG(d_logger, "INITIAL_DOPPLER_ESTIMATE_CONTINUE");
while (!_samples.empty() && nout < noutput_items) {
insert_sample(_samples.back());
_sample_counter += 1;
@ -229,79 +254,65 @@ adaptive_dfe_impl::general_work(int noutput_items,
out[nout++] = filter();
}
if (_samples.empty()) {
GR_LOG_DEBUG(d_logger, "next state > DO_FILTER");
_state = DO_FILTER;
} else {
GR_LOG_DEBUG(d_logger, "next state > INITIAL_DOPPLER_ESTIMATE_CONTINUE");
_state = INITIAL_DOPPLER_ESTIMATE_CONTINUE;
}
continue;
}
_samples.push_back(in[i++]);
} // INITIAL_DOPPLER_ESTIMATE_CONTINUE
break;
} // INITIAL_DOPPLER_ESTIMATE_CONTINUE
if (_state == INITIAL_DOPPLER_ESTIMATE_CONTINUE) {
GR_LOG_DEBUG(d_logger, "INITIAL_DOPPLER_ESTIMATE_CONTINUE");
while (!_samples.empty() && nout < noutput_items) {
insert_sample(_samples.back());
_sample_counter += 1;
_samples.pop_back();
if ((_sample_counter%_sps) == 0)
out[nout++] = filter();
}
if (_samples.empty()) {
_state = DO_FILTER;
} else {
_state = INITIAL_DOPPLER_ESTIMATE_CONTINUE;
}
continue;
} // INITIAL_DOPPLER_ESTIMATE_CONTINUE
if (_state == DO_FILTER) {
if ((_sample_counter%_sps) == 0) {
if (_symbol_counter == _symbols.size()) { // frame is ready
_symbol_counter = 0;
GILLock gil_lock;
try {
// update doppler estimate
update_doppler_information(_physicalLayer.attr("get_doppler")
(complex_vector_to_ndarray(_descrambled_symbols),
complex_vector_to_ndarray(_samples)));
// publish soft decisions
if (!_vec_soft_decisions.empty()) {
unsigned int const bits_per_symbol = _constellations[_constellation_index]->bits_per_symbol();
_msg_metadata = pmt::dict_add(_msg_metadata, pmt::mp("bits_per_symbol"), pmt::from_long(bits_per_symbol));
message_port_pub(_msg_port_name,
pmt::cons(_msg_metadata,
pmt::init_f32vector(_vec_soft_decisions.size(), _vec_soft_decisions)));
_vec_soft_decisions.clear();
case DO_FILTER: {
if ((_sample_counter%_sps) == 0) {
if (_symbol_counter == _symbols.size()) { // frame is ready
_symbol_counter = 0;
GILLock gil_lock;
try {
// update doppler estimate
if (!update_doppler_information(_physicalLayer.attr("get_doppler")
(complex_vector_to_ndarray(_descrambled_symbols),
complex_vector_to_ndarray(_samples)))) {
GR_LOG_DEBUG(d_logger, "next state > WAIT_FOR_PREAMBLE");
_state = WAIT_FOR_PREAMBLE;
break;
}
// publish soft decisions
if (!_vec_soft_decisions.empty()) {
unsigned int const bits_per_symbol = _constellations[_constellation_index]->bits_per_symbol();
_msg_metadata = pmt::dict_add(_msg_metadata, pmt::mp("bits_per_symbol"), pmt::from_long(bits_per_symbol));
message_port_pub(_msg_port_name,
pmt::cons(_msg_metadata,
pmt::init_f32vector(_vec_soft_decisions.size(), _vec_soft_decisions)));
_vec_soft_decisions.clear();
}
_samples.clear();
// get information about the following frame
update_frame_information(_physicalLayer.attr("get_frame")());
} catch (boost::python::error_already_set const&) {
PyErr_Print();
}
_samples.clear();
// get information about the following frame
update_frame_information(_physicalLayer.attr("get_frame")());
} catch (boost::python::error_already_set const&) {
PyErr_Print();
} // frame is ready
if (_ignore_filter_updates == 0) {
out[nout++] = filter();
if (_symbol_counter+1 == _symbols.size())
recenter_filter_taps();
} else {
_ignore_filter_updates -= 1;
}
} // frame is ready
if (_ignore_filter_updates == 0) {
out[nout++] = filter();
if (_symbol_counter+1 == _symbols.size())
recenter_filter_taps();
} else {
_ignore_filter_updates -= 1;
} // (_sample_counter%_sps) == 0
if (_need_samples) {
_samples.push_back(_hist_samples[_hist_sample_index+_nB+1]);
}
} // (_sample_counter%_sps) == 0
if (_need_samples) {
_samples.push_back(_hist_samples[_hist_sample_index+_nB+1]);
}
if (_saved_samples.empty()) {
insert_sample(in[i++]);
} else {
insert_sample(_saved_samples.back());
_saved_samples.pop_back();
}
_sample_counter += 1;
} // DO_FILTER
if (_saved_samples.empty()) {
insert_sample(in[i++]);
} else {
insert_sample(_saved_samples.back());
_saved_samples.pop_back();
}
_sample_counter += 1;
} // DO_FILTER
} // switch _state
} // next input sample
consume(0, i);
@ -522,7 +533,6 @@ bool adaptive_dfe_impl::update_doppler_information(boost::python::object obj)
assert(n==2);
bool const do_continue = boost::python::extract<bool>(obj[0]);
if (!do_continue) {
_state = WAIT_FOR_PREAMBLE;
_phase = 0;
_df = 0;
std::fill_n(_hist_samples, 2*(_nB+_nF+1), gr_complex(0));

232
python/physical_layer/MIL_STD_188_110C.py Normal file
View File

@ -0,0 +1,232 @@
## -*- python -*-
from __future__ import print_function
import numpy as np
def n_psk(n,x):
return np.complex64(np.exp(2j*np.pi*x/n))
## ---- constellations -----------------------------------------------------------
CONST_DTYPE=np.dtype([('points', np.complex64),
('symbols', np.uint8)])
BPSK=np.array(zip(np.exp(2j*np.pi*np.arange(2)/2), range(2)), CONST_DTYPE)
QPSK=np.array(zip(np.exp(2j*np.pi*np.arange(4)/4), [0,1,3,2]), CONST_DTYPE)
PSK8=np.array(zip(np.exp(2j*np.pi*np.arange(8)/8), [0,1,3,2,7,6,4,5]), CONST_DTYPE)
QAM16=np.array(
zip([+0.866025+0.500000j, 0.500000+0.866025j, 1.000000+0.000000j, 0.258819+0.258819j,
-0.500000+0.866025j, 0.000000+1.000000j, -0.866025+0.500000j, -0.258819+0.258819j,
+0.500000-0.866025j, 0.000000-1.000000j, 0.866025-0.500000j, 0.258819-0.258819j,
-0.866025-0.500000j, -0.500000-0.866025j, -1.000000+0.000000j, -0.258819-0.258819j],
range(16)), CONST_DTYPE)
QAM32=np.array(
zip([+0.866380+0.499386j, 0.984849+0.173415j, 0.499386+0.866380j, 0.173415+0.984849j,
+0.520246+0.520246j, 0.520246+0.173415j, 0.173415+0.520246j, 0.173415+0.173415j,
-0.866380+0.499386j, -0.984849+0.173415j, -0.499386+0.866380j, -0.173415+0.984849j,
-0.520246+0.520246j, -0.520246+0.173415j, -0.173415+0.520246j, -0.173415+0.173415j,
+0.866380-0.499386j, 0.984849-0.173415j, 0.499386-0.866380j, 0.173415-0.984849j,
+0.520246-0.520246j, 0.520246-0.173415j, 0.173415-0.520246j, 0.173415-0.173415j,
-0.866380-0.499386j, -0.984849-0.173415j, -0.499386-0.866380j, -0.173415-0.984849j,
-0.520246-0.520246j, -0.520246-0.173415j, -0.173415-0.520246j, -0.173415-0.173415j],
range(32)), CONST_DTYPE)
QAM64=np.array(
zip([+1.000000+0.000000j, 0.822878+0.568218j, 0.821137+0.152996j, 0.932897+0.360142j,
+0.000000-1.000000j, 0.822878-0.568218j, 0.821137-0.152996j, 0.932897-0.360142j,
+0.568218+0.822878j, 0.588429+0.588429j, 0.588429+0.117686j, 0.588429+0.353057j,
+0.568218-0.822878j, 0.588429-0.588429j, 0.588429-0.117686j, 0.588429-0.353057j,
+0.152996+0.821137j, 0.117686+0.588429j, 0.117686+0.117686j, 0.117686+0.353057j,
+0.152996-0.821137j, 0.117686-0.588429j, 0.117686-0.117686j, 0.117686-0.353057j,
+0.360142+0.932897j, 0.353057+0.588429j, 0.353057+0.117686j, 0.353057+0.353057j,
+0.360142-0.932897j, 0.353057-0.588429j, 0.353057-0.117686j, 0.353057-0.353057j,
+0.000000+1.000000j, -0.822878+0.568218j, -0.821137+0.152996j, -0.932897+0.360142j,
-1.000000+0.000000j, -0.822878-0.568218j, -0.821137-0.152996j, -0.932897-0.360142j,
-0.568218+0.822878j, -0.588429+0.588429j, -0.588429+0.117686j, -0.588429+0.353057j,
-0.568218-0.822878j, -0.588429-0.588429j, -0.588429-0.117686j, -0.588429-0.353057j,
-0.152996+0.821137j, -0.117686+0.588429j, -0.117686+0.117686j, -0.117686+0.353057j,
-0.152996-0.821137j, -0.117686-0.588429j, -0.117686-0.117686j, -0.117686-0.353057j,
-0.360142+0.932897j, -0.353057+0.588429j, -0.353057+0.117686j, -0.353057+0.353057j,
-0.360142-0.932897j, -0.353057-0.588429j, -0.353057-0.117686j, -0.353057-0.353057j],
range(64)), CONST_DTYPE)
## ---- constellation indices ---------------------------------------------------
MODE_BPSK = 0
MODE_QPSK = 1
MODE_8PSK = 2
MODE_16QAM = 3
MODE_32QAM = 4
MODE_64QAM = 5
## ---- data scrambler -----------------------------------------------------------
class ScrambleData(object):
"""data scrambling sequence generator"""
def __init__(self):
self.reset()
def reset(self):
self._state = 1
def next(self, num_bits):
r = self._state & ((1<<num_bits)-1)
for i in range(num_bits):
self._advance()
return r
def _advance(self):
lsb = self._state&1
self._state = (self._state>>1)&511
if lsb:
self._state ^= 0x10B
return self._state
## ---- preamble definitions ---------------------------------------------------
## 184 = 8*23
PREAMBLE=np.array(
[1,5,1,3,6,1,3,1,1,6,3,7,7,3,5,4,3,6,6,4,5,4,0,
2,2,2,6,0,7,5,7,4,0,7,5,7,1,6,1,0,5,2,2,6,2,3,
6,0,0,5,1,4,2,2,2,3,4,0,6,2,7,4,3,3,7,2,0,2,6,
4,4,1,7,6,2,0,6,2,3,6,7,4,3,6,1,3,7,4,6,5,7,2,
0,1,1,1,4,4,0,0,5,7,7,4,7,3,5,4,1,6,5,6,6,4,6,
3,4,3,0,7,1,3,4,7,0,1,4,3,3,3,5,1,1,1,4,6,1,0,
6,0,1,3,1,4,1,7,7,6,3,0,0,7,2,7,2,0,2,6,1,1,1,
2,7,7,5,3,3,6,0,5,3,3,1,0,7,1,1,0,3,0,4,0,7,3],
dtype=np.uint8)
## 103 = 31 + 1 + 3*13 + 1 + 31
REINSERTED_PREAMBLE=np.array(
[0,0,0,0,0,2,4,6,0,4,0,4,0,6,4,2,0,0,0,0,0,2,4,6,0,4,0,4,0,6,4, ## MP+
2,
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D0
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D1
0,4,0,4,0,0,4,4,0,0,0,0,0, # + D2
6,
4,4,4,4,4,6,0,2,4,0,4,0,4,2,0,6,4,4,4,4,4,6,0,2,4,0,4,0,4,2,0], ## MP-
dtype=np.uint8)
## ---- physcal layer class -----------------------------------------------------
class PhysicalLayer(object):
"""Physical layer description for MIL-STD-188-110 Appendix D = STANAG 4539"""
def __init__(self, sps):
"""intialization"""
self._sps = sps
self._frame_counter = -1
self._constellations = [BPSK, QPSK, PSK8, QAM16, QAM32, QAM64]
self._preamble = self.get_preamble()
self._scr_data = ScrambleData()
def get_constellations(self):
return self._constellations
def get_frame(self):
"""returns a tuple describing the frame:
[0] ... known+unknown symbols and scrambling
[1] ... modulation type after descrambling
[2] ... a boolean indicating whethere or not raw IQ samples needed
[3] ... a boolean indicating if the soft decision for the unknown
symbols are saved"""
print('-------------------- get_frame --------------------',
self._frame_counter)
## --- preamble frame ----
if self._frame_counter == -1:
return [self._preamble,MODE_BPSK,True,False]
## ----- data frame ------
if self._frame_counter == 0:
self.a = self.make_reinserted_preamble()
return [self.a, MODE_QPSK,False,True]
def get_doppler(self, symbols, iq_samples):
"""returns a tuple
[0] ... quality flag
[1] ... doppler estimate (rad/symbol) if available"""
print('-------------------- get_doppler --------------------',
self._frame_counter,len(symbols),len(iq_samples))
#if len(symbols)!=0:
# print('symb=', symbols)
success = False
doppler = 0
if self._frame_counter == -1: ## -- preamble ----
success,doppler = self.get_doppler_from_preamble(symbols, iq_samples)
if len(symbols) != 0:
for s in symbols:
print(s)
self._frame_counter = 0
else: ## ------------------------ data frame ----
if len(symbols) != 0:
for s in symbols:
print(s)
success = False
self._frame_counter = -1
return success,doppler
def get_doppler_from_preamble(self, symbols, iq_samples):
"""quality check and doppler estimation for preamble"""
success = True
doppler = 0
shift=9
if len(iq_samples) != 0:
zp = np.conj(self.get_preamble_z(self._sps)[shift*self._sps:])
cc = np.array([np.sum(iq_samples[i:i+23*self._sps] *
zp[0:23*self._sps])
for i in range(23*3*self._sps)])
acc = np.abs(cc)
for i in range(0,len(cc),23*self._sps):
print('i=%3d: '%i,end='')
for j in range(23*self._sps):
print('%3.0f ' % acc[i+j], end='')
print()
imax = np.argmax(np.abs(cc[0:3*23*self._sps]))
print(imax)
pks = np.array([np.sum(iq_samples[(imax+23*i*self._sps):
(imax+23*i*self._sps+23*self._sps)] *
zp[(23*i*self._sps):
(23*i*self._sps+23*self._sps)])
for i in range(1,5)])
print('doppler apks', np.abs(pks))
print('doppler ppks', np.angle(pks), np.diff(np.unwrap(np.angle(pks)))/23)
doppler = np.mean(np.diff(np.unwrap(np.angle(pks))))/23
success = True
print('success=', success, 'doppler=', doppler)
return success,doppler
def make_reinserted_preamble(self):
a=np.zeros(len(REINSERTED_PREAMBLE), dtype=[('symb', np.complex64),
('scramble', np.complex64)])
a['symb'] = n_psk(8, REINSERTED_PREAMBLE)
a['scramble'] = n_psk(8, REINSERTED_PREAMBLE)
a['symb'][32:32+3*13] = 0 ## D0,D1,D2
return a
@staticmethod
def get_preamble():
"""preamble symbols + scrambler"""
a=np.zeros(len(PREAMBLE), dtype=[('symb', np.complex64),
('scramble', np.complex64)])
a['symb'] = n_psk(8, PREAMBLE)
a['scramble'] = n_psk(8, PREAMBLE)
##a['symb'][-30:] = 0
return a
@staticmethod
def get_preamble_z(sps):
"""preamble symbols for preamble correlation"""
a = PhysicalLayer.get_preamble()
return np.array([z for z in a['symb'] for i in range(sps)])
if __name__ == '__main__':
print(PREAMBLE)
z = n_psk(8,PREAMBLE)
cc = [np.sum(z[0:23]*np.conj(z[23*i:23*i+23])) for i in range(6)]
print(np.abs(cc))
print(np.angle(cc)/np.pi*4)
print(all(z==PhysicalLayer.get_preamble()['symb']))
print(len(PhysicalLayer.get_preamble()['symb']))
s = ScrambleData()
print([s.next(1) for _ in range(511)])
print([s.next(1) for _ in range(511)] ==
[s.next(1) for _ in range(511)])
print(QAM64)
print(QAM32)
print(QAM16)
print(PSK8)
print(QPSK)
print(BPSK)