c4dynamics.sensors.radar.radar.scale_factor

property radar.scale_factor: float

Gets and sets the object’s scale_factor.

The scale factor is a random variable generated once at the stage of constructing the instance by the errors model:

\[scalefactor = std \cdot randn\]

Where scale_factor_std is a parameter with default value of 0.05 (5%) for seeker object, and 0.07 (7%) for radar object.

To get the scale factor generated by the errors model, or to override it, the user may call scale_factor to get or set the final scale factor error.

Parameters:

scale_factor (float) – Required scale factor, [dimensionless].

Returns:

scale_factor (float) – Current scale factor, [dimensionless].

Example

The following example for a seeker object is directly applicable to a radar object too. Simply replace c4d.sensors.seeker(...) with c4d.sensors.radar(...).

Required packages:

>>> import c4dynamics as c4d
>>> from matplotlib import pyplot as plt
>>> import numpy as np

Settings and initial conditions:

(see seeker or radar examples for more details):

Target:

>>> tgt = c4d.datapoint(x = 1000, y = 0, vx = -80 * c4d.kmh2ms, vy = 10 * c4d.kmh2ms)
>>> for t in np.arange(0, 60, 0.01):
...   tgt.inteqm(np.zeros(3), .01) 
...   tgt.store(t)

Origin:

>>> pedestal = c4d.rigidbody(z = 30, theta = -1 * c4d.d2r)

Ground truth reference:

>>> skr_ideal = c4d.sensors.seeker(origin = pedestal, isideal = True)

Tracking with scale factor

Define a seeker with a scale factor error only (mute the bias and the noise) and set it to 1.2 (= 20%) to track a constant velocity target:

>>> skr = c4d.sensors.seeker(origin = pedestal, bias_std = 0, noise_std = 0)
>>> skr.scale_factor = 1.2
>>> for x in tgt.data():
...   skr_ideal.measure(c4d.create(x[1:]), t = x[0], store = True)  
...   skr.measure(c4d.create(x[1:]), t = x[0], store = True)    

Compare with the true target angles (ideal seeker):

>>> ax = plt.subplot()
>>> ax.plot(*skr_ideal.data('az', scale = c4d.r2d), label = 'target')  
>>> ax.plot(*skr.data('az', scale = c4d.r2d), label = 'seeker')