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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, 0.0625 uT/LSB */
#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))
/**
* Compute the covariance element: (avg(ab) - avg(a)*avg(b))
*
* @param sq The accumulated sum of a*b
* @param a The accumulated sum of a
* @param b The accumulated sum of b
* @return (sq - ((a * b) * inv)) * inv
*/
static inline fp_t covariance_element(fp_t sq, fp_t a, fp_t b, fp_t inv)
{
return fp_mul(sq - fp_mul(fp_mul(a, b), inv), inv);
}
/*
* 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;
fp_t inv = fp_div_dbz(FLOAT_TO_FP(1.0f),
INT_TO_FP((int) moc->kasa_fit.nsamples));
int eigen_pass;
/* covariance matrix */
S[0][0] = covariance_element(moc->kasa_fit.acc_xx,
moc->kasa_fit.acc_x,
moc->kasa_fit.acc_x,
inv);
S[0][1] = S[1][0] = covariance_element(moc->kasa_fit.acc_xy,
moc->kasa_fit.acc_x,
moc->kasa_fit.acc_y,
inv);
S[0][2] = S[2][0] = covariance_element(moc->kasa_fit.acc_xz,
moc->kasa_fit.acc_x,
moc->kasa_fit.acc_z,
inv);
S[1][1] = covariance_element(moc->kasa_fit.acc_yy,
moc->kasa_fit.acc_y,
moc->kasa_fit.acc_y,
inv);
S[1][2] = S[2][1] = covariance_element(moc->kasa_fit.acc_yz,
moc->kasa_fit.acc_y,
moc->kasa_fit.acc_z,
inv);
S[2][2] = covariance_element(moc->kasa_fit.acc_zz,
moc->kasa_fit.acc_z,
moc->kasa_fit.acc_z,
inv);
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: (%.02d %.02d %.02d), ",
PRINTF_FLOAT(eigenvals[X]),
PRINTF_FLOAT(eigenvals[Y]),
PRINTF_FLOAT(eigenvals[Z]));
CPRINTF("ratio %.02d, mag %.02d: pass %d\r\n",
PRINTF_FLOAT(evmax / evmin),
PRINTF_FLOAT(evmag),
eigen_pass);
#endif
return eigen_pass;
}
void init_mag_cal(struct mag_cal_t *moc)
{
kasa_reset(&moc->kasa_fit);
}
int mag_cal_update(struct mag_cal_t *moc, const intv3_t v)
{
int new_bias = 0;
/* 1. run accumulators */
kasa_accumulate(&moc->kasa_fit, INT_TO_FP(v[X]), INT_TO_FP(v[Y]),
INT_TO_FP(v[Z]));
/* 2. batch has enough samples? */
if (moc->batch_size > 0 && moc->kasa_fit.nsamples >= moc->batch_size) {
/* 3. eigen test */
if (moc_eigen_test(moc)) {
fpv3_t bias;
fp_t radius;
/* 4. Kasa sphere fitting */
kasa_compute(&moc->kasa_fit, bias, &radius);
if (radius > MIN_FIT_MAG && radius < MAX_FIT_MAG) {
moc->bias[X] = -FP_TO_INT(bias[X]);
moc->bias[Y] = -FP_TO_INT(bias[Y]);
moc->bias[Z] = -FP_TO_INT(bias[Z]);
moc->radius = radius;
new_bias = 1;
}
}
/* 5. reset for next batch */
init_mag_cal(moc);
}
return new_bias;
}
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