import numpy as np
from pymrt.tracking.utils import GaussianComponent

# Dimensionality
n = 2

# 12 targets, 2D Constant Velocity Model
n_targets = 12
x_init = np.zeros((n_targets, 5))
x_init[ 0, :] = [   0,    0,    0, -10, 0]
x_init[ 1, :] = [ 400, -600,  -10,   5, 1]
x_init[ 2, :] = [-800, -200,   20,  -5, 2]
x_init[ 3, :] = [ 400, -600,   -7,  -4, 3]
x_init[ 4, :] = [ 400, -600, -2.5,  10, 4]
x_init[ 5, :] = [   0,    0,  7.5,  -5, 5]
x_init[ 6, :] = [-800, -200,   12,   7, 6]
x_init[ 7, :] = [-200,  800,   15, -10, 7]
x_init[ 8, :] = [-800, -200,    3,  15, 8]
x_init[ 9, :] = [-200,  800,   -3, -15, 9]
x_init[10, :] = [   0,    0,  -20, -15, 10]
x_init[11, :] = [-200,  800,   15,  -5, 11]

# Total 100 steps
n_steps = 100
birth = np.zeros((n_targets, 1), np.int)
# #target      0  1  2   3   4   5   6   7   8   9  10  11
birth[:, 0] = [1, 1, 1, 20, 20, 20, 40, 40, 60, 60, 80, 80]

death = np.zeros((n_targets, 1), np.int)
death[:, :] = n_steps
death[0, 0] = 70
death[2, 0] = 70

# STD of CV disturbance on state updates
w_cov_std = 1
# STD of CV disturbance on measurements
r_cov_std = 5
# Lambda parameter of clutter process - on average, 10 false-alarms per
# observation
poisson_lambda = 10

# Birth probability
model_birth = [
    GaussianComponent(n=5,
                      mean=np.array([0., 0., 0., 0., 0.]).T,
                      cov=np.eye(5)*100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([400., -600., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([-800., -200., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([-200., 800., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
]

2421d40ecadc4f136ba2679bceaad0d96d18e26a
<mask token>

<mask token>
n = 2
n_targets = 12
x_init = np.zeros((n_targets, 5))
x_init[0, :] = [0, 0, 0, -10, 0]
x_init[1, :] = [400, -600, -10, 5, 1]
x_init[2, :] = [-800, -200, 20, -5, 2]
x_init[3, :] = [400, -600, -7, -4, 3]
x_init[4, :] = [400, -600, -2.5, 10, 4]
x_init[5, :] = [0, 0, 7.5, -5, 5]
x_init[6, :] = [-800, -200, 12, 7, 6]
x_init[7, :] = [-200, 800, 15, -10, 7]
x_init[8, :] = [-800, -200, 3, 15, 8]
x_init[9, :] = [-200, 800, -3, -15, 9]
x_init[10, :] = [0, 0, -20, -15, 10]
x_init[11, :] = [-200, 800, 15, -5, 11]
n_steps = 100
birth = np.zeros((n_targets, 1), np.int)
birth[:, 0] = [1, 1, 1, 20, 20, 20, 40, 40, 60, 60, 80, 80]
death = np.zeros((n_targets, 1), np.int)
death[:, :] = n_steps
death[0, 0] = 70
death[2, 0] = 70
w_cov_std = 1
r_cov_std = 5
poisson_lambda = 10
model_birth = [GaussianComponent(n=5, mean=np.array([0.0, 0.0, 0.0, 0.0, 
    0.0]).T, cov=np.eye(5) * 100, weight=0.03), GaussianComponent(n=5, mean
    =np.array([400.0, -600.0, 0.0, 0.0, 0.0]).T, cov=np.eye(5) * 100,
    weight=0.03), GaussianComponent(n=5, mean=np.array([-800.0, -200.0, 0.0,
    0.0, 0.0]).T, cov=np.eye(5) * 100, weight=0.03), GaussianComponent(n=5,
    mean=np.array([-200.0, 800.0, 0.0, 0.0, 0.0]).T, cov=np.eye(5) * 100,
    weight=0.03)]

import numpy as np
from pymrt.tracking.utils import GaussianComponent
n = 2
n_targets = 12
x_init = np.zeros((n_targets, 5))
x_init[0, :] = [0, 0, 0, -10, 0]
x_init[1, :] = [400, -600, -10, 5, 1]
x_init[2, :] = [-800, -200, 20, -5, 2]
x_init[3, :] = [400, -600, -7, -4, 3]
x_init[4, :] = [400, -600, -2.5, 10, 4]
x_init[5, :] = [0, 0, 7.5, -5, 5]
x_init[6, :] = [-800, -200, 12, 7, 6]
x_init[7, :] = [-200, 800, 15, -10, 7]
x_init[8, :] = [-800, -200, 3, 15, 8]
x_init[9, :] = [-200, 800, -3, -15, 9]
x_init[10, :] = [0, 0, -20, -15, 10]
x_init[11, :] = [-200, 800, 15, -5, 11]
n_steps = 100
birth = np.zeros((n_targets, 1), np.int)
birth[:, 0] = [1, 1, 1, 20, 20, 20, 40, 40, 60, 60, 80, 80]
death = np.zeros((n_targets, 1), np.int)
death[:, :] = n_steps
death[0, 0] = 70
death[2, 0] = 70
w_cov_std = 1
r_cov_std = 5
poisson_lambda = 10
model_birth = [GaussianComponent(n=5, mean=np.array([0.0, 0.0, 0.0, 0.0, 
    0.0]).T, cov=np.eye(5) * 100, weight=0.03), GaussianComponent(n=5, mean
    =np.array([400.0, -600.0, 0.0, 0.0, 0.0]).T, cov=np.eye(5) * 100,
    weight=0.03), GaussianComponent(n=5, mean=np.array([-800.0, -200.0, 0.0,
    0.0, 0.0]).T, cov=np.eye(5) * 100, weight=0.03), GaussianComponent(n=5,
    mean=np.array([-200.0, 800.0, 0.0, 0.0, 0.0]).T, cov=np.eye(5) * 100,
    weight=0.03)]

import numpy as np
from pymrt.tracking.utils import GaussianComponent

# Dimensionality
n = 2

# 12 targets, 2D Constant Velocity Model
n_targets = 12
x_init = np.zeros((n_targets, 5))
x_init[ 0, :] = [   0,    0,    0, -10, 0]
x_init[ 1, :] = [ 400, -600,  -10,   5, 1]
x_init[ 2, :] = [-800, -200,   20,  -5, 2]
x_init[ 3, :] = [ 400, -600,   -7,  -4, 3]
x_init[ 4, :] = [ 400, -600, -2.5,  10, 4]
x_init[ 5, :] = [   0,    0,  7.5,  -5, 5]
x_init[ 6, :] = [-800, -200,   12,   7, 6]
x_init[ 7, :] = [-200,  800,   15, -10, 7]
x_init[ 8, :] = [-800, -200,    3,  15, 8]
x_init[ 9, :] = [-200,  800,   -3, -15, 9]
x_init[10, :] = [   0,    0,  -20, -15, 10]
x_init[11, :] = [-200,  800,   15,  -5, 11]

# Total 100 steps
n_steps = 100
birth = np.zeros((n_targets, 1), np.int)
# #target      0  1  2   3   4   5   6   7   8   9  10  11
birth[:, 0] = [1, 1, 1, 20, 20, 20, 40, 40, 60, 60, 80, 80]

death = np.zeros((n_targets, 1), np.int)
death[:, :] = n_steps
death[0, 0] = 70
death[2, 0] = 70

# STD of CV disturbance on state updates
w_cov_std = 1
# STD of CV disturbance on measurements
r_cov_std = 5
# Lambda parameter of clutter process - on average, 10 false-alarms per
# observation
poisson_lambda = 10

# Birth probability
model_birth = [
    GaussianComponent(n=5,
                      mean=np.array([0., 0., 0., 0., 0.]).T,
                      cov=np.eye(5)*100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([400., -600., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([-800., -200., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
    GaussianComponent(n=5,
                      mean=np.array([-200., 800., 0., 0., 0.]).T,
                      cov=np.eye(5) * 100,
                      weight=0.03),
]
