-
Notifications
You must be signed in to change notification settings - Fork 3
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1 changed file
with
99 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1 +1,100 @@ | ||
#ifndef AranovskiyFilter_h | ||
#define AranovskiyFilter_h | ||
|
||
/* | ||
Aranovskiy frequency estimator which is a simple on-line filter. | ||
Ref: | ||
Alexey A. Bobtsov, Nikolay A. Nikolaev, Olga V. Slita, Alexander S. Borgul, Stanislav V. Aranovskiy | ||
The New Algorithm of Sinusoidal Signal Frequency Estimation. | ||
11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing July 3-5, 2013. Caen, France | ||
Usage example: | ||
double omega_up = 1.0 * (2 * PI); // upper frequency Hz * 2 * PI | ||
double k_gain = 2.0; | ||
double t_0 = 0.0; | ||
double x1_0 = 0.0; | ||
double theta_0 = - (omega_up * omega_up / 4.0); | ||
double sigma_0 = theta_0; | ||
double delta_t; // time step sec | ||
AranovskiyParams params; | ||
AranovskiyState state; | ||
aranovskiy_default_params(¶ms, omega_up, k_gain); | ||
aranovskiy_init_state(&state, t_0, x1_0, theta_0, sigma_0); | ||
unsigned long now = 0UL, last_update = 0UL; | ||
last_update = millis(); | ||
while(1) { | ||
delay(4); | ||
// measure | ||
double y = getAccelZfrom(imu) - 1.0; // remove G acceration | ||
now = millis(); | ||
delta_t = ((now - last_update) / 1000.0); | ||
last_update = now; | ||
aranovskiy_update(¶ms, &state, y, delta_t); | ||
// state.f contains estimated frequency | ||
} | ||
*/ | ||
|
||
typedef struct aranovskiy_params { | ||
double a = 1.0; | ||
double b = a; | ||
double k = 1.0; // gain | ||
} AranovskiyParams; | ||
|
||
typedef struct aranovskiy_state { | ||
double t = 0.0; // time | ||
double y; // signal measurement | ||
double x1 = 0.0; | ||
double theta = -0.25; | ||
double sigma = -0.25; | ||
double x1_dot; | ||
double sigma_dot; | ||
double omega; // frequency * 2 * pi | ||
double f; // frequency | ||
} AranovskiyState; | ||
|
||
void aranovskiy_default_params(AranovskiyParams* p, double omega_up, double k_gain); | ||
void aranovskiy_init_state(AranovskiyState* s, double t_0, double x1_0, double theta_0, double sigma_0); | ||
void aranovskiy_update(AranovskiyParams* p, AranovskiyState* s, double y, double delta_t); | ||
|
||
void aranovskiy_default_params(AranovskiyParams* p, double omega_up, double k_gain) { | ||
p->a = omega_up; | ||
p->b = p->a; | ||
p->k = k_gain; | ||
} | ||
|
||
void aranovskiy_init_state(AranovskiyState* s, double t_0, double x1_0, double theta_0, double sigma_0) { | ||
s->t = t_0; | ||
s->x1 = x1_0; | ||
s->theta = theta_0; | ||
s->sigma = sigma_0; | ||
} | ||
|
||
void aranovskiy_update(AranovskiyParams* p, AranovskiyState* s, double y, double delta_t) { | ||
s->x1_dot = - p->a * s->x1 + p->b * y; | ||
s->sigma_dot = - p->k * s->x1 * s->x1 * s->theta - p->k * p->a * s->x1 * s->x1_dot - p->k * p->b * s->x1_dot * y; | ||
s->theta = s->sigma + p->k * p->b * s->x1 * y; | ||
s->omega = sqrt(abs(s->theta)); | ||
s->f = s->omega / (2.0 * PI); | ||
// step | ||
s->x1 = s->x1 + s->x1_dot * delta_t; | ||
s->sigma = s->sigma + s->sigma_dot * delta_t; | ||
s->t = s->t + delta_t; | ||
} | ||
|
||
#endif | ||
|