Working on DSSS demodulator sim
This commit is contained in:
jaseg
parent
d9b26d16c0
commit
4b419bd1ad
14 changed files with 94312 additions and 62 deletions
|
|
@ -6,7 +6,7 @@ LIBSODIUM_DIR ?= libsodium
|
|||
TINYAES_DIR ?= tinyaes
|
||||
MUSL_DIR ?= musl
|
||||
|
||||
C_SOURCES := src/main.c src/mspdebug_wrapper.c src/spi_flash.c src/freq_meas.c
|
||||
C_SOURCES := src/main.c src/mspdebug_wrapper.c src/spi_flash.c src/freq_meas.c src/dsss_demod.c
|
||||
C_SOURCES += $(MSPDEBUG_DIR)/drivers/jtaglib.c
|
||||
|
||||
CMSIS_SOURCES += $(CMSIS_DIR)/CMSIS/DSP/Source/TransformFunctions/arm_rfft_fast_init_f32.c
|
||||
|
|
@ -41,13 +41,17 @@ OPENCM3_LIB := opencm3_stm32f4
|
|||
PREFIX ?= arm-none-eabi-
|
||||
|
||||
|
||||
GOLD_CODE_NBITS ?= 5
|
||||
FMEAS_ADC_SAMPLING_RATE ?= 1000
|
||||
FMEAS_ADC_MAX ?= 4096
|
||||
FMEAS_FFT_LEN ?= 256
|
||||
FMEAS_FFT_WINDOW ?= gaussian
|
||||
FMEAS_FFT_WINDOW_SIGMA ?= 16.0
|
||||
|
||||
DSSS_GOLD_CODE_NBITS ?= 5
|
||||
DSSS_DECIMATION ?= 10
|
||||
DSSS_THESHOLD_FACTOR ?= 4.0f
|
||||
DSSS_WAVELET_WIDTH ?= 7.3
|
||||
DSSS_WAVELET_LUT_SIZE ?= 69
|
||||
|
||||
CC := $(PREFIX)gcc
|
||||
CXX := $(PREFIX)g++
|
||||
|
|
@ -78,6 +82,7 @@ MUSL_DIR_ABS := $(abspath $(MUSL_DIR))
|
|||
COMMON_CFLAGS += -I$(OPENCM3_DIR_ABS)/include -Imspdebug/util -Imspdebug/drivers -Ilevmarq
|
||||
COMMON_CFLAGS += -I$(CMSIS_DIR_ABS)/CMSIS/DSP/Include -I$(CMSIS_DIR_ABS)/CMSIS/Core/Include
|
||||
CFLAGS += -I$(abspath musl_include_shims)
|
||||
COMMON_CFLAGS += -I$(BUILDDIR) -Isrc
|
||||
|
||||
COMMON_CFLAGS += -Os -std=gnu11 -g -DSTM32F4
|
||||
CFLAGS += -mthumb -mcpu=cortex-m4 -mfloat-abi=hard -mfpu=fpv4-sp-d16
|
||||
|
|
@ -85,13 +90,18 @@ CFLAGS += -mthumb -mcpu=cortex-m4 -mfloat-abi=hard -mfpu=fpv4-sp-d16
|
|||
# Note: libopencm3 requires some standard libc definitions from stdint.h and stdbool.h, so we don't pass -nostdinc here.
|
||||
CFLAGS += -nostdlib -ffreestanding
|
||||
CFLAGS += -fno-common -ffunction-sections -fdata-sections
|
||||
COMMON_CFLAGS += -DGOLD_CODE_NBITS=$(GOLD_CODE_NBITS) -DFMEAS_FFT_LEN=$(FMEAS_FFT_LEN) -DFMEAS_ADC_MAX=$(FMEAS_ADC_MAX)
|
||||
COMMON_CFLAGS += -DDSSS_GOLD_CODE_NBITS=$(DSSS_GOLD_CODE_NBITS) -DFMEAS_FFT_LEN=$(FMEAS_FFT_LEN) -DFMEAS_ADC_MAX=$(FMEAS_ADC_MAX)
|
||||
COMMON_CFLAGS += -DFMEAS_ADC_SAMPLING_RATE=$(FMEAS_ADC_SAMPLING_RATE)
|
||||
COMMON_CFLAGS += -DFMEAS_FFT_WINDOW_SIGMA=$(FMEAS_FFT_WINDOW_SIGMA)
|
||||
COMMON_CFLAGS += -DDSSS_DECIMATION=$(DSSS_DECIMATION)
|
||||
COMMON_CFLAGS += -DDSSS_THESHOLD_FACTOR=$(DSSS_THESHOLD_FACTOR)
|
||||
COMMON_CFLAGS += -DDSSS_WAVELET_WIDTH=$(DSSS_WAVELET_WIDTH)
|
||||
COMMON_CFLAGS += -DDSSS_WAVELET_LUT_SIZE=$(DSSS_WAVELET_LUT_SIZE)
|
||||
|
||||
# for musl
|
||||
CFLAGS += -Dhidden=
|
||||
|
||||
SIM_CFLAGS += -Isrc -lm -DSIMULATION
|
||||
SIM_CFLAGS += -lm -DSIMULATION
|
||||
|
||||
INT_CFLAGS += -Wall -Wextra -Wpedantic -Wshadow -Wimplicit-function-declaration -Wundef
|
||||
INT_CFLAGS += -Wredundant-decls -Wmissing-prototypes -Wstrict-prototypes
|
||||
|
|
@ -112,7 +122,24 @@ LDFLAGS += -L$(OPENCM3_DIR_ABS)/lib -l$(OPENCM3_LIB) $(shell $(CC) -print-libg
|
|||
|
||||
all: $(BUILDDIR)/$(BINARY)
|
||||
|
||||
tests: $(BUILDDIR)/tools/freq_meas_test
|
||||
src/dsss_demod.c: $(BUILDDIR)/generated/dsss_gold_code.h
|
||||
|
||||
$(BUILDDIR)/generated/dsss_gold_code.h: $(BUILDDIR)/generated/gold_code_$(DSSS_GOLD_CODE_NBITS).h
|
||||
ln -srf $< $@
|
||||
|
||||
.PRECIOUS: $(BUILDDIR)/generated/gold_code_%.c
|
||||
$(BUILDDIR)/generated/gold_code_%.c $(BUILDDIR)/generated/gold_code_%.h: | $(BUILDDIR)/generated
|
||||
$(PYTHON3) tools/gold_code_header_gen.py -v dsss_gold_code_table -$(patsubst .%,%,$(suffix $@)) $* > $@
|
||||
|
||||
.PRECIOUS: $(BUILDDIR)/generated/fmeas_fft_window.c
|
||||
$(BUILDDIR)/generated/fmeas_fft_window.c: | $(BUILDDIR)/generated
|
||||
$(PYTHON3) tools/fft_window_header_gen.py -v fmeas_fft_window_table $(FMEAS_FFT_WINDOW) $(FMEAS_FFT_LEN) $(FMEAS_FFT_WINDOW_SIGMA) > $@
|
||||
|
||||
.PRECIOUS: $(BUILDDIR)/generated/dsss_cwt_wavelet.c
|
||||
$(BUILDDIR)/generated/dsss_cwt_wavelet.c: | $(BUILDDIR)/generated
|
||||
$(PYTHON3) tools/cwt_wavelet_header_gen.py -v dsss_cwt_wavelet_table $(DSSS_WAVELET_LUT_SIZE) $(DSSS_WAVELET_WIDTH) > $@
|
||||
|
||||
$(BUILDDIR)/generated: ; mkdir -p $@
|
||||
|
||||
OBJS := $(addprefix $(BUILDDIR)/,$(C_SOURCES:.c=.o) $(CXX_SOURCES:.cpp=.o))
|
||||
|
||||
|
|
@ -121,16 +148,23 @@ ALL_OBJS += $(OPENCM3_DIR)/lib/lib$(OPENCM3_LIB).a
|
|||
ALL_OBJS += $(BUILDDIR)/libsodium/src/libsodium/.libs/libsodium.a
|
||||
ALL_OBJS += $(BUILDDIR)/tinyaes/aes.o
|
||||
ALL_OBJS += $(BUILDDIR)/levmarq/levmarq.o
|
||||
ALL_OBJS += $(BUILDDIR)/generated/gold_code_$(GOLD_CODE_NBITS).o
|
||||
ALL_OBJS += $(BUILDDIR)/generated/gold_code_$(DSSS_GOLD_CODE_NBITS).o
|
||||
ALL_OBJS += $(BUILDDIR)/generated/fmeas_fft_window.o
|
||||
ALL_OBJS += $(BUILDDIR)/generated/dsss_cwt_wavelet.o
|
||||
|
||||
$(BUILDDIR)/$(BINARY): $(ALL_OBJS)
|
||||
$(LD) -T$(LDSCRIPT) $(COMMON_LDFLAGS) $(LDFLAGS) -o $@ -Wl,-Map=$(BUILDDIR)/src/$*.map $^
|
||||
|
||||
tools: $(BUILDDIR)/tools/freq_meas_test
|
||||
$(BUILDDIR)/tools/freq_meas_test: tools/freq_meas_test.c src/freq_meas.c levmarq/levmarq.c $(BUILDDIR)/generated/fmeas_fft_window.c $(CMSIS_SOURCES)
|
||||
mkdir -p $(@D)
|
||||
$(HOST_CC) $(COMMON_CFLAGS) $(SIM_CFLAGS) -o $@ $^
|
||||
|
||||
tools: $(BUILDDIR)/tools/dsss_demod_test
|
||||
$(BUILDDIR)/tools/dsss_demod_test: tools/dsss_demod_test.c src/dsss_demod.c $(BUILDDIR)/generated/dsss_cwt_wavelet.c $(BUILDDIR)/generated/gold_code_$(DSSS_GOLD_CODE_NBITS).c
|
||||
mkdir -p $(@D)
|
||||
$(HOST_CC) $(COMMON_CFLAGS) $(SIM_CFLAGS) -o $@ $^
|
||||
|
||||
$(BUILDDIR)/src/%.o: src/%.c
|
||||
mkdir -p $(@D)
|
||||
$(CC) $(COMMON_CFLAGS) $(CFLAGS) $(INT_CFLAGS) -o $@ -c $<
|
||||
|
|
@ -158,14 +192,6 @@ $(BUILDDIR)/tinyaes/aes.o:
|
|||
mkdir -p $(@D)
|
||||
make -C $(@D) -f $(TINYAES_DIR_ABS)/Makefile VPATH=$(TINYAES_DIR_ABS) CFLAGS="$(COMMON_CFLAGS) $(CFLAGS) -c" CC=$(CC) LD=$(LD) AR=$(AR) aes.o
|
||||
|
||||
$(BUILDDIR)/generated/gold_code_%.c:
|
||||
mkdir -p $(@D)
|
||||
$(PYTHON3) tools/gold_code_header_gen.py $* > $@
|
||||
|
||||
$(BUILDDIR)/generated/fmeas_fft_window.c:
|
||||
mkdir -p $(@D)
|
||||
$(PYTHON3) tools/fft_window_header_gen.py -v fmeas_fft_window_table $(FMEAS_FFT_WINDOW) $(FMEAS_FFT_LEN) $(FMEAS_FFT_WINDOW_SIGMA) > $@
|
||||
|
||||
$(BUILDDIR)/musl/lib/libc.a:
|
||||
mkdir -p $(BUILDDIR)/musl
|
||||
cd $(BUILDDIR)/musl && CFLAGS="$(SIM_CFLAGS) $(COMMON_CFLAGS)" CC=$(CC) LD=$(LD) AR=$(AR) $(MUSL_DIR_ABS)/configure && $(MAKE) TARGET=arm-linux-musleabihf GCC_CONFIG="-mcpu=cortex-m4 -mfloat-abi=soft" -j $(shell nproc)
|
||||
|
|
@ -174,13 +200,13 @@ build/ldpc_decoder_test.so: src/ldpc_decoder.c
|
|||
gcc -fPIC -shared -Wall -Wextra -Wpedantic -std=gnu11 -O0 -g -o $@ $^
|
||||
|
||||
clean:
|
||||
-rm -r $(BUILDDIR)/src
|
||||
-rm -r $(BUILDDIR)/generated
|
||||
-rm $(BUILDDIR)/$(BINARY)
|
||||
-rm $(BUILDDIR)/tools/freq_meas_test
|
||||
rm -rf $(BUILDDIR)/src
|
||||
rm -rf $(BUILDDIR)/generated
|
||||
rm -f $(BUILDDIR)/$(BINARY)
|
||||
rm -f $(BUILDDIR)/tools/freq_meas_test
|
||||
|
||||
mrproper: clean
|
||||
-rm -r build
|
||||
rm -rf build
|
||||
-$(MAKE) -C $(OPENCM3_DIR) clean
|
||||
|
||||
.PHONY: clean mrproper
|
||||
|
|
|
|||
180
controller/fw/src/dsss_demod.c
Normal file
180
controller/fw/src/dsss_demod.c
Normal file
|
|
@ -0,0 +1,180 @@
|
|||
|
||||
#include <unistd.h>
|
||||
#include <stdbool.h>
|
||||
#include <math.h>
|
||||
|
||||
#include <arm_math.h>
|
||||
|
||||
#include "freq_meas.h"
|
||||
#include "sr_global.h"
|
||||
#include "dsss_demod.h"
|
||||
#include "simulation.h"
|
||||
|
||||
#include "generated/dsss_gold_code.h"
|
||||
|
||||
/* Generated CWT wavelet LUT */
|
||||
extern const float * const dsss_cwt_wavelet_table;
|
||||
|
||||
struct iir_biquad cwt_filter_bq[3] = {
|
||||
{.a = {-1.993939440f, 0.993949280f}, .b = {0.2459934300683e-5, 0.4919868601367e-5, 0.2459934300683e-5}},
|
||||
{.a = {-1.995557124f, 0.995566972f}, .b = {0.2461930046414e-5, 0.4923860092828e-5, 0.2461930046414e-5}},
|
||||
{.a = {-1.998365254f, 0.998375115f}, .b = {0.2465394452097e-5, 0.4930788904195e-5, 0.2465394452097e-5}},
|
||||
};
|
||||
|
||||
float gold_correlate_step(const size_t ncode, const float a[DSSS_CORRELATION_LENGTH], size_t offx);
|
||||
float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx);
|
||||
float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]);
|
||||
float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_state *const restrict st);
|
||||
|
||||
#ifdef SIMULATION
|
||||
void dsss_demod_step(struct dsss_demod_state *st, float new_value, size_t sim_pos) {
|
||||
#else /* SIMULATION */
|
||||
void dsss_demod_step(struct dsss_demod_state *st, float new_value) {
|
||||
#endif /* SIMULATION */
|
||||
|
||||
//#define DEBUG_PRINT(...) ((void)0)
|
||||
//#define DEBUG_PRINTN(...) ((void)0)
|
||||
bool debug = sim_pos % DSSS_CORRELATION_LENGTH == 0;
|
||||
if (debug) DEBUG_PRINT("Iteration %zd", sim_pos);
|
||||
//const float peak_group_threshold = 0.05 * DSSS_CORRELATION_LENGTH;
|
||||
//const float hole_patching_threshold = 0.01 * DSSS_CORRELATION_LENGTH;
|
||||
|
||||
st->signal[st->signal_wpos] = new_value;
|
||||
st->signal_wpos = (st->signal_wpos + 1) % ARRAY_LENGTH(st->signal);
|
||||
if (debug) DEBUG_PRINT(" signal: %f", new_value);
|
||||
|
||||
/* use new, incremented wpos for gold_correlate_step as first element of old data in ring buffer */
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
st->correlation[i][st->correlation_wpos] = gold_correlate_step(i, st->signal, st->correlation_wpos);
|
||||
/* debug */
|
||||
/*
|
||||
DEBUG_PRINTN(" correlation: [");
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
DEBUG_PRINTN("%f, ", st->correlation[i][st->correlation_wpos]);
|
||||
DEBUG_PRINTN("]\n");
|
||||
*/
|
||||
/* end */
|
||||
st->correlation_wpos = (st->correlation_wpos + 1) % ARRAY_LENGTH(st->correlation[0]);
|
||||
|
||||
float cwt[DSSS_GOLD_CODE_COUNT];
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
cwt[i] = cwt_convolve_step(st->correlation[i], st->correlation_wpos);
|
||||
/* debug */
|
||||
if (debug) DEBUG_PRINTN(" cwt: [");
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
if (debug) DEBUG_PRINTN("%f, ", cwt[i]);
|
||||
if (debug) DEBUG_PRINTN("]\n");
|
||||
/* end */
|
||||
|
||||
float avg[DSSS_GOLD_CODE_COUNT];
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
avg[i] = run_iir(fabs(cwt[i]), ARRAY_LENGTH(cwt_filter_bq), cwt_filter_bq, st->cwt_filter[i].st);
|
||||
/* debug */
|
||||
/*
|
||||
DEBUG_PRINTN(" avg: [");
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
|
||||
DEBUG_PRINTN("%f, ", avg[i]);
|
||||
DEBUG_PRINTN("]\n");
|
||||
*/
|
||||
/* end */
|
||||
|
||||
float max_val = st->group.max;
|
||||
int max_ch = st->group.max_ch;
|
||||
int max_idx = st->group.max_idx + 1;
|
||||
bool found = false;
|
||||
if (debug) DEBUG_PRINTN(" rel: [");
|
||||
for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++) {
|
||||
float val = cwt[i] / avg[i];
|
||||
if (debug) DEBUG_PRINTN("%f, ", val);
|
||||
|
||||
if (fabs(val) > DSSS_THESHOLD_FACTOR)
|
||||
found = true;
|
||||
|
||||
if (fabs(val) > fabs(max_val)) {
|
||||
max_val = val;
|
||||
max_ch = i;
|
||||
max_idx = st->group.len;
|
||||
}
|
||||
}
|
||||
if (debug) DEBUG_PRINTN("]\n");
|
||||
|
||||
/* debug */
|
||||
if (debug) DEBUG_PRINT(" found=%d len=%d idx=%d ch=%d max=%f",
|
||||
found, st->group.len, st->group.max_idx, st->group.max_ch, st->group.max);
|
||||
/* end */
|
||||
|
||||
if (found) {
|
||||
/* Continue ongoing group */
|
||||
st->group.len++;
|
||||
st->group.max = max_val;
|
||||
st->group.max_ch = max_ch;
|
||||
st->group.max_idx = max_idx;
|
||||
return;
|
||||
}
|
||||
|
||||
if (st->group.len == 0)
|
||||
/* We're between groups */
|
||||
return;
|
||||
|
||||
/* A group ended. Process result. */
|
||||
DEBUG_PRINT("GROUP FOUND: %8d len=%3d max=%f ch=%d offx=%d",
|
||||
sim_pos, st->group.len, st->group.max, st->group.max_ch, st->group.max_idx);
|
||||
|
||||
/* reset grouping state */
|
||||
st->group.len = 0;
|
||||
st->group.max_idx = 0;
|
||||
st->group.max_ch = 0;
|
||||
st->group.max = 0.0f;
|
||||
}
|
||||
|
||||
float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]) {
|
||||
float intermediate = x;
|
||||
for (int i=0; i<(order+1)/2; i++)
|
||||
intermediate = run_biquad(intermediate, &q[i], &st[i]);
|
||||
return intermediate;
|
||||
}
|
||||
|
||||
float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_state *const restrict st) {
|
||||
/* direct form 2, see https://en.wikipedia.org/wiki/Digital_biquad_filter */
|
||||
float intermediate = x + st->reg[0] * -q->a[0] + st->reg[1] * -q->a[1];
|
||||
float out = intermediate * q->b[0] + st->reg[0] * q->b[1] + st->reg[1] * q->b[2];
|
||||
st->reg[1] = st->reg[0];
|
||||
st->reg[0] = intermediate;
|
||||
return out;
|
||||
}
|
||||
|
||||
float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx) {
|
||||
float sum = 0.0f;
|
||||
for (ssize_t j=0; j<DSSS_WAVELET_LUT_SIZE; j++) {
|
||||
/* Our wavelet is symmetric so convolution and correlation are identical. Use correlation here for ease of
|
||||
* implementation */
|
||||
sum += v[(offx + j) % DSSS_WAVELET_LUT_SIZE] * dsss_cwt_wavelet_table[j];
|
||||
//DEBUG_PRINT(" j=%d v=%f w=%f", j, v[(offx + j) % DSSS_WAVELET_LUT_SIZE], dsss_cwt_wavelet_table[j]);
|
||||
}
|
||||
return sum / DSSS_WAVELET_LUT_SIZE;
|
||||
}
|
||||
|
||||
/* Compute last element of correlation for input [a] and hard-coded gold sequences.
|
||||
*
|
||||
* This is intened to be used once for each new incoming sample in [a]. It expects [a] to be of length
|
||||
* [dsss_correlation_length] and produces the one sample where both the reference sequence and the input fully overlap.
|
||||
* This is equivalent to "valid" mode in numpy's terminology[0].
|
||||
*
|
||||
* [0] https://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html
|
||||
*/
|
||||
float gold_correlate_step(const size_t ncode, const float a[DSSS_CORRELATION_LENGTH], size_t offx) {
|
||||
float acc_outer = 0.0f;
|
||||
uint8_t table_byte = 0;
|
||||
for (size_t i=0, pos=0; i<DSSS_GOLD_CODE_LENGTH; i++, pos += DSSS_DECIMATION) {
|
||||
float acc_inner = 0.0f;
|
||||
for (size_t j=0; j<DSSS_DECIMATION; j++) {
|
||||
if ((pos&7) == 0)
|
||||
table_byte = dsss_gold_code_table[ncode][pos>>3]; /* Fetch sequence table item */
|
||||
int bv = table_byte & (1<<(pos&7)); /* Extract bit */
|
||||
bv = !!bv*2 - 1; /* Map 0, 1 -> -1, 1 */
|
||||
acc_inner += a[(offx + i + j) % DSSS_CORRELATION_LENGTH] * bv; /* Multiply item */
|
||||
}
|
||||
acc_outer += acc_inner;
|
||||
}
|
||||
return acc_outer / DSSS_CORRELATION_LENGTH;
|
||||
}
|
||||
44
controller/fw/src/dsss_demod.h
Normal file
44
controller/fw/src/dsss_demod.h
Normal file
|
|
@ -0,0 +1,44 @@
|
|||
#ifndef __DSSS_DEMOD_H__
|
||||
#define __DSSS_DEMOD_H__
|
||||
|
||||
#define DSSS_GOLD_CODE_LENGTH ((1<<DSSS_GOLD_CODE_NBITS) - 1)
|
||||
#define DSSS_GOLD_CODE_COUNT ((1<<DSSS_GOLD_CODE_NBITS) + 1)
|
||||
#define DSSS_CORRELATION_LENGTH (DSSS_GOLD_CODE_LENGTH * DSSS_DECIMATION)
|
||||
|
||||
struct iir_biquad {
|
||||
float a[2];
|
||||
float b[3];
|
||||
};
|
||||
|
||||
struct iir_biquad_state {
|
||||
float reg[2];
|
||||
};
|
||||
|
||||
struct cwt_iir_filter_state {
|
||||
struct iir_biquad_state st[3];
|
||||
};
|
||||
|
||||
struct dsss_demod_state {
|
||||
float signal[DSSS_CORRELATION_LENGTH];
|
||||
size_t signal_wpos;
|
||||
|
||||
float correlation[DSSS_GOLD_CODE_COUNT][DSSS_WAVELET_LUT_SIZE];
|
||||
size_t correlation_wpos;
|
||||
|
||||
struct cwt_iir_filter_state cwt_filter[DSSS_GOLD_CODE_COUNT];
|
||||
|
||||
struct {
|
||||
int len; /* length of group in samples */
|
||||
float max; /* signed value of largest peak in group on any channel */
|
||||
int max_idx; /* position of above peak counted from start of group */
|
||||
int max_ch; /* channel (gold sequence index) of above peak */
|
||||
} group;
|
||||
};
|
||||
|
||||
#ifdef SIMULATION
|
||||
void dsss_demod_step(struct dsss_demod_state *st, float new_value, size_t sim_pos);
|
||||
#else /* SIMULATION */
|
||||
void dsss_demod_step(struct dsss_demod_state *st, float new_value);
|
||||
#endif /* SIMULATION */
|
||||
|
||||
#endif /* __DSSS_DEMOD_H__ */
|
||||
|
|
@ -2,6 +2,7 @@
|
|||
#define __SR_GLOBAL_H__
|
||||
|
||||
#define UNUSED(x) ((void) x)
|
||||
#define ARRAY_LENGTH(x) (sizeof(x) / sizeof(x[0]))
|
||||
|
||||
static inline uint16_t htole(uint16_t val) { return val; }
|
||||
|
||||
|
|
|
|||
29
controller/fw/tools/cwt_wavelet_header_gen.py
Normal file
29
controller/fw/tools/cwt_wavelet_header_gen.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import textwrap
|
||||
|
||||
import scipy.signal as sig
|
||||
import numpy as np
|
||||
|
||||
if __name__ == '__main__':
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('n', type=int, help='Window size')
|
||||
parser.add_argument('w', type=float, help='Wavelet width')
|
||||
parser.add_argument('-v', '--variable', default='cwt_ricker_table', help='Name for alias variable pointing to generated wavelet LUT')
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f'/* CWT Ricker wavelet LUT for {args.n} sample window of width {args.w}. */')
|
||||
varname = f'cwt_ricker_{args.n}_window_{str(args.w).replace(".", "F")}'
|
||||
print(f'const float {varname}[{args.n}] = {{')
|
||||
|
||||
win = sig.ricker(args.n, args.w)
|
||||
par = ' '.join(f'{f:>015.8g}f,' for f in win)
|
||||
print(textwrap.fill(par,
|
||||
initial_indent=' '*4, subsequent_indent=' '*4,
|
||||
width=120,
|
||||
replace_whitespace=False, drop_whitespace=False))
|
||||
print('};')
|
||||
print()
|
||||
print(f'const float * const {args.variable} __attribute__((weak)) = {varname};')
|
||||
|
||||
84
controller/fw/tools/dsss_demod_test.c
Normal file
84
controller/fw/tools/dsss_demod_test.c
Normal file
|
|
@ -0,0 +1,84 @@
|
|||
|
||||
#include <stdint.h>
|
||||
#include <math.h>
|
||||
#include <unistd.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
#include <errno.h>
|
||||
#include <stdlib.h>
|
||||
#include <sys/stat.h>
|
||||
#include <sys/types.h>
|
||||
#include <sys/fcntl.h>
|
||||
|
||||
#include "dsss_demod.h"
|
||||
|
||||
void print_usage() {
|
||||
fprintf(stderr, "Usage: dsss_demod_test [test_data.bin]\n");
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
if (argc != 2) {
|
||||
fprintf(stderr, "Error: Invalid arguments.\n");
|
||||
print_usage();
|
||||
return 1;
|
||||
}
|
||||
|
||||
int fd = open(argv[1], O_RDONLY);
|
||||
struct stat st;
|
||||
if (fstat(fd, &st)) {
|
||||
fprintf(stderr, "Error querying test data file size: %s\n", strerror(errno));
|
||||
return 2;
|
||||
}
|
||||
|
||||
if (st.st_size < 0 || st.st_size > 1000000) {
|
||||
fprintf(stderr, "Error reading test data: too much test data (size=%zd)\n", st.st_size);
|
||||
return 2;
|
||||
}
|
||||
|
||||
if (st.st_size % sizeof(float) != 0) {
|
||||
fprintf(stderr, "Error reading test data: file size is not divisible by %zd (size=%zd)\n", sizeof(float), st.st_size);
|
||||
return 2;
|
||||
}
|
||||
|
||||
char *buf = malloc(st.st_size);
|
||||
if (!buf) {
|
||||
fprintf(stderr, "Error allocating memory");
|
||||
return 2;
|
||||
}
|
||||
|
||||
fprintf(stderr, "Reading %zd samples test data...", st.st_size/sizeof(float));
|
||||
size_t nread = 0;
|
||||
while (nread < st.st_size) {
|
||||
ssize_t rc = read(fd, buf, st.st_size - nread);
|
||||
|
||||
if (rc == -EINTR || rc == -EAGAIN)
|
||||
continue;
|
||||
|
||||
if (rc < 0) {
|
||||
fprintf(stderr, "\nError reading test data: %s\n", strerror(errno));
|
||||
return 2;
|
||||
}
|
||||
|
||||
if (rc == 0) {
|
||||
fprintf(stderr, "\nError reading test data: Unexpected end of file\n");
|
||||
return 2;
|
||||
}
|
||||
|
||||
nread += rc;
|
||||
}
|
||||
fprintf(stderr, " done.\n");
|
||||
|
||||
const size_t n_samples = st.st_size / sizeof(float);
|
||||
float *buf_f = (float *)buf;
|
||||
|
||||
fprintf(stderr, "Starting simulation.\n");
|
||||
|
||||
struct dsss_demod_state demod;
|
||||
memset(&demod, 0, sizeof(demod));
|
||||
for (size_t i=0; i<n_samples; i++) {
|
||||
//fprintf(stderr, "Iteration %zd/%zd\n", i, n_samples);
|
||||
dsss_demod_step(&demod, buf_f[i], i);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
39
controller/fw/tools/dsss_demod_test_runner.py
Normal file
39
controller/fw/tools/dsss_demod_test_runner.py
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
from os import path
|
||||
import subprocess
|
||||
import json
|
||||
|
||||
import numpy as np
|
||||
np.set_printoptions(linewidth=240)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(metavar='test_data_directory', dest='dir', help='Directory with test data .bin files')
|
||||
default_binary = path.abspath(path.join(path.dirname(__file__), '../build/tools/dsss_demod_test'))
|
||||
parser.add_argument(metavar='test_binary', dest='binary', nargs='?', default=default_binary)
|
||||
parser.add_argument('-d', '--dump', help='Write raw measurements to JSON file')
|
||||
args = parser.parse_args()
|
||||
|
||||
bin_files = [ path.join(args.dir, d) for d in os.listdir(args.dir) if d.lower().endswith('.bin') ]
|
||||
|
||||
savedata = {}
|
||||
for p in bin_files:
|
||||
output = subprocess.check_output([args.binary, p], stderr=subprocess.DEVNULL)
|
||||
measurements = np.array([ float(value) for _offset, value in [ line.split() for line in output.splitlines() ] ])
|
||||
savedata[p] = list(measurements)
|
||||
|
||||
# Cut off first and last sample for mean and RMS calculations as these show boundary effects.
|
||||
measurements = measurements[1:-1]
|
||||
mean = np.mean(measurements)
|
||||
rms = np.sqrt(np.mean(np.square(measurements - mean)))
|
||||
|
||||
print(f'{path.basename(p):<60}: mean={mean:<8.4f}Hz rms={rms*1000:.3f}mHz')
|
||||
|
||||
if args.dump:
|
||||
with open(args.dump, 'w') as f:
|
||||
json.dump(savedata, f)
|
||||
|
||||
|
|
@ -70,7 +70,7 @@ int main(int argc, char **argv) {
|
|||
}
|
||||
fprintf(stderr, " done.\n");
|
||||
|
||||
size_t n_samples = st.st_size / sizeof(float);
|
||||
const size_t n_samples = st.st_size / sizeof(float);
|
||||
float *buf_f = (float *)buf;
|
||||
|
||||
int16_t *sim_adc_buf = calloc(sizeof(int16_t), n_samples);
|
||||
|
|
|
|||
|
|
@ -1,6 +1,9 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import sys
|
||||
import math
|
||||
import textwrap
|
||||
import contextlib
|
||||
|
||||
import numpy as np
|
||||
import scipy.signal as sig
|
||||
|
|
@ -24,22 +27,50 @@ def gold(n):
|
|||
(seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)
|
||||
return gen_gold(seq0, seq1)
|
||||
|
||||
@contextlib.contextmanager
|
||||
def print_include_guards(macro_name):
|
||||
print(f'#ifndef {macro_name}')
|
||||
print(f'#define {macro_name}')
|
||||
yield
|
||||
print(f'#endif /* {macro_name} */')
|
||||
|
||||
if __name__ == '__main__':
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser = argparse.ArgumentParser(add_help=False)
|
||||
parser.add_argument('n', type=int, choices=preferred_pairs, help='bit width of shift register. Generate 2**n + 1 sequences of length 2**n - 1.')
|
||||
parser.add_argument('-v', '--variable', default='gold_code_table', help='Name for weak alias of generated table')
|
||||
parser.add_argument('-h', '--header', action='store_true', help='Generate header file')
|
||||
parser.add_argument('-c', '--source', action='store_true', help='Generate table source file')
|
||||
args = parser.parse_args()
|
||||
|
||||
print('#include <unistd.h>')
|
||||
print()
|
||||
print(f'/* {args.n} bit gold sequences: {2**args.n+1} sequences of length {2**args.n-1} bit.')
|
||||
print(f' *')
|
||||
print(f' * Each code is packed left-aligned into {2**args.n // 8} bytes in big-endian byte order.')
|
||||
print(f' */')
|
||||
print(f'const uint8_t gold_code_{args.n}bit[] = {{')
|
||||
for i, code in enumerate(gold(args.n)):
|
||||
par = ' '.join(f'0x{d:02x},' for d in np.packbits(code)) + f' /* {i: 3d} "{"".join(str(x) for x in code)}" */'
|
||||
print(textwrap.fill(par, initial_indent=' '*4, subsequent_indent=' '*4, width=120))
|
||||
print('};')
|
||||
print()
|
||||
print(f'const uint8_t * const gold_code_table __attribute__((weak)) = gold_code_{args.n}bit;')
|
||||
if not args.header != args.source:
|
||||
print('Exactly one of --header and --source must be given.', file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
nbytes = math.ceil((2**args.n-1)/8)
|
||||
|
||||
if args.source:
|
||||
print('/* THIS IS A GENERATED FILE. DO NOT EDIT! */')
|
||||
print('#include <unistd.h>')
|
||||
print('#include <stdint.h>')
|
||||
print()
|
||||
print(f'/* {args.n} bit gold sequences: {2**args.n+1} sequences of length {2**args.n-1} bit.')
|
||||
print(f' *')
|
||||
print(f' * Each code is packed left-aligned into {nbytes} bytes in big-endian byte order.')
|
||||
print(f' */')
|
||||
print(f'const uint8_t gold_code_{args.n}bit[{2**args.n+1}][{nbytes}] = {{')
|
||||
for i, code in enumerate(gold(args.n)):
|
||||
par = '{' + ' '.join(f'0x{d:02x},' for d in np.packbits(code)) + f'}}, /* {i: 3d} "{"".join(str(x) for x in code)}" */'
|
||||
print(textwrap.fill(par, initial_indent=' '*4, subsequent_indent=' '*4, width=120))
|
||||
print('};')
|
||||
print()
|
||||
print(f'const uint8_t * const {args.variable} __attribute__((weak)) = (uint8_t *const)gold_code_{args.n}bit;')
|
||||
print(f'const size_t {args.variable}_nbits __attribute__((weak)) = {args.n};')
|
||||
else:
|
||||
print('/* THIS IS A GENERATED FILE. DO NOT EDIT! */')
|
||||
with print_include_guards(f'__GOLD_CODE_GENERATED_HEADER_{args.n}__'):
|
||||
print(f'extern const uint8_t gold_code_{args.n}bit[{2**args.n+1}][{nbytes}];')
|
||||
|
||||
with print_include_guards(f'__GOLD_CODE_GENERATED_HEADER_GLOBAL_SYM_{args.variable.upper()}__'):
|
||||
print(f'extern const uint8_t {args.variable}[{2**args.n+1}][{nbytes}];')
|
||||
print(f'extern const size_t {args.variable}_nbits;')
|
||||
|
|
|
|||
|
|
@ -137,6 +137,25 @@
|
|||
" return test_data, signal + noise"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_duration = 300\n",
|
||||
"test_nbits = 5\n",
|
||||
"test_signal_amplitude=2.0e-3\n",
|
||||
"test_decimation=10\n",
|
||||
"\n",
|
||||
"for test_signal_amplitude in [2.0e-3, 20e-3, 200e-3, 2]:\n",
|
||||
" test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**test_nbits), test_duration)\n",
|
||||
" signal = np.repeat(modulate(test_data, test_nbits) * 2.0 - 1, test_decimation) * test_signal_amplitude\n",
|
||||
" with open(f'dsss_test_signals/dsss_test_noiseless_{test_signal_amplitude*1000:.0f}mHz.bin', 'wb') as f:\n",
|
||||
" for e in signal:\n",
|
||||
" f.write(struct.pack('<f', e))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
|
|
@ -159,7 +178,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c5fba7c7cec24f09a4f23f7c7d87eb90",
|
||||
"model_id": "373401133cfe408aa15738e48c58dfaa",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -184,6 +203,129 @@
|
|||
"noprint = lambda *args, **kwargs: None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-44-38927d86fcc7>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "2015d1f2951340d49397c6c289096b01",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Text(0.5, 1.0, 'Ricker wavelet, w=69 a=7.3')"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"w = 69\n",
|
||||
"a = 7.3\n",
|
||||
"ax.plot(range(-w//2+1, w//2+1), sig.ricker(w, a))\n",
|
||||
"ax.grid()\n",
|
||||
"ax.axvline(0, color='orange')\n",
|
||||
"ax.set_title(f'Ricker wavelet, w={w} a={a}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-56-8e404350b85f>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "3cd0c25b914b41df835005ef6fb51b34",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"r = list(range(60, 120))\n",
|
||||
"ax.plot(r, [sum(sig.ricker(w, a)) for w in r])\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.grid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-67-2097444ed7d2>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, ax = plt.subplots()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "c5d1587fa0c944378aed31941ab66ff6",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"sw = 256\n",
|
||||
"w = sig.ricker(sw, a)\n",
|
||||
"r = list(range(1, sw//2 - 10))\n",
|
||||
"d = [-sum(w[:i]) - sum(w[-i:]) for i in r]\n",
|
||||
"ax.plot([sw-2*x for x in r], d)\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"ax.grid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
|
|
@ -279,7 +421,7 @@
|
|||
" chain_candidates = []\n",
|
||||
" for chain_score, chain in candidates:\n",
|
||||
" pos, ampl, _idx = chain[-1]\n",
|
||||
" score_fun = lambda pos, npos, npol: pol_score_factor*abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
|
||||
" score_fun = lambda pos, npos, npol: pol_score_eebfactor*abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
|
||||
" next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
|
||||
"\n",
|
||||
" print(f' candidates for {pos}, {ampl}:')\n",
|
||||
|
|
@ -366,13 +508,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5106e62521e246a0b0766cce3f9e556f",
|
||||
"model_id": "672fae23dc99473a967c46b4b90d09d7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -386,10 +528,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7fc200d359d0>]"
|
||||
"[<matplotlib.lines.Line2D at 0x7fc38cb743d0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -444,7 +586,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -454,13 +596,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "15e67925b1bf47b5bbde289b353ed201",
|
||||
"model_id": "985b1935796343d19c27e0b0ae31363c",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -474,7 +616,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "6432dbccc7db43d7a227384bca772716",
|
||||
"model_id": "418a9dd281534ffa8e0aa5f250424127",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -491,12 +633,12 @@
|
|||
"text": [
|
||||
"nbits=5\n",
|
||||
"nbits=6\n",
|
||||
"signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
|
||||
"signal_amplitude=0.00015: ser=0.98958 ±0.00737, br=5.85938\n",
|
||||
"signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
|
||||
"signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
|
||||
"signal_amplitude=0.00032: ser=0.97396 ±0.00737, br=14.64844\n",
|
||||
"signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
|
||||
"signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
|
||||
"signal_amplitude=0.00068: ser=0.94792 ±0.01949, br=29.29688\n",
|
||||
"signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
|
||||
"signal_amplitude=0.00100: ser=0.78125 ±0.02552, br=123.04688\n",
|
||||
"signal_amplitude=0.00147: ser=0.28646 ±0.03683, br=401.36719\n",
|
||||
"signal_amplitude=0.00215: ser=0.06250 ±0.03375, br=527.34375\n",
|
||||
|
|
@ -505,8 +647,8 @@
|
|||
"signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
|
||||
"signal_amplitude=0.00022: ser=0.98438 ±0.00000, br=5.27344\n",
|
||||
"signal_amplitude=0.00032: ser=0.98438 ±0.00000, br=5.27344\n",
|
||||
"signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
|
||||
"signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
|
||||
"signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
|
||||
"signal_amplitude=0.00100: ser=0.15104 ±0.05156, br=286.52344\n",
|
||||
"signal_amplitude=0.00147: ser=0.01562 ±0.00000, br=332.22656\n",
|
||||
"signal_amplitude=0.00215: ser=0.01562 ±0.00000, br=332.22656\n",
|
||||
|
|
@ -519,10 +661,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x7fc23a223550>"
|
||||
"<matplotlib.legend.Legend at 0x7fc382e9b7f0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -595,13 +737,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "bda9f8070b0142928936a7752f318e97",
|
||||
"model_id": "8b9593f48de94ec399fd8e61dc11d1de",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -615,10 +757,10 @@
|
|||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x7fc20046b730>"
|
||||
"<matplotlib.legend.Legend at 0x7fc3c4c54b20>"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -651,13 +793,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "33b1f777dda149eda3a2f031087372c9",
|
||||
"model_id": "15dc2e1a7cdd460e8cbde0352ea18270",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -861,7 +1003,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -870,13 +1012,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "406505452bb84597a6c39bc12267dcc6",
|
||||
"model_id": "e381ce02f19d4b85bbbaa9ea99e65623",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -891,7 +1033,7 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-25-50856f807a2a>:20: RuntimeWarning: divide by zero encountered in log10\n",
|
||||
"<ipython-input-19-50856f807a2a>:20: RuntimeWarning: divide by zero encountered in log10\n",
|
||||
" cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -1011,9 +1153,20 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"41.6"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"13 * 2**5 / 10"
|
||||
]
|
||||
|
|
|
|||
BIN
lab-windows/dsss_test_signals/dsss_test_noiseless_2000mHz.bin
Normal file
BIN
lab-windows/dsss_test_signals/dsss_test_noiseless_2000mHz.bin
Normal file
Binary file not shown.
File diff suppressed because one or more lines are too long
93661
lab-windows/dsss_test_signals/dsss_test_noiseless_20mHz.bin
Normal file
93661
lab-windows/dsss_test_signals/dsss_test_noiseless_20mHz.bin
Normal file
File diff suppressed because it is too large
Load diff
File diff suppressed because one or more lines are too long
Loading…
Add table
Add a link
Reference in a new issue