Namespace inertialsim::sensors¶

Namespace List > inertialsim > sensors

Sensor specification and simulation.More...

Classes¶

Type	Name
class	Accelerometer Simulate accelerometer sensors.
struct	AccelerometerData Accelerometer output data.
class	AccelerometerSpecification Accelerometer specification.
class	Gyro Simulate gyroscope (gyro) sensors.
struct	GyroData Gyro output data.
class	GyroSpecification Gyro specification.
class	IMU Simulate an Inertial Measurement Unit (IMU ).
struct	IMUData IMU output data.
struct	IMUModel IMU model options.
class	IMUSpecification IMU sensor specification.
class	INS INS sensor simulation.
struct	INSData INS simulation results.
struct	INSModel INS model options for simulation.
class	INSSpecification INS specification.
struct	INSState Simulation state for INS error parameters.
class	InertialSensor Base class for inertial sensors (gyros and accelerometers).
struct	InertialSensorModel Inertial sensor model options.
class	InertialSensorSpecification Inertial sensor specification class.
class	InertialSensorState Inertial sensor internal state.
class	Magnetometer Simulate magnetometer sensors.
struct	MagnetometerData Magnetometer output data.
class	MagnetometerSpecification Magnetometer specification.
class	Measurement Sensor measurement data.
class	Parameter <typename ValueType> A parameter with value and associated units.
class	Sensor Abstract base class for sensors.
struct	SensorModel Sensor model options.
class	SensorSpecification Base sensor specification class.
class	SensorState Sensor internal state.
class	WhiteNoiseBuffer A buffer for white noise samples.

Public Types¶

Type	Name
typedef InertialSensorModel	AccelerometerModel
enum	AttitudeFormat Attitude (orientation) output format options.
typedef InertialSensorModel	GyroModel
enum	INSSimulationMode INS simulation mode.
typedef SensorModel	MagnetometerModel
enum	SimulationMode Simulation mode for sensors.

Public Attributes¶

Type	Name
constexpr double	kDegreesCelsiusPerDegreeFahrenheit = `5.0 / 9.0`
constexpr double	kSecondsPerHour = `3600`
constexpr double	kTeslaPerGauss = `1[e](namespaceinertialsim.md#typedef-matrix)-4`

Public Functions¶

Type	Name
Array	DeltaQuantizationNoise (Eigen::Index channels, Eigen::Index samples, double sample_rate, std::mt19937_64 & rng) Generate delta (integrated) quantization noise.
	Parameter (double, const std::string &)
	Parameter (float, const std::string &)
	Parameter (int, const std::string &)
	Parameter (unsigned int, const std::string &)
	Parameter (short, const std::string &)
	Parameter (unsigned short, const std::string &)
	Parameter (long, const std::string &)
	Parameter (unsigned long, const std::string &)
	Parameter (long long, const std::string &)
	Parameter (unsigned long long, const std::string &)
	Parameter (const Eigen::ArrayBase< Derived > &, const std::string &)
	Parameter (std::initializer_list< double >, const std::string &)
	Parameter (const Eigen::MatrixBase< Derived > &, const std::string &)
	Parameter (std::initializer_list< std::initializer_list< double > >, const std::string &)
Array	PinkNoise (Eigen::Index channels, Eigen::Index samples, double sample_rate, std::mt19937_64 & rng) Generate a pink noise sequence.
Array	RandomWalkNoise (Eigen::Index channels, Eigen::Index samples, double sample_rate, std::mt19937_64 & rng) Generate a discrete time random walk sequence.
Array	RateQuantizationNoise (Eigen::Index channels, Eigen::Index samples, double sample_rate, std::mt19937_64 & rng) Generate rate quantization noise.
Array	WhiteGaussianNoise (Eigen::Index channels, Eigen::Index samples, double sample_rate, std::mt19937_64 & rng) Generate white Gaussian noise.

Detailed Description¶

The sensors namespace provides:

classes to simulate gyroscope, accelerometer, IMU, magnetometer, and INS sensor measurements from a wide variety of inputs (combinations of angular rate, specific force, acceleration, orientation, velocity, position and pose in local and global coordinate frames)
classes to specify sensor parameters in customary units with automatic conversion to SI units
classes to represent sensor simulation outputs (measurements, simulation state, and metadata)

Public Types Documentation¶

typedef AccelerometerModel¶

using inertialsim::sensors::AccelerometerModel = typedef InertialSensorModel;

enum AttitudeFormat¶

Attitude (orientation) output format options.

enum inertialsim::sensors::AttitudeFormat {
    EULER_ANGLE,
    QUATERNION,
    ROTATION_MATRIX
};

typedef GyroModel¶

using inertialsim::sensors::GyroModel = typedef InertialSensorModel;

enum INSSimulationMode¶

INS simulation mode.

enum inertialsim::sensors::INSSimulationMode {
    BATCH,
    REALTIME
};

typedef MagnetometerModel¶

using inertialsim::sensors::MagnetometerModel = typedef SensorModel;

enum SimulationMode¶

Simulation mode for sensors.

enum inertialsim::sensors::SimulationMode {
    BATCH,
    REALTIME
};

Public Attributes Documentation¶

variable kDegreesCelsiusPerDegreeFahrenheit¶

constexpr double inertialsim::sensors::kDegreesCelsiusPerDegreeFahrenheit;

variable kSecondsPerHour¶

constexpr double inertialsim::sensors::kSecondsPerHour;

variable kTeslaPerGauss¶

constexpr double inertialsim::sensors::kTeslaPerGauss;

Public Functions Documentation¶

function DeltaQuantizationNoise¶

Generate delta (integrated) quantization noise.

Array inertialsim::sensors::DeltaQuantizationNoise (
    Eigen::Index channels,
    Eigen::Index samples,
    double sample_rate,
    std::mt19937_64 & rng
)

Generate a discrete time, white noise sequence uniformly distributed between -0.5 and 0.5. White noise is characterized by a constant power spectral density over all frequencies. Given the sampling frequency in Hz, the output has a two-sided power spectral density of 1/12 (units^2)/Hz.

Parameters:

channels The number of independent channels of noise to return.
samples The number of samples of noise to return.
sample_rate Frequency in Hz that the signal is sampled at.
rng Random number generator.

Returns:

Array of random noise samples (channels x samples).

function Parameter¶

inertialsim::sensors::Parameter (
    double,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    float,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    int,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    unsigned  int,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    short,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    unsigned  short,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    long,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    unsigned  long,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    long  long,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    unsigned  long  long,
    const std::string &
)

function Parameter¶

template<typename Derived>
inertialsim::sensors::Parameter (
    const Eigen::ArrayBase< Derived > &,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    std::initializer_list< double >,
    const std::string &
)

function Parameter¶

template<typename Derived>
inertialsim::sensors::Parameter (
    const Eigen::MatrixBase< Derived > &,
    const std::string &
)

function Parameter¶

inertialsim::sensors::Parameter (
    std::initializer_list< std::initializer_list< double > >,
    const std::string &
)

function PinkNoise¶

Generate a pink noise sequence.

Array inertialsim::sensors::PinkNoise (
    Eigen::Index channels,
    Eigen::Index samples,
    double sample_rate,
    std::mt19937_64 & rng
)

Pink noise (also known as flicker noise or 1/f noise) is characterized by a power spectrum that is inversely proportional to frequency (1/f).

This implementation automatically pads the FFT size to the next efficient size (with only small prime factors: 2, 3, 5, 7) for optimal performance, then returns exactly the requested number of samples. This padding is transparent to the user and can provide significant speedup (100x+) when the requested size would otherwise have poor FFT factorization.

Parameters:

channels The number of independent channels of noise to return.
samples The number of samples of noise to return.
sample_rate Frequency in Hz that the signal is sampled at.
rng Random number generator.

Returns:

Array of random noise samples (channels x samples).

function RandomWalkNoise¶

Generate a discrete time random walk sequence.

Array inertialsim::sensors::RandomWalkNoise (
    Eigen::Index channels,
    Eigen::Index samples,
    double sample_rate,
    std::mt19937_64 & rng
)

Random walk noise (also known as Brown noise or Brownian noise) is the integral of white noise and is characterized by a 1/(f^2) power spectral density.

Parameters:

channels The number of independent channels of noise to return.
samples The number of samples of noise to return.
sample_rate Frequency in Hz that the signal is sampled at.
rng Random number generator.

Returns:

Array of random noise samples (channels x samples).

function RateQuantizationNoise¶

Generate rate quantization noise.

Array inertialsim::sensors::RateQuantizationNoise (
    Eigen::Index channels,
    Eigen::Index samples,
    double sample_rate,
    std::mt19937_64 & rng
)

Generate a discrete time, white noise sequence uniformly distributed between -0.5 and 0.5. This is the derivative of delta quantization noise.

Parameters:

channels The number of independent channels of noise to return.
samples The number of samples of noise to return.
sample_rate Frequency in Hz that the signal is sampled at.
rng Random number generator.

Returns:

Array of random noise samples (channels x samples).

function WhiteGaussianNoise¶

Generate white Gaussian noise.

Array inertialsim::sensors::WhiteGaussianNoise (
    Eigen::Index channels,
    Eigen::Index samples,
    double sample_rate,
    std::mt19937_64 & rng
)

Generate a discrete time, normally distributed, white noise sequence with unit power spectral density. White noise is characterized by a constant power spectral density over all frequencies. Given the sampling frequency in Hz, the output has a two-sided power spectral density of 1.0 (units^2)/Hz.

Parameters:

channels The number of independent channels of noise to return.
samples The number of samples of noise to return.
sample_rate Frequency in Hz that the signal is sampled at.
rng Random number generator.

Returns:

Array of random noise samples (channels x samples).

The documentation for this class was generated from the following file cpp/include/inertialsim/sensors/accelerometer.h