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use the Theil-Sen estimator on each symbol

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Ahmet Inan 2021-09-03 01:19:36 +02:00
commit 7b45ee894d
1 changed files with 24 additions and 32 deletions
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56
decode.cc
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@ -9,8 +9,8 @@ Copyright 2021 Ahmet Inan <inan@aicodix.de>
#include <cassert>
#include <cmath>
namespace DSP { using std::abs; using std::min; using std::cos; using std::sin; }
#include "regression.hh"
#include "bip_buffer.hh"
#include "theil_sen.hh"
#include "xorshift.hh"
#include "trigger.hh"
#include "complex.hh"
@ -176,6 +176,7 @@ struct Decoder
static const int mod_max = 3;
static const int cons_max = 64800 / mod_min;
static const int cols_min = 256;
static const int cols_max = 512;
static const int rows_max = cons_max / cols_min;
static const int mls0_len = 127;
static const int mls0_off = - mls0_len + 1;
@ -191,6 +192,7 @@ struct Decoder
DSP::BlockDC<value, value> blockdc;
DSP::Hilbert<cmplx, filter_len> hilbert;
DSP::BipBuffer<cmplx, buffer_len> input_hist;
DSP::TheilSenEstimator<value, cols_max> tse;
SchmidlCox<value, cmplx, search_pos, symbol_len/2, guard_len> correlator;
CODE::CRC<uint16_t> crc0;
CODE::CRC<uint32_t> crc1;
@ -202,7 +204,7 @@ struct Decoder
code_type code[65536];
cmplx head[symbol_len], tail[symbol_len], cons[cons_max];
cmplx fdom[symbol_len], tdom[buffer_len];
value index[cons_max], phase[cons_max];
value index[cols_max], phase[cols_max];
value cfo_rad, sfo_rad;
const uint32_t *frozen_bits;
int code_order;
@ -467,40 +469,30 @@ struct Decoder
cons[cons_cols*j+i] = demod_or_erase(fdom[bin(i+code_off)], head[bin(i+code_off)]);
}
if (1) {
for (int i = 0; i < cons_cnt; ++i) {
code_type tmp[mod_max];
mod_hard(tmp, cons[i]);
index[i] = (i % cons_cols) + code_off;
phase[i] = arg(cons[i] * conj(mod_map(tmp)));
}
DSP::SimpleLinearRegression<value> dirty(index, phase, cons_cnt);
//std::cerr << "dirty slope = " << dirty.slope() << std::endl;
//std::cerr << "dirty yint = " << dirty.yint() << std::endl;
value avg_diff = 0;
for (int i = 0; i < cons_cnt; ++i)
avg_diff += dirty(index[i]) - phase[i];
avg_diff /= value(cons_cnt);
value var_diff = 0;
for (int i = 0; i < cons_cnt; ++i)
var_diff += (dirty(index[i]) - phase[i] - avg_diff) * (dirty(index[i]) - phase[i] - avg_diff);
value std_dev = std::sqrt(var_diff/(cons_cnt-1));
int count = 0;
for (int i = 0; i < cons_cnt; ++i) {
if (std::abs(dirty(index[i])-phase[i]) <= std_dev) {
index[count] = index[i];
phase[count] = phase[i];
++count;
value sum_slope = 0, sum_yint = 0;
for (int j = 0; j < cons_rows; ++j) {
for (int i = 0; i < cons_cols; ++i) {
code_type tmp[mod_max];
mod_hard(tmp, cons[cons_cols*j+i]);
index[i] = i + code_off;
phase[i] = arg(cons[cons_cols*j+i] * conj(mod_map(tmp)));
}
tse.compute(index, phase, cons_cols);
//std::cerr << "Theil-Sen slope = " << tse.slope() << std::endl;
//std::cerr << "Theil-Sen yint = " << tse.yint() << std::endl;
sum_slope += tse.slope();
sum_yint += tse.yint();
for (int i = 0; i < cons_cols; ++i)
cons[cons_cols*j+i] *= DSP::polar<value>(1, -tse(i+code_off));
}
DSP::SimpleLinearRegression<value> pruned(index, phase, count);
//std::cerr << "pruned slope = " << pruned.slope() << std::endl;
//std::cerr << "pruned yint = " << pruned.yint() << std::endl;
sfo_rad -= pruned.slope() * symbol_len / value(symbol_len+guard_len);
cfo_rad += pruned.yint() / (symbol_len+guard_len);
value avg_slope = sum_slope / cons_rows;
value avg_yint = sum_yint / cons_rows;
//for (int i = 0; i < cons_cnt; ++i)
// cons[i] *= DSP::polar<value>(1, -(avg_yint+avg_slope*((i%cons_cols)+code_off)));
sfo_rad -= avg_slope * symbol_len / value(symbol_len+guard_len);
cfo_rad += avg_yint / (symbol_len+guard_len);
std::cerr << "coarse sfo: " << 1000000 * sfo_rad / Const::TwoPi() << " ppm" << std::endl;
std::cerr << "finer cfo: " << cfo_rad * (rate / Const::TwoPi()) << " Hz " << std::endl;
for (int i = 0; i < cons_cnt; ++i)
cons[i] *= DSP::polar<value>(1, -pruned((i % cons_cols) + code_off));
}
value precision = 16;
if (1) {