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/* Copyright 2015 The Chromium OS Authors. All rights reserved.
* Use of this source code is governed by a BSD-style license that can be
* found in the LICENSE file.
*/
#include "common.h"
#include "console.h"
#include "mag_cal.h"
#include "mat33.h"
#include "mat44.h"
#include "math.h"
#include "math_util.h"
#include "util.h"
/* Data from sensor is in 16th of uT */
#define MAG_CAL_RAW_UT 16
#define MAX_EIGEN_RATIO FLOAT_TO_FP(25.0f)
#define MAX_EIGEN_MAG FLOAT_TO_FP(80.0f * MAG_CAL_RAW_UT)
#define MIN_EIGEN_MAG FLOAT_TO_FP(10.0f * MAG_CAL_RAW_UT)
#define MAX_FIT_MAG MAX_EIGEN_MAG
#define MIN_FIT_MAG MIN_EIGEN_MAG
#define CPRINTF(format, args...) cprintf(CC_ACCEL, format, ## args)
#define PRINTF_FLOAT(x) ((int)((x) * 100.0f))
/*
* eigen value magnitude and ratio test
*
* Using the magnetometer information, caculate the 3 eigen values/vectors
* for the transformation. Check the eigen values are sane.
*/
static int moc_eigen_test(struct mag_cal_t *moc)
{
mat33_fp_t S;
fpv3_t eigenvals;
mat33_fp_t eigenvecs;
fp_t evmax, evmin, evmag;
int eigen_pass;
/* covariance matrix */
S[0][0] = moc->acc[0][0] - fp_sq(moc->acc[0][3]);
S[0][1] = S[1][0] =
moc->acc[0][1] - fp_mul(moc->acc[0][3], moc->acc[1][3]);
S[0][2] = S[2][0] =
moc->acc[0][2] - fp_mul(moc->acc[0][3], moc->acc[2][3]);
S[1][1] = moc->acc[1][1] - fp_sq(moc->acc[1][3]);
S[1][2] = S[2][1] =
moc->acc[1][2] - fp_mul(moc->acc[1][3], moc->acc[2][3]);
S[2][2] = moc->acc[2][2] - fp_sq(moc->acc[2][3]);
mat33_fp_get_eigenbasis(S, eigenvals, eigenvecs);
evmax = (eigenvals[X] > eigenvals[Y]) ? eigenvals[X] : eigenvals[Y];
evmax = (eigenvals[Z] > evmax) ? eigenvals[Z] : evmax;
evmin = (eigenvals[X] < eigenvals[Y]) ? eigenvals[X] : eigenvals[Y];
evmin = (eigenvals[Z] < evmin) ? eigenvals[Z] : evmin;
evmag = fp_sqrtf(eigenvals[X] + eigenvals[Y] + eigenvals[Z]);
eigen_pass = (fp_mul(evmin, MAX_EIGEN_RATIO) > evmax)
&& (evmag > MIN_EIGEN_MAG)
&& (evmag < MAX_EIGEN_MAG);
#if 0
CPRINTF("mag eigenvalues: (%d %d %d), ",
PRINTF_FLOAT(eigenvals[X]),
PRINTF_FLOAT(eigenvals[Y]),
PRINTF_FLOAT(eigenvals[Z]));
CPRINTF("ratio %d, mag %d: pass %d\r\n",
PRINTF_FLOAT(evmax / evmin),
PRINTF_FLOAT(evmag),
PRINTF_FLOAT(eigen_pass));
#endif
return eigen_pass;
}
/*
* Kasa sphere fitting with normal equation
*/
static int moc_fit(struct mag_cal_t *moc, fpv3_t bias, fp_t *radius)
{
sizev4_t pivot;
fpv4_t out;
int success = 0;
/*
* To reduce stack size, moc->acc is A,
* moc->acc_w is b: we are looking for out, where:
*
* A * out = b
* (4 x 4) (4 x 1) (4 x 1)
*/
/* complete the matrix: */
moc->acc[1][0] = moc->acc[0][1];
moc->acc[2][0] = moc->acc[0][2];
moc->acc[2][1] = moc->acc[1][2];
moc->acc[3][0] = moc->acc[0][3];
moc->acc[3][1] = moc->acc[1][3];
moc->acc[3][2] = moc->acc[2][3];
moc->acc[3][3] = FLOAT_TO_FP(1.0f);
moc->acc_w[X] = fp_mul(moc->acc_w[X], FLOAT_TO_FP(-1));
moc->acc_w[Y] = fp_mul(moc->acc_w[Y], FLOAT_TO_FP(-1));
moc->acc_w[Z] = fp_mul(moc->acc_w[Z], FLOAT_TO_FP(-1));
moc->acc_w[W] = fp_mul(moc->acc_w[W], FLOAT_TO_FP(-1));
mat44_fp_decompose_lup(moc->acc, pivot);
mat44_fp_solve(moc->acc, out, moc->acc_w, pivot);
/*
* spherei is defined by:
* (x - xc)^2 + (y - yc)^2 + (z - zc)^2 = r^2
*
* Where r is:
* xc = -out[X] / 2, yc = -out[Y] / 2, zc = -out[Z] / 2
* r = sqrt(xc^2 + yc^2 + zc^2 - out[W])
*/
memcpy(bias, out, sizeof(fpv3_t));
fpv3_scalar_mul(bias, FLOAT_TO_FP(-0.5f));
*radius = fp_sqrtf(fpv3_dot(bias, bias) - out[W]);
#if 0
CPRINTF("mag cal: bias (%d, %d, %d), R %d uT\n",
PRINTF_FLOAT(bias[X] / MAG_CAL_RAW_UT),
PRINTF_FLOAT(bias[Y] / MAG_CAL_RAW_UT),
PRINTF_FLOAT(bias[Z] / MAG_CAL_RAW_UT),
PRINTF_FLOAT(*radius / MAG_CAL_RAW_UT));
#endif
/* TODO (menghsuan): bound on bias as well? */
if (*radius > MIN_FIT_MAG && *radius < MAX_FIT_MAG)
success = 1;
return success;
}
void init_mag_cal(struct mag_cal_t *moc)
{
memset(moc->acc, 0, sizeof(moc->acc));
memset(moc->acc_w, 0, sizeof(moc->acc_w));
moc->nsamples = 0;
}
int mag_cal_update(struct mag_cal_t *moc, const intv3_t v)
{
int new_bias = 0;
/* 1. run accumulators */
fp_t w = fp_sq(v[X]) + fp_sq(v[Y]) + fp_sq(v[Z]);
moc->acc[0][3] += v[X];
moc->acc[1][3] += v[Y];
moc->acc[2][3] += v[Z];
moc->acc_w[W] += w;
moc->acc[0][0] += fp_sq(v[X]);
moc->acc[0][1] += fp_mul(v[X], v[Y]);
moc->acc[0][2] += fp_mul(v[X], v[Z]);
moc->acc_w[X] += fp_mul(v[X], w);
moc->acc[1][1] += fp_sq(v[Y]);
moc->acc[1][2] += fp_mul(v[Y], v[Z]);
moc->acc_w[Y] += fp_mul(v[Y], w);
moc->acc[2][2] += fp_sq(v[Z]);
moc->acc_w[Z] += fp_mul(v[Z], w);
if (moc->nsamples < MAG_CAL_MAX_SAMPLES)
moc->nsamples++;
/* 2. batch has enough samples? */
if (moc->batch_size > 0 && moc->nsamples >= moc->batch_size) {
fp_t inv = fp_div_dbz(FLOAT_TO_FP(1.0f),
INT_TO_FP((int)moc->nsamples));
moc->acc[0][3] = fp_mul(moc->acc[0][3], inv);
moc->acc[1][3] = fp_mul(moc->acc[1][3], inv);
moc->acc[2][3] = fp_mul(moc->acc[2][3], inv);
moc->acc_w[W] = fp_mul(moc->acc_w[W], inv);
moc->acc[0][0] = fp_mul(moc->acc[0][0], inv);
moc->acc[0][1] = fp_mul(moc->acc[0][1], inv);
moc->acc[0][2] = fp_mul(moc->acc[0][2], inv);
moc->acc_w[X] = fp_mul(moc->acc_w[X], inv);
moc->acc[1][1] = fp_mul(moc->acc[1][1], inv);
moc->acc[1][2] = fp_mul(moc->acc[1][2], inv);
moc->acc_w[Y] = fp_mul(moc->acc_w[Y], inv);
moc->acc[2][2] = fp_mul(moc->acc[2][2], inv);
moc->acc_w[Z] = fp_mul(moc->acc_w[Z], inv);
/* 3. eigen test */
if (moc_eigen_test(moc)) {
fpv3_t bias;
fp_t radius;
/* 4. Kasa sphere fitting */
if (moc_fit(moc, bias, &radius)) {
moc->bias[X] = fp_mul(bias[X], FLOAT_TO_FP(-1));
moc->bias[Y] = fp_mul(bias[Y], FLOAT_TO_FP(-1));
moc->bias[Z] = fp_mul(bias[Z], FLOAT_TO_FP(-1));
moc->radius = radius;
new_bias = 1;
}
}
/* 5. reset for next batch */
init_mag_cal(moc);
}
return new_bias;
}
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