Kalman-and-Bayesian-Filters.../experiments/two_radar.py
Roger Labbe 48f7eadc3e Refactoring UKF chapter.
Still a lot of work to be done, but I'm writing more realistic examples,
and reordering the material.
2015-02-16 07:10:01 -08:00

164 lines
3.0 KiB
Python

# -*- coding: utf-8 -*-
"""
Created on Sun Feb 15 14:29:23 2015
@author: rlabbe
"""
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 8 09:34:36 2015
@author: rlabbe
"""
import numpy as np
import matplotlib.pyplot as plt
from numpy import array, asarray
from numpy.linalg import norm
from numpy.random import randn
import math
from math import atan2, radians, degrees
from filterpy.kalman import UnscentedKalmanFilter as UKF
class RadarStation(object):
def __init__(self, pos, range_std, bearing_std):
self.pos = asarray(pos)
self.range_std = range_std
self.bearing_std = bearing_std
def reading_of(self, ac_pos):
""" Returns range and bearing to aircraft as tuple. bearing is in
radians.
"""
diff = np.subtract(self.pos, ac_pos)
rng = norm(diff)
brg = atan2(diff[1], diff[0])
return rng, brg
def noisy_reading(self, ac_pos):
rng, brg = self.reading_of(ac_pos)
rng += randn() * self.range_std
brg += randn() * self.bearing_std
return rng, brg
class ACSim(object):
def __init__(self, pos, vel, vel_std):
self.pos = asarray(pos, dtype=float)
self.vel = asarray(vel, dtype=float)
self.vel_std = vel_std
def update(self):
vel = self.vel + (randn() * self.vel_std)
self.pos += vel
print(pos)
return self.pos
dt = 1.
def hx(x):
r1, b1 = hx.R1.reading_of((x[0], x[2]))
r2, b2 = hx.R2.reading_of((x[0], x[2]))
return array([r1, b1, r2, b2])
pass
def fx(x, dt):
x_est = x.copy()
x_est[0] += x[1]*dt
x_est[2] += x[3]*dt
return x_est
f = UKF(dim_x=4, dim_z=4, dt=dt, hx=hx, fx=fx, kappa=1)
aircraft = ACSim ((100,100), (0.1,0.1), 0.0)
R1 = RadarStation ((0,0), range_std=1.0, bearing_std=radians(0.5))
R2 = RadarStation ((200,0), range_std=1.0, bearing_std=radians(0.5))
hx.R1 = R1
hx.R2 = R2
f.x = array([100, 1, 100, 1])
f.R = np.diag([1.0, 0.5, 1.0, 0.5])
f.Q *= 0.0020
xs = []
track = []
for i in range(int(20/dt)):
pos = aircraft.update()
r1, b1 = R1.noisy_reading(pos)
r2, b2, = R2.noisy_reading(pos)
z = np.array([r1, b1, r2, b2])
track.append(pos.copy())
f.predict()
f.update(z)
xs.append(f.x)
xs = asarray(xs)
track = asarray(track)
time = np.arange(0,len(xs)*dt, dt)
plt.figure()
plt.subplot(411)
plt.plot(time, track[:,0])
plt.plot(time, xs[:,0])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('x position (m)')
plt.subplot(412)
plt.plot(time, track[:,1])
plt.plot(time, xs[:,3])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('y position (m)')
plt.subplot(413)
plt.plot(time, xs[:,1])
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.ylabel('x velocity (m/s)')
plt.subplot(414)
plt.plot(time, xs[:,3])
plt.ylabel('y velocity (m/s)')
plt.legend(loc=4)
plt.xlabel('time (sec)')
plt.show()