Internal Sensor

Description

The Internal Sensor is a base class for sensors that measure on-board state or environmental properties without requiring an external field of view. Unlike optical sensors that observe external targets, internal sensors measure quantities such as magnetic field strength, angular velocity, linear acceleration, or navigation solutions. This class extends the base Sensor and provides common error modeling parameters shared by its derived sensor types.

Module Implementation

Error Model Parameters

Internal sensors share a common set of error modeling parameters that modify the true measured value to produce a realistic sensor output:

Scale Factor: A multiplicative gain applied to the sensor output. A value of 1.0 represents ideal scaling; deviations model calibration errors or manufacturing tolerances.

Bias: An additive offset applied to each axis of the sensor output. The bias vector allows modeling of constant offsets that persist throughout the measurement.

Saturation: The maximum output magnitude before the sensor saturates. When the true measurement exceeds this limit, the output is clamped to $\pm M_{max}$ .

Walk Bounds: Three-sigma bounds for Gauss-Markov random walk noise. These bounds constrain the drift behavior of time-correlated noise processes used in derived sensor implementations.

The general sensor output model follows:

y = S \cdot x + b

where $y$ is the sensor output, $S$ is the scale factor, $x$ is the true value, and $b$ is the bias vector. Saturation is applied after scaling and bias.

Assumptions/Limitations

This is an abstract base class; it cannot be instantiated directly.
Units for bias, saturation, and walk bounds are sensor-specific and defined by derived classes.
The error model parameters are applied uniformly across all axes unless overridden by derived implementations.