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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 25.0f
#define MAX_EIGEN_MAG (80.0f * MAG_CAL_RAW_UT)
#define MIN_EIGEN_MAG (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_t S;
vec3_t eigenvals;
mat33_t eigenvecs;
float evmax, evmin, evmag;
int eigen_pass;
/* covariance matrix */
S[0][0] = moc->acc[0][0] - moc->acc[0][3] * moc->acc[0][3];
S[0][1] = S[1][0] = moc->acc[0][1] - moc->acc[0][3] * moc->acc[1][3];
S[0][2] = S[2][0] = moc->acc[0][2] - moc->acc[0][3] * moc->acc[2][3];
S[1][1] = moc->acc[1][1] - moc->acc[1][3] * moc->acc[1][3];
S[1][2] = S[2][1] = moc->acc[1][2] - moc->acc[1][3] * moc->acc[2][3];
S[2][2] = moc->acc[2][2] - moc->acc[2][3] * moc->acc[2][3];
mat33_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 = sqrtf(eigenvals[X] + eigenvals[Y] + eigenvals[Z]);
eigen_pass = (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, vec3_t bias, float *radius)
{
size4_t pivot;
vec4_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] = 1.0f;
moc->acc_w[X] *= -1;
moc->acc_w[Y] *= -1;
moc->acc_w[Z] *= -1;
moc->acc_w[W] *= -1;
mat44_decompose_lup(moc->acc, pivot);
mat44_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(vec3_t));
vec3_scalar_mul(bias, -0.5f);
*radius = sqrtf(vec3_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 vector_3_t v)
{
int new_bias = 0;
/* 1. run accumulators */
float w = v[X] * v[X] + v[Y] * v[Y] + v[Z] * 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] += v[X] * v[X];
moc->acc[0][1] += v[X] * v[Y];
moc->acc[0][2] += v[X] * v[Z];
moc->acc_w[X] += v[X] * w;
moc->acc[1][1] += v[Y] * v[Y];
moc->acc[1][2] += v[Y] * v[Z];
moc->acc_w[Y] += v[Y] * w;
moc->acc[2][2] += v[Z] * v[Z];
moc->acc_w[Z] += 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) {
float inv = 1.0f / moc->nsamples;
moc->acc[0][3] *= inv;
moc->acc[1][3] *= inv;
moc->acc[2][3] *= inv;
moc->acc_w[W] *= inv;
moc->acc[0][0] *= inv;
moc->acc[0][1] *= inv;
moc->acc[0][2] *= inv;
moc->acc_w[X] *= inv;
moc->acc[1][1] *= inv;
moc->acc[1][2] *= inv;
moc->acc_w[Y] *= inv;
moc->acc[2][2] *= inv;
moc->acc_w[Z] *= inv;
/* 3. eigen test */
if (moc_eigen_test(moc)) {
vec3_t bias;
float radius;
/* 4. Kasa sphere fitting */
if (moc_fit(moc, bias, &radius)) {
moc->bias[X] = bias[X] * -1;
moc->bias[Y] = bias[Y] * -1;
moc->bias[Z] = bias[Z] * -1;
moc->radius = radius;
new_bias = 1;
}
}
/* 5. reset for next batch */
init_mag_cal(moc);
}
return new_bias;
}
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