fw simulator: WIP
This commit is contained in:
jaseg
parent
e505627ada
commit
87ae7dfcb3
24 changed files with 465 additions and 192 deletions
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@ -3,6 +3,8 @@
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# Dependency directories
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########################################################################################################################
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$(info $(shell env))
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CUBE_DIR ?= STM32CubeF4
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CMSIS_DIR ?= cmsis
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MSPDEBUG_DIR ?= mspdebug
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@ -25,13 +27,12 @@ FMEAS_SAMPLING_RATE ?= $(shell echo $(FMEAS_ADC_SAMPLING_RATE) / \($(FMEAS_FFT_
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DSSS_GOLD_CODE_NBITS ?= 5
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DSSS_DECIMATION ?= 10
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# TODO maybe auto adjust this based on detection rate?
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DSSS_THESHOLD_FACTOR ?= 5.0f
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DSSS_THRESHOLD_FACTOR ?= 5.0f
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DSSS_WAVELET_WIDTH ?= 7.3
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DSSS_WAVELET_LUT_SIZE ?= 69
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DSSS_FILTER_FC ?= 3e-3
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DSSS_FILTER_ORDER ?= 12
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PAYLOAD_DATA_BIT ?= 64
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TRANSMISSION_SYMBOLS ?= 32
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PRESIG_STORE_SIZE ?= 3
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@ -149,17 +150,16 @@ COMMON_CFLAGS += -DFMEAS_ADC_MAX=$(FMEAS_ADC_MAX)
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COMMON_CFLAGS += -DFMEAS_ADC_SAMPLING_RATE=$(FMEAS_ADC_SAMPLING_RATE)
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COMMON_CFLAGS += -DFMEAS_FFT_WINDOW_SIGMA=$(FMEAS_FFT_WINDOW_SIGMA)
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COMMON_CFLAGS += -DDSSS_DECIMATION=$(DSSS_DECIMATION)
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COMMON_CFLAGS += -DDSSS_THESHOLD_FACTOR=$(DSSS_THESHOLD_FACTOR)
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COMMON_CFLAGS += -DDSSS_THRESHOLD_FACTOR=$(DSSS_THRESHOLD_FACTOR)
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COMMON_CFLAGS += -DDSSS_WAVELET_WIDTH=$(DSSS_WAVELET_WIDTH)
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COMMON_CFLAGS += -DDSSS_WAVELET_LUT_SIZE=$(DSSS_WAVELET_LUT_SIZE)
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COMMON_CFLAGS += -DPAYLOAD_DATA_BIT=$(PAYLOAD_DATA_BIT)
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COMMON_CFLAGS += -DTRANSMISSION_SYMBOLS=$(TRANSMISSION_SYMBOLS)
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COMMON_CFLAGS += -DPRESIG_STORE_SIZE=$(PRESIG_STORE_SIZE)
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# for musl
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CFLAGS += -Dhidden=
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SIM_CFLAGS += -lm -DSIMULATION -fsanitize=address
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SIM_CFLAGS += -lm -DSIMULATION
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SIM_CFLAGS += -Wall -Wextra -Wpedantic -Wshadow -Wimplicit-function-declaration -Wundef -Wno-unused-parameter
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INT_CFLAGS += -Wall -Wextra -Wpedantic -Wshadow -Wimplicit-function-declaration -Wundef -Wno-unused-parameter
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@ -203,7 +203,8 @@ binsize: $(BUILDDIR)/$(BINARY) $(BUILDDIR)/$(BINARY:.elf=-symbol-sizes.pdf)
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@echo "▐▬▬▬▌ SyMbOL sIzE HiGhScORe LiSt ▐▬▬▬▌"
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$(NM) --print-size --size-sort --radix=d $< | tail -n 20
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src/dsss_demod.c: $(BUILDDIR)/generated/dsss_gold_code.h $(BUILDDIR)/generated/dsss_butter_filter.h
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# $(BUILDDIR)/generated/dsss_butter_filter.h
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src/dsss_demod.c: $(BUILDDIR)/generated/dsss_gold_code.h
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$(BUILDDIR)/generated/dsss_gold_code.h: $(BUILDDIR)/generated/gold_code_$(DSSS_GOLD_CODE_NBITS).h
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ln -srf $< $@
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@ -13,23 +13,23 @@
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#include "simulation.h"
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#include "generated/dsss_gold_code.h"
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#include "generated/dsss_butter_filter.h"
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// #include "generated/dsss_butter_filter.h"
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/* Generated CWT wavelet LUT */
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extern const float * const dsss_cwt_wavelet_table;
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struct iir_biquad cwt_filter_bq[DSSS_FILTER_CLEN] = {DSSS_FILTER_COEFF};
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//struct iir_biquad cwt_filter_bq[DSSS_FILTER_CLEN] = {DSSS_FILTER_COEFF};
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void debug_print_vector(const char *name, size_t len, const float *data, size_t stride, bool index, bool debug);
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static float gold_correlate_step(const size_t ncode, const float a[DSSS_CORRELATION_LENGTH], size_t offx, bool debug);
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static float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx);
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static float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]);
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static float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_state *const restrict st);
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//static float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]);
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//static float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_state *const restrict st);
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static void matcher_init(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE]);
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static void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE],
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uint64_t ts, int peak_ch, float peak_ampl);
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static void group_received(struct dsss_demod_state *st);
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static uint8_t decode_peak(int peak_ch, float peak_ampl);
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static symbol_t decode_peak(int peak_ch, float peak_ampl);
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#ifdef SIMULATION
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void debug_print_vector(const char *name, size_t len, const float *data, size_t stride, bool index, bool debug) {
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@ -61,7 +61,7 @@ void dsss_demod_init(struct dsss_demod_state *st) {
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void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts) {
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//const float hole_patching_threshold = 0.01 * DSSS_CORRELATION_LENGTH;
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bool log = false;
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bool log_groups = true;
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bool log_groups = false;
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st->signal[st->signal_wpos] = new_value;
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st->signal_wpos = (st->signal_wpos + 1) % ARRAY_LENGTH(st->signal);
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@ -99,7 +99,7 @@ void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts)
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max_ts = ts;
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}
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if (fabsf(val) > DSSS_THESHOLD_FACTOR)
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if (fabsf(val) > DSSS_THRESHOLD_FACTOR)
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found = true;
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}
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if (log) DEBUG_PRINTN("%f %d ", max_val, found);
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@ -134,12 +134,12 @@ void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts)
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/* Map a sequence match to a data symbol. This maps the sequence's index number to the 2nd to n+2nd bit of the result,
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* and maps the polarity of detection to the LSb. 5-bit example:
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*
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* [0, S, S, S, S, S, S, P] ; S ^= symbol index (0 - 2^n+1), P ^= symbol polarity
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* [0, S, S, S, S, S, S, P] ; S ^= symbol index (0 - 2^n+1 so we need just about n+1 bit), P ^= symbol polarity
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*
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* Symbol polarity is preserved from transmitter to receiver. The symbol index is n+1 bit instead of n bit since we have
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* 2^n+1 symbols to express, one too many for an n-bit index.
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*/
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uint8_t decode_peak(int peak_ch, float peak_ampl) {
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symbol_t decode_peak(int peak_ch, float peak_ampl) {
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return (peak_ch<<1) | (peak_ampl > 0);
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}
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@ -157,7 +157,7 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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const float score_depreciation = 0.1f; /* 0.0 -> no depreciation, 1.0 -> complete disregard */
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const int current_phase = ts % DSSS_CORRELATION_LENGTH;
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const int max_skips = TRANSMISSION_SYMBOLS/4*3;
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bool debug = true;
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bool debug = false;
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bool header_printed = false;
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for (size_t i=0; i<DSSS_MATCHER_CACHE_SIZE; i++) {
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@ -172,7 +172,7 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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header_printed = true;
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DEBUG_PRINTN("windows %zu\n", ts);
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}
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if (debug) DEBUG_PRINTN(" skip %d old=%f new=%f\n", i, states[i].candidate_score, score);
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if (debug) DEBUG_PRINTN(" skip %zd old=%f new=%f\n", i, states[i].candidate_score, score);
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/* We win, update candidate */
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assert(i < DSSS_MATCHER_CACHE_SIZE);
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states[i].candidate_score = score;
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@ -195,7 +195,7 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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header_printed = true;
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DEBUG_PRINTN("windows %zu\n", ts);
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}
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if (debug) DEBUG_PRINTN(" %d ", i);
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if (debug) DEBUG_PRINTN(" %zd ", i);
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/* Process window results */
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assert(i < DSSS_MATCHER_CACHE_SIZE);
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assert(0 <= states[i].data_pos && states[i].data_pos < TRANSMISSION_SYMBOLS);
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@ -235,7 +235,7 @@ static float score_group(const struct group *g, int phase_delta) {
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}
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void group_received(struct dsss_demod_state *st) {
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bool debug = true;
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bool debug = false;
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const int group_phase = st->group.max_ts % DSSS_CORRELATION_LENGTH;
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/* This is the score of a decoding starting at this group (with no context) */
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float base_score = score_group(&st->group, 0);
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@ -305,6 +305,7 @@ void group_received(struct dsss_demod_state *st) {
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}
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}
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#if 0
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float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]) {
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float intermediate = x;
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for (int i=0; i<(order+1)/2; i++)
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@ -320,6 +321,7 @@ float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_st
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st->reg[0] = intermediate;
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return out;
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}
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#endif
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float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx) {
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float sum = 0.0f;
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@ -10,8 +10,12 @@
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/* FIXME: move to makefile */
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#define DSSS_MATCHER_CACHE_SIZE 8
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/* FIXME: move to more appropriate header */
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#define PAYLOAD_DATA_BYTE ((PAYLOAD_DATA_BIT+7)/8)
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#if DSSS_GOLD_CODE_NBITS < 8
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typedef uint8_t symbol_t;
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#else
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typedef uint16_t symbol_t;
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#endif
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struct iir_biquad {
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float a[2];
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@ -43,12 +47,9 @@ struct matcher_state {
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int last_skips;
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int candidate_skips;
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#if DSSS_GOLD_CODE_NBITS > 7
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#error DSSS_GOLD_CODE_NBITS is too large for matcher_state.data data type (uint8_t)
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#endif
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uint8_t data[TRANSMISSION_SYMBOLS];
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symbol_t data[TRANSMISSION_SYMBOLS];
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int data_pos;
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uint8_t candidate_data;
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symbol_t candidate_data;
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};
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struct dsss_demod_state {
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@ -66,7 +67,7 @@ struct dsss_demod_state {
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};
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extern void handle_dsss_received(uint8_t data[static TRANSMISSION_SYMBOLS]);
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extern void handle_dsss_received(symbol_t data[static TRANSMISSION_SYMBOLS]);
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void dsss_demod_init(struct dsss_demod_state *st);
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void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts);
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@ -3,7 +3,7 @@
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#ifdef SIMULATION
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#include <stdio.h>
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#define DEBUG_PRINTN(...) fprintf(stderr, __VA_ARGS__)
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#define DEBUG_PRINTN(...) printf(__VA_ARGS__)
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#define DEBUG_PRINTNF(fmt, ...) DEBUG_PRINTN("%s:%d: " fmt, __FILE__, __LINE__, ##__VA_ARGS__)
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#define DEBUG_PRINT(fmt, ...) DEBUG_PRINTNF(fmt "\n", ##__VA_ARGS__)
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#else
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@ -18,7 +18,7 @@ if __name__ == '__main__':
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print(f'const float {varname}[{args.n}] = {{')
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win = sig.ricker(args.n, args.w)
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par = ' '.join(f'{f:>015.8g}f,' for f in win)
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par = ' '.join(f'{f:>015.12e}f,' for f in win)
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print(textwrap.fill(par,
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initial_indent=' '*4, subsequent_indent=' '*4,
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width=120,
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@ -12,7 +12,7 @@
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#include "dsss_demod.h"
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void handle_dsss_received(uint8_t data[static TRANSMISSION_SYMBOLS]) {
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void handle_dsss_received(symbol_t data[static TRANSMISSION_SYMBOLS]) {
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printf("data sequence received: [ ");
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for (size_t i=0; i<TRANSMISSION_SYMBOLS; i++) {
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printf("%+3d", ((data[i]&1) ? 1 : -1) * (data[i]>>1));
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@ -4,36 +4,204 @@ import os
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from os import path
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import subprocess
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import json
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from collections import namedtuple, defaultdict
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from tqdm import tqdm
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import uuid
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import multiprocessing
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import sqlite3
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import time
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from urllib.parse import urlparse
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import functools
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import tempfile
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import itertools
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import numpy as np
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np.set_printoptions(linewidth=240)
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from dsss_demod_test_waveform_gen import load_noise_meas_params, load_noise_synth_params,\
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mains_noise_measured, mains_noise_synthetic, modulate as dsss_modulate
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def build_test_binary(nbits, thf, decimation, symbols, cachedir):
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build_id = str(uuid.uuid4())
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builddir = path.join(cachedir, build_id)
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os.mkdir(builddir)
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cwd = path.join(path.dirname(__file__), '..')
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env = os.environ.copy()
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env['BUILDDIR'] = path.abspath(builddir)
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env['DSSS_GOLD_CODE_NBITS'] = str(nbits)
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env['DSSS_DECIMATION'] = str(decimation)
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env['DSSS_THRESHOLD_FACTOR'] = str(thf)
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env['DSSS_WAVELET_WIDTH'] = str(0.73 * decimation)
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env['DSSS_WAVELET_LUT_SIZE'] = str(10 * decimation)
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env['TRANSMISSION_SYMBOLS'] = str(symbols)
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with open(path.join(builddir, 'make_stdout.txt'), 'w') as stdout,\
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open(path.join(builddir, 'make_stderr.txt'), 'w') as stderr:
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subprocess.run(['make', 'clean', os.path.abspath(path.join(builddir, 'tools/dsss_demod_test'))],
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env=env, cwd=cwd, check=True, stdout=stdout, stderr=stderr)
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return build_id
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@functools.lru_cache()
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def load_noise_gen(url):
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schema, refpath = url.split('://')
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if not path.isabs(refpath):
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refpath = path.abspath(path.join(path.dirname(__file__), refpath))
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if schema == 'meas':
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return mains_noise_measured, load_noise_meas_params(refpath)
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elif schema == 'synth':
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return mains_noise_synthetic, load_noise_synth_params(refpath)
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else:
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raise ValueError('Invalid schema', schema)
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def sequence_matcher(test_data, decoded, max_shift=3):
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match_result = []
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for shift in range(-max_shift, max_shift):
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failures = -shift if shift < 0 else 0 # we're skipping the first $shift symbols
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a = test_data if shift > 0 else test_data[-shift:]
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b = decoded if shift < 0 else decoded[shift:]
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for i, (ref, found) in enumerate(itertools.zip_longest(a, b)):
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if ref is None: # end of signal
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break
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if ref != found:
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failures += 1
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match_result.append(failures)
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failures = min(match_result)
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return failures/len(test_data)
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ResultParams = namedtuple('ResultParams', ['nbits', 'thf', 'decimation', 'symbols', 'seed', 'amplitude', 'background'])
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def run_test(seed, amplitude_spec, background, nbits, decimation, symbols, thfs, lookup_binary, cachedir):
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noise_gen, noise_params = load_noise_gen(background)
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test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), symbols)
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signal = np.repeat(dsss_modulate(test_data, nbits) * 2.0 - 1, decimation)
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# We're re-using the seed here. This is not a problem.
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noise = noise_gen(seed, len(signal), *noise_params)
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amplitudes = amplitude_spec[0] * 10 ** np.linspace(0, amplitude_spec[1], amplitude_spec[2])
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output = []
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for amp in amplitudes:
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with tempfile.NamedTemporaryFile(dir=cachedir) as f:
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waveform = signal*amp + noise
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f.write(waveform.astype('float').tobytes())
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f.flush()
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for thf in thfs:
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cmdline = [lookup_binary(nbits, thf, decimation, symbols), f.name]
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proc = subprocess.Popen(cmdline, stdout=subprocess.PIPE, text=True)
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stdout, _stderr = proc.communicate()
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if proc.returncode != 0:
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raise SystemError(f'Subprocess signalled error: {proc.returncode=}')
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lines = stdout.splitlines()
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matched = [ l.partition('[')[2].partition(']')[0]
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for l in lines if l.strip().startswith('data sequence received:') ]
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matched = [ [ int(elem) for elem in l.split(',') ] for l in matched ]
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ser = min(sequence_matcher(test_data, match) for match in matched) if matched else None
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rpars = ResultParams(nbits, thf, decimation, symbols, seed, amp, background)
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output.append((rpars, ser))
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print(f'ran {rpars} {ser=} {" ".join(cmdline)}')
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return output
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def parallel_generator(db, table, columns, builder, param_list, desc, context={}, params_mapper=lambda *args: args):
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with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:
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with db as conn:
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jobs = []
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for params in param_list:
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found_res = conn.execute(
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f'SELECT result FROM {table} WHERE ({",".join(columns)}) = ({",".join("?"*len(columns))})',
|
||||
params_mapper(*params)).fetchone()
|
||||
|
||||
if found_res:
|
||||
yield params, json.loads(*found_res)
|
||||
|
||||
else:
|
||||
jobs.append((params, pool.apply_async(builder, params, context)))
|
||||
|
||||
pool.close()
|
||||
print('Using', len(param_list) - len(jobs), 'cached jobs', flush=True)
|
||||
with tqdm(total=len(jobs), desc=desc) as tq:
|
||||
for params, res in jobs:
|
||||
tq.update(1)
|
||||
result = res.get()
|
||||
with db as conn:
|
||||
conn.execute(f'INSERT INTO {table} VALUES ({"?,"*len(params)}?,?)',
|
||||
(*params_mapper(*params), json.dumps(result), timestamp()))
|
||||
yield params, result
|
||||
pool.join()
|
||||
|
||||
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')
|
||||
parser.add_argument('-d', '--dump', help='Write results to JSON file')
|
||||
parser.add_argument('-c', '--cachedir', default='dsss_test_cache', help='Directory to store build output and data in')
|
||||
args = parser.parse_args()
|
||||
|
||||
bin_files = [ path.join(args.dir, d) for d in os.listdir(args.dir) if d.lower().endswith('.bin') ]
|
||||
DecoderParams = namedtuple('DecoderParams', ['nbits', 'thf', 'decimation', 'symbols'])
|
||||
dec_paramses = [ DecoderParams(nbits=nbits, thf=thf, decimation=decimation, symbols=20)
|
||||
for nbits in [5, 6]
|
||||
for thf in [4.5, 4.0, 5.0]
|
||||
for decimation in [10, 5, 22] ]
|
||||
# dec_paramses = [ DecoderParams(nbits=nbits, thf=thf, decimation=decimation, symbols=100)
|
||||
# for nbits in [5, 6, 7, 8]
|
||||
# for thf in [1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0]
|
||||
# for decimation in [1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 16, 22, 30, 40, 50] ]
|
||||
|
||||
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)
|
||||
build_cache_dir = path.join(args.cachedir, 'builds')
|
||||
data_cache_dir = path.join(args.cachedir, 'data')
|
||||
os.makedirs(build_cache_dir, exist_ok=True)
|
||||
os.makedirs(data_cache_dir, exist_ok=True)
|
||||
|
||||
# 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)))
|
||||
build_db = sqlite3.connect(path.join(args.cachedir, 'build_db.sqlite3'))
|
||||
build_db.execute('CREATE TABLE IF NOT EXISTS builds (nbits, thf, decimation, symbols, result, timestamp)')
|
||||
timestamp = lambda: int(time.time()*1000)
|
||||
|
||||
print(f'{path.basename(p):<60}: mean={mean:<8.4f}Hz rms={rms*1000:.3f}mHz')
|
||||
builds = dict(parallel_generator(build_db, table='builds', columns=['nbits', 'thf', 'decimation', 'symbols'],
|
||||
builder=build_test_binary, param_list=dec_paramses, desc='Building decoders',
|
||||
context=dict(cachedir=build_cache_dir)))
|
||||
print('Done building decoders.')
|
||||
|
||||
GeneratorParams = namedtuple('GeneratorParams', ['seed', 'amplitude_spec', 'background'])
|
||||
gen_params = [ GeneratorParams(rep, (5e-3, 1, 5), background)
|
||||
#GeneratorParams(rep, (0.05e-3, 3.5, 50), background)
|
||||
for rep in range(30)
|
||||
for background in ['meas://fmeas_export_ocxo_2day.bin', 'synth://grid_freq_psd_spl_108pt.json'] ]
|
||||
|
||||
data_db = sqlite3.connect(path.join(args.cachedir, 'data_db.sqlite3'))
|
||||
data_db.execute('CREATE TABLE IF NOT EXISTS waveforms'
|
||||
'(seed, amplitude_spec, background, nbits, decimation, symbols, thresholds, result, timestamp)')
|
||||
|
||||
dec_param_groups = defaultdict(lambda: [])
|
||||
for nbits, thf, decimation, symbols in dec_paramses:
|
||||
dec_param_groups[(nbits, decimation, symbols)].append(thf)
|
||||
waveform_params = [ (*gp, *dp, thfs) for gp in gen_params for dp, thfs in dec_param_groups.items() ]
|
||||
print(f'Generated {len(waveform_params)} parameter sets')
|
||||
|
||||
def lookup_binary(*params):
|
||||
return path.join(build_cache_dir, builds[tuple(params)], 'tools/dsss_demod_test')
|
||||
|
||||
def params_mapper(seed, amplitude_spec, background, nbits, decimation, symbols, thresholds):
|
||||
amplitude_spec = ','.join(str(x) for x in amplitude_spec)
|
||||
thresholds = ','.join(str(x) for x in thresholds)
|
||||
return seed, amplitude_spec, background, nbits, decimation, symbols, thresholds
|
||||
|
||||
results = []
|
||||
for _params, chunk in parallel_generator(data_db, 'waveforms',
|
||||
['seed', 'amplitude_spec', 'background', 'nbits', 'decimation', 'symbols', 'thresholds'],
|
||||
params_mapper=params_mapper,
|
||||
builder=run_test,
|
||||
param_list=waveform_params, desc='Generating waveforms',
|
||||
context=dict(cachedir=data_cache_dir, lookup_binary=lookup_binary)):
|
||||
results += chunk
|
||||
|
||||
if args.dump:
|
||||
with open(args.dump, 'w') as f:
|
||||
json.dump(savedata, f)
|
||||
json.dump(results, f)
|
||||
|
||||
|
|
|
|||
71
controller/fw/tools/dsss_demod_test_waveform_gen.py
Normal file
71
controller/fw/tools/dsss_demod_test_waveform_gen.py
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
|
||||
import functools
|
||||
|
||||
import numpy as np
|
||||
import numbers
|
||||
import math
|
||||
from scipy import signal as sig
|
||||
import scipy.fftpack
|
||||
|
||||
sampling_rate = 10 # sp/s
|
||||
|
||||
# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py
|
||||
preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],
|
||||
8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]],
|
||||
10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}
|
||||
|
||||
def gen_gold(seq1, seq2):
|
||||
gold = [seq1, seq2]
|
||||
for shift in range(len(seq1)):
|
||||
gold.append(seq1 ^ np.roll(seq2, -shift))
|
||||
return gold
|
||||
|
||||
def gold(n):
|
||||
n = int(n)
|
||||
if not n in preferred_pairs:
|
||||
raise KeyError('preferred pairs for %s bits unknown' % str(n))
|
||||
t0, t1 = preferred_pairs[n]
|
||||
(seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)
|
||||
return gen_gold(seq0, seq1)
|
||||
|
||||
def modulate(data, nbits=5):
|
||||
# 0, 1 -> -1, 1
|
||||
mask = np.array(gold(nbits))*2 - 1
|
||||
|
||||
sel = mask[data>>1]
|
||||
data_lsb_centered = ((data&1)*2 - 1)
|
||||
|
||||
signal = (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2
|
||||
return np.hstack([ np.zeros(len(mask)), signal, np.zeros(len(mask)) ])
|
||||
|
||||
def load_noise_meas_params(capture_file):
|
||||
with open(capture_file, 'rb') as f:
|
||||
meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))
|
||||
meas_data -= np.mean(meas_data)
|
||||
return (meas_data,)
|
||||
|
||||
def mains_noise_measured(seed, n, meas_data):
|
||||
last_valid = len(meas_data) - n
|
||||
st = np.random.RandomState(seed)
|
||||
start = st.randint(last_valid)
|
||||
return meas_data[start:start+n]
|
||||
|
||||
def load_noise_synth_params(specfile):
|
||||
with open(specfile) as f:
|
||||
d = json.load(f)
|
||||
return (np.linspace(*d['x_spec']), # spl_x
|
||||
d['x_spec'][2], # spl_N
|
||||
(d['t'], d['c'], d['k'])) # psd_spl
|
||||
|
||||
def mains_noise_synthetic(seed, n, psd_spl, spl_N, spl_x):
|
||||
st = np.random.RandomState(seed)
|
||||
noise = st.normal(size=spl_N) * 2
|
||||
spec = scipy.fftpack.fft(noise) **2
|
||||
|
||||
spec *= np.exp(scipy.interpolate.splev(spl_x, psd_spl))
|
||||
|
||||
spec **= 1/2
|
||||
|
||||
renoise = scipy.fftpack.ifft(spec)
|
||||
return renoise[10000:][:n]
|
||||
|
||||
BIN
controller/fw/tools/fmeas_export_ocxo_2day.bin
Normal file
BIN
controller/fw/tools/fmeas_export_ocxo_2day.bin
Normal file
Binary file not shown.
1
controller/fw/tools/grid_freq_psd_spl_108pt.json
Normal file
1
controller/fw/tools/grid_freq_psd_spl_108pt.json
Normal file
|
|
@ -0,0 +1 @@
|
|||
{"x_spec": [3.2595692805152726e-05, 5.0, 613575], "t": [3.2595692805152726e-05, 3.2595692805152726e-05, 3.2595692805152726e-05, 3.2595692805152726e-05, 0.0001423024947075771, 0.00015800362803968106, 0.00017543716661470822, 0.00019479425764873777, 0.0002162871388378975, 0.00024015146540428407, 0.00026664889389955537, 0.00029606995109590574, 0.00032873721941990017, 0.0003650088738553592, 0.0004052826090950758, 0.00045000000000000004, 0.000499651343175437, 0.0005547810327489297, 0.0006159935292916862, 0.0006839599873288199, 0.0007594256141046668, 0.0008432178402871724, 0.0009362553921977272, 0.0010395583650374223, 0.0011542594075560205, 0.001281616140796111, 0.0014230249470757708, 0.001580036280396809, 0.0017543716661470824, 0.0019479425764873776, 0.002162871388378975, 0.0024015146540428403, 0.002666488938995554, 0.002960699510959057, 0.0032873721941990056, 0.0036500887385535925, 0.004052826090950754, 0.0045000000000000005, 0.00499651343175437, 0.005547810327489296, 0.006159935292916869, 0.0068395998732882, 0.007594256141046669, 0.008432178402871724, 0.009362553921977271, 0.010395583650374221, 0.011542594075560205, 0.012816161407961109, 0.014230249470757707, 0.01580036280396809, 0.017543716661470823, 0.01947942576487376, 0.02162871388378975, 0.024015146540428405, 0.026664889389955565, 0.02960699510959057, 0.03287372194199005, 0.036500887385535925, 0.04052826090950754, 0.045, 0.0499651343175437, 0.05547810327489296, 0.06159935292916863, 0.06839599873288206, 0.07594256141046668, 0.08432178402871732, 0.09362553921977272, 0.10395583650374222, 0.11542594075560206, 0.12816161407961107, 0.14230249470757705, 0.15800362803968088, 0.1754371666147082, 0.1947942576487376, 0.21628713883789774, 0.24015146540428406, 0.26664889389955565, 0.2960699510959057, 0.32873721941990053, 0.36500887385535924, 0.40528260909507535, 0.45, 0.499651343175437, 0.5547810327489296, 0.6159935292916868, 0.6839599873288206, 0.7594256141046669, 0.8432178402871732, 0.9362553921977271, 1.0395583650374223, 1.1542594075560206, 1.2816161407961109, 1.4230249470757708, 1.5800362803968104, 1.7543716661470823, 1.9479425764873777, 2.162871388378975, 2.4015146540428405, 2.6664889389955535, 2.960699510959057, 3.287372194199002, 3.6500887385535927, 4.052826090950758, 4.5, 5.0, 5.0, 5.0, 5.0], "c": [0.7720161468716866, -0.5547528253056444, 0.30706059086000753, 0.19422577014134906, -1.1954636661840032, 0.9215976941641111, -0.6668136393976918, -1.341269161156733, -0.16311330594842666, -1.7639636752234251, -1.238385544822954, -0.32649555618555554, -0.03086589610280171, -2.358195657381619, -0.5759152419849985, 0.1892225800004134, -1.8122889670546236, -0.8109120798216202, -0.5500991736738969, -4.680192969256771, -2.8007700704649876, 0.16866469558571784, -1.1040811840849307, -3.0243574268705546, -4.018139927365795, -4.100581028618109, -0.556354762846191, -7.414377514669229, 1.36396325920194, -6.002559557058508, -2.2113451390305365, -4.578944771104116, -4.372644849632638, -3.945339124673235, -4.778747958903158, -2.370174137632325, -5.7372466088109295, -4.707506574819875, -4.834404729330929, -5.005244244061701, -5.82644896783577, -4.717966026411524, -6.146374820241562, -4.972788381244952, -5.854957092953355, -5.702174935205885, -6.222035857079607, -6.2128389666872, -6.212821706753751, -6.253599689326325, -6.681685577659057, -6.372364384360678, -6.771223202540934, -6.856809137231159, -6.986412256164045, -7.190466178818742, -7.577896455149433, -7.515731696006047, -7.598155006351761, -7.824526916149126, -8.141496591776512, -8.36794927682997, -8.80307396767114, -8.828816533544659, -9.357524260470413, -9.658130054343863, -10.005768472049466, -10.499801262514108, -11.028689820560558, -11.413688641742898, -11.906162042727946, -12.232342460719975, -12.438432746733596, -13.088338100203112, -12.308710772618745, -11.685074853925329, -11.397838681243094, -12.265219694936695, -13.600359694898529, -14.031425961884718, -12.236885080485473, -13.527508426900974, -13.698402018452601, -13.397911198962568, -14.144410560196603, -13.905769594095293, -14.410874830544122, -14.531727635304264, -14.59275291853806, -14.35404826562502, -14.58670053318149, -14.432515268864977, -14.363428024828353, -14.429222027493264, -14.73947634127499, -14.717315405960353, -14.678539669792505, -14.825278423641382, -14.80936417940876, -14.943375264882789, -14.680885181815674, -14.54841244844906, -14.634365225950589, -14.609444790868906, 0.0, 0.0, 0.0, 0.0], "k": 3}
|
||||
File diff suppressed because one or more lines are too long
Binary file not shown.
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|
|
@ -701,7 +701,7 @@
|
|||
" ax.set_xlabel('Period T [s]')\n",
|
||||
" ax.set_ylabel('Power Spectral Density [Hz^2/Hz]')\n",
|
||||
"\n",
|
||||
" for i, t in enumerate([2.0, 3.2, 30, 150, 220, 450, 600, 900]):\n",
|
||||
" for i, t in enumerate([2.0, 3.2, 30, 150, 220, 450, 600, 900]):z\n",
|
||||
" ax.axvline(1/t, color='red', alpha=0.5, zorder=-1)\n",
|
||||
" ax.annotate(f'{t} s', xy=(1/t, 1e-7), xytext=(-10, 5), xycoords='data', textcoords='offset pixels', rotation=90)\n",
|
||||
" #ax.text(1/60, 10,'60 s', ha='left')\n",
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ all: safety_reset.pdf
|
|||
|
||||
safety_reset.pdf: resources/grid_freq_estimation.pdf
|
||||
safety_reset.pdf: resources/gps_clock_jitter_analysis.pdf
|
||||
safety_reset.pdf: resources/dsss_experiments-ber.pdf
|
||||
|
||||
%.pdf: %.tex %.bib
|
||||
pdflatex -shell-escape $<
|
||||
|
|
|
|||
|
|
@ -1183,16 +1183,30 @@ indicates SER is related fairly monotonically to the signal-to-noise margins ins
|
|||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/dsss_gold_nbits_overview}
|
||||
\includegraphics{../lab-windows/fig_out/dsss_gold_nbits_overview}
|
||||
\caption{
|
||||
Symbol Error Rate (SER) as a function of transmission amplitude. The line indicates the mean of several
|
||||
measurements for each parameter set. The shaded areas indicate one standard deviation from the mean. Background
|
||||
noise for each trial is a random segment of measured grid frequency. Background noise amplitude is the same for
|
||||
all trials. Shown are four traces for four different DSSS sequence lengths. Using a 5-bit gold code, one DSSS
|
||||
symbol measures 31 chips. 6 bit per symbol are 63 chips, 7 bit are 127 chips and 8 bit 255 chips. This
|
||||
simulation uses a decimation of 10, which corresponds to an $1 \text{s}$ chip length at our $10 \text{Hz}$ grid
|
||||
frequency sampling rate. At 5 bit per symbol, one symbol takes $31 \text{s}$ and one bit takes $6.2 \text{s}$
|
||||
amortized. At 8 bit one symbol takes $255 \text{s} = 4 \text{min} 15 \text{s}$ and one bit takes $31.9 \text{s}$
|
||||
amortized. Here, slower transmission speed buys coding gain. All else being the same this allows for a decrease
|
||||
in transmission power.
|
||||
}
|
||||
\label{dsss_gold_nbits_overview}
|
||||
\end{figure}
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/dsss_gold_nbits_sensitivity}
|
||||
\includegraphics{../lab-windows/fig_out/dsss_gold_nbits_sensitivity}
|
||||
\caption{
|
||||
Amplitude at a SER of 0.5\ in mHz depending on symbol length. Here we can observe an increase of sensitivity
|
||||
with increasing symbol length, but we can clearly see diminishing returns above 6 bit (63 chips). Considering
|
||||
that each bit roughly doubles overall transmission time for a given data length it seems lower bit counts are
|
||||
preferrable if the necessary transmitter power can be realized.
|
||||
}
|
||||
\label{dsss_gold_nbits_sensitivity}
|
||||
\end{figure}
|
||||
|
|
@ -1200,20 +1214,38 @@ indicates SER is related fairly monotonically to the signal-to-noise margins ins
|
|||
\begin{figure}
|
||||
\begin{subfigure}{\textwidth}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/dsss_thf_amplitude_5678}
|
||||
\includegraphics{../lab-windows/fig_out/dsss_thf_amplitude_5678}
|
||||
\label{dsss_thf_amplitude_5678}
|
||||
\caption{
|
||||
\footnotesize SER vs.\ amplitude graph similar to fig.\ \ref{dsss_gold_nbits_overview} with dependence on
|
||||
threshold factor color-coded. Each graph shows traces for a single DSSS symbol length.
|
||||
}
|
||||
\end{subfigure}
|
||||
\end{figure}
|
||||
\begin{figure}
|
||||
\ContinuedFloat
|
||||
\begin{subfigure}{\textwidth}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/dsss_thf_sensitivity_5678}
|
||||
\includegraphics{../lab-windows/fig_out/dsss_thf_sensitivity_5678}
|
||||
\label{dsss_thf_sensitivity_5678}
|
||||
\caption{
|
||||
\footnotesize Graphs of amplitude at $SER=0.5$ for each symbol length as well as asymptotic SER for large
|
||||
amplitudes. Areas shaded red indicate that $SER=0.5$ was not reached for any amplitude in the simulated
|
||||
range. We can observe that smaller symbol lengths favor lower threshold factors, and that optimal threshold
|
||||
factors for all symbol lengths are between $4.0$ and $5.0$.
|
||||
}
|
||||
\end{subfigure}
|
||||
\caption{
|
||||
}
|
||||
Dependence of demodulator sensitivity on the threshold factor used for correlation peak detection in our
|
||||
DSSS demodulator. This is an empirically-determined parameter specific to our demodulation algorithm. At low
|
||||
threshold factors our classifier yields lots of spurious peaks that have to be thrown out by our maximum
|
||||
likelihood estimator. These spurious peaks have a random time distribution and thus do not pose much of a
|
||||
challenge to our MLE but at very low threshold factors the number of spurious peaks slows down decoding and
|
||||
does still clog our MLE's internal size-limited candidate lists which leads to failed decodings. At very
|
||||
high threshold factors decoding performance suffers greatly since many valid correlation peaks get
|
||||
incorrectly ignored. The glitches at medium threshold factors in the 7- and 8-bit graphs are artifacts of
|
||||
our prototype decoding algorithm that we have not fixed in the prototype implementation since we wanted to
|
||||
focus on the final C version.}
|
||||
\label{dsss_thf_sensitivity}
|
||||
\end{figure}
|
||||
|
||||
|
|
@ -1223,16 +1255,31 @@ indicates SER is related fairly monotonically to the signal-to-noise margins ins
|
|||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/chip_duration_sensitivity_5}
|
||||
\label{chip_duration_sensitivity_5}
|
||||
\caption{
|
||||
5 bit Gold code
|
||||
}
|
||||
\end{subfigure}
|
||||
\end{figure}
|
||||
\begin{figure}
|
||||
\ContinuedFloat
|
||||
\begin{subfigure}{\textwidth}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/chip_duration_sensitivity_6}
|
||||
\label{chip_duration_sensitivity_6}
|
||||
\caption{
|
||||
6 bit Gold code
|
||||
}
|
||||
\end{subfigure}
|
||||
\caption{
|
||||
Dependence of demodulator sensitivity on DSSS chip duration. Due to computational constraints this simulation is
|
||||
limited to 5 bit and 6 bit DSSS sequences. There is a clearly visible sensitivity maximum at fairly short chip
|
||||
lengths around $0.2 \text{s}$. Short chip durations shift the entire transmission band up in frequency. In fig.\
|
||||
\ref{freq_meas_spectrum} we can see that noise energy is mostly concentrated at lower frequencies, so shifting
|
||||
our signal up in frequency will reduce the amount of noise the decoder sees behind the correlator by shifting
|
||||
the band of interest into a lower-noise spectral region. For a practical implementation chip duration is limited
|
||||
by physical factors such as the maximum modulation slew rate ($\frac{\text{d}P}{\text{d}t}$), the maximum
|
||||
Rate-Of-Change-Of-Frequency (ROCOF, $\frac{\text{d}f}{\text{d}t}$) the grid can tolerate and possible inertial
|
||||
effects limiting response of frequency to load changes at certain load levels.
|
||||
% FIXME are these inertial effects likely? Ask an expert.
|
||||
}
|
||||
\label{chip_duration_sensitivity}
|
||||
\end{figure}
|
||||
|
|
@ -1243,16 +1290,25 @@ indicates SER is related fairly monotonically to the signal-to-noise margins ins
|
|||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/chip_duration_sensitivity_cmp_meas_6}
|
||||
\label{chip_duration_sensitivity_cmp_meas_6}
|
||||
\caption{
|
||||
Simulation using baseline frequency data from actual measurements.
|
||||
}
|
||||
\end{subfigure}
|
||||
\end{figure}
|
||||
\begin{figure}
|
||||
\ContinuedFloat
|
||||
\begin{subfigure}{\textwidth}
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{../lab-windows/fig_out/chip_duration_sensitivity_cmp_synth_6}
|
||||
\label{chip_duration_sensitivity_cmp_synth_6}
|
||||
\caption{
|
||||
Simulation using synthetic frequency data.
|
||||
}
|
||||
\end{subfigure}
|
||||
\caption{
|
||||
Chip duration/sensitivity simulation results like in fig.\ \ref{chip_duration_sensitivity} compared between a
|
||||
simulation using measured frequency data like previous graphs and one using artificially generated noise. There
|
||||
is almost no visible difference indicating that we have found a good model of reality in our noise synthesizer,
|
||||
but also that real grid frequency behaves like a frequency-shaped gaussian noise process.
|
||||
}
|
||||
\label{chip_duration_sensitivity_cmp}
|
||||
\end{figure}
|
||||
|
|
@ -1355,6 +1411,7 @@ correctly configure than it is to simply use separate hardware and secure the in
|
|||
|
||||
\includenotebook{Grid frequency estimation}{grid_freq_estimation}
|
||||
\includenotebook{Frequency sensor clock stability analysis}{gps_clock_jitter_analysis}
|
||||
\includenotebook{DSSS modulation experiments}{dsss_experiments-ber}
|
||||
|
||||
\chapter{Demonstrator schematics and code}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue