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sensors module

Sensors module for attitude determination and control system.

This module models the behavior of a suite of sensors and contains classes that abstract gyroscopes, Earth horizon sensors, and magnetometers in order to produce attitude and angular velocity estimates.

class sensors.EarthHorizonSensor(accuracy)

Bases: object

A class to store Earth horizon sensor parameters and methods

Parameters:resolution (float) – the accuracy of the sensor in degrees; an accuracy of 0 means no measurement noise is applied (it is a perfect sensor)
noise_vals

numpy ndarray – a cache of pre-generated noise values to aid in the addition of noise

estimate_earth_direction(q_actual, r, t, delta_t)

Estimates the direction vector to the Earth

Parameters:
  • q_actual (numpy ndarray) – the actual quaternion representing the attitude (from the inertial to body frame) of the spacecraft
  • r (numpy ndarray) – the inertial position of the spacecraft (m)
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the direction vector to the Earth as measured by the

sensor in body coordinates
Return type:

numpy ndarray
get_noise(t, delta_t)

Gets the Gaussian noise for a set of x, y, and z direction measurements

NOTE: This method uses a cache of random values generated in this
class’s constructor. This is done to (1) reduce the overhead of many individual rvs calls and (2) ensure that all adaptive integrator steps in between user-defined steps use the same noise value (so that the dynamics are not constantly changing). Without this, the integrator fails to move forward in time. Therefore, a custom hash function is used to apply the same noise is necessary cases.
Parameters:
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the noise vector (noise in x, y, and z)
Return type:

numpy ndarray
class sensors.Gyros(bias_stability, angular_random_walk)

Bases: object

A class to store gyroscope parameters and methods

Parameters:
  • bias_stability (float) – the bias stability of the gyro in deg/hr
  • angular_random_walk (float) – the angular random walk of the gyro in deg/sqrt(hr)
bias

numpy ndarray – the biases of the gyros throughout the duration of a simulation (3x1)

noise_vals

numpy ndarray – a cache of pre-generated noise values to aid in the addition of noise

estimate_angular_velocity(w_actual, t, delta_t)

Provides an estimated angular velocity (adding noise & bias to the actual)

Parameters:
  • w_actual (numpy ndarray) – the actual angular velocity in rad/s
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the estimated angular velocity in rad/s
Return type:

numpy ndarray
get_noise(t, delta_t)

Gets the Gaussian noise for a set of x, y, and z gyro measurements

NOTE: This method uses a cache of random values generated in this
class’s constructor. This is done to (1) reduce the overhead of many individual rvs calls and (2) ensure that all adaptive integrator steps in between user-defined steps use the same noise value (so that the dynamics are not constantly changing). Without this, the integrator fails to move forward in time. Therefore, a custom hash function is used to apply the same noise is necessary cases.
Parameters:
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the noise vector (noise in x, y, and z)
Return type:

numpy ndarray
class sensors.Magnetometer(resolution=0)

Bases: object

A class to store magnetometer parameters and methods

Parameters:resolution (float) – the resolution of the magnetometer in Tesla; a resolution of 0 T means no measurement noise (it is a perfect sensor)
noise_vals

numpy ndarray – a cache of pre-generated noise values to aid in the addition of noise

estimate_magnetic_field(q_actual, r, t, delta_t)

Estimates the local magnetic field in body coordinates

Parameters:
  • q_actual (numpy ndarray) – the actual quaternion representing the attitude (from the inertial to body frame) of the spacecraft
  • r (numpy ndarray) – the inertial position of the spacecraft (m)
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the vector representing the local magnetic field

measured in body coordinates (with measurement noise applied)
Return type:

numpy ndarray
get_noise(t, delta_t)

Gets the Gaussian noise for a set of x, y, and z magnetic field measurements

NOTE: This method uses a cache of random values generated in this
class’s constructor. This is done to (1) reduce the overhead of many individual rvs calls and (2) ensure that all adaptive integrator steps in between user-defined steps use the same noise value (so that the dynamics are not constantly changing). Without this, the integrator fails to move forward in time. Therefore, a custom hash function is used to apply the same noise is necessary cases.
Parameters:
  • t (float) – the current simulation time in seconds
  • delta_t (float) – the time between user-defined integrator steps (not the internal/adaptive integrator steps) in seconds
Returns:

the noise vector (noise in x, y, and z)
Return type:

numpy ndarray
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