Barometer

Barometer sensor model with noise

Since R2025a

• 

Libraries:
UAV Toolbox / UAV Scenario and Sensor Modeling

Description

The Barometer block models a barometer sensor that generates an air pressure reading with a measurement noise that consists of constant measurement bias, uncorrelated white noise, and correlated noise [1]. For more information on the noise model, see Noise Model.

Examples

Generate Air Pressure Reading from Altitude

Open the barometerSimulation.slx Simulink model

open_system("barometerSimulation");

The model consists of the following blocks:

1. Lapse Rate Model (Aerospace Blockset) — Simulates lapse rate atmospheric model. The block receives altitude in meter, and outputs the atmospheric air pressure in Pascal, which is then measured by the barometer sensor. In this model, the input altitude is 50 meter.

2. Rate Transition — Simulates the discrete measurement of the Barometer sensor. In this model, the block is configured with a sample time of 0.1 second.

3. Barometer — Simulates a barometer sensor that generates air pressure reading in Pascal with a measurement noise that consists of constant measurement bias, uncorrelated white noise, and correlated noise.

4. Pressure Altitude (Aerospace Blockset) — Computes the altitude above mean sea level in meters based on the air pressure reading.

5. Measured Pressure Scope — Visualizes the pressure reading.

6. Altitude Scope — Visualizes the computed altitude above mean sea level and ground truth.

Run the model, and then open scope blocks to see the simulation results.

Ports

Input

True pressure — True air pressure

scalar | vector

True air pressure in Pa, specified as a scalar or N-element vector, where N is the number of air pressure measurements.

Data Types: single | double

Output

Measured pressure — Measured air pressure

scalar | vector

Measured air pressure in Pa, returned as a nonnegative scalar or N-element vector. The length of Measured pressure matches the length of True pressure.

Data Types: single | double

Parameters

Constant bias — Constant measurement bias

0 (default) | finite scalar

Specify the constant measurement bias as a finite scalar, in Pa.

Noise density — White noise standard variance

1 (default) | nonnegative finite scalar

Specify the white noise standard variance as a nonnegative finite scalar. This parameter specifies the power spectral density of the sensor noise in Pa/√Hz.

Bias instability — Correlated noise standard variance

1 (default) | nonnegative finite scalar

Specify the correlated noise standard variance as a nonnegative finite scalar, in Pa

Decay factor — Correlated noise decay factor

0.99 (default) | positive scalar in the range (0,1)

Specify the correlated noise decay factor as a scalar in the range (0, 1).

Specifying a smaller decay factor models the correlated noise closer to white noise, while specifying a larger decay factor models the correlated noise closer to a random walk process.

Seed — Random number initial seed

67 (default) | nonnegative integer scalar

Specify the Initial seed of the mt19937ar random number generator algorithm as a real, nonnegative integer scalar.

Simulate using — Type of simulation to run

Interpreted execution (default) | Code generation

Select the type of simulation to run from these options:

• Interpreted execution — Simulate the model using the MATLAB^® interpreter. For more information, see Interpreted Execution vs. Code Generation (Simulink).

• Code generation — Simulate the model using generated C code. The first time you run a simulation, Simulink^® generates C code for the block. The C code is reused for subsequent simulations, as long as the model does not change.

Algorithms

Noise Model

This equation represents the measurement noise that is added to the true air pressure:

$$n(t)=n_c(t)+n_u(t)+b.$$

b is the constant measurement bias that you set by using the ConstantBias property. n_u(t) is the uncorrelated noise that is modeled as white Gaussian noise with zero mean and variance that you set by using the NoiseDensity property. n_c(t) is the correlated noise given by this equation:

$$n_c\left(N+1\right)=D{n}_c\left(N\right)+\left(1-D\right)\sqrt{\frac{-2}{\log \left(D\right)}}Bw,$$

where:

• n_c(N) is the correlated noise at the N^th time step.

• w is the white Gaussian noise with unit variance.

• D is the decay factor that you set by using the DecayFactor property.

• B is the bias instability that you set by using the BiasInstability property.

References

[1] Sabatini, A.M., and V. Genovese. “A Stochastic Approach to Noise Modeling for Barometric Altimeters.” Sensors 13 (November 2013): 15692-15707. https://doi.org/10.3390/s131115692

Version History

Introduced in R2025a