Barometer

R2026b

Model barometer sensor with measurement noise for UAV altitude estimation

Since R2025a

• 

Barometer block

To add a block to a model, double-click the canvas and start typing the block name. Then, select the block from the list.

Libraries:
UAV Toolbox / UAV Scenario and Sensor Modeling

Description

The Barometer block models a barometer sensor that adds measurement noise to the true air pressure input. The noise consists of three additive components: a constant bias offset, uncorrelated white Gaussian noise (scaled by the Noise density parameter), and correlated noise modeled as a first-order autoregressive process (controlled by the Bias instability and Decay factor parameters) [1]. For the noise equations, see Noise Model. Use this block to simulate barometric pressure readings for UAV altitude estimation in the presence of sensor noise. For MATLAB script-based workflows, use the barometerSensor System object instead.

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. In a typical UAV simulation, connect this port to an atmosphere model or a pressure-from-altitude computation.

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. Use this output as the sensor input to an altitude estimation or state estimation algorithm.

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 — Standard deviation of the correlated noise component (a slowly drifting offset modeled as a first-order autoregressive process)

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).

A smaller decay factor models the correlated noise closer to independent white noise at each time step. A larger decay factor models the correlated noise closer to a random walk (unbounded drift).

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 later simulations, as long as the model does not change.

Tips

To configure the block for a specific barometer sensor, obtain these values from the sensor datasheet:

• Set Noise density to the sensor noise spectral density (typically listed in Pa/√Hz or equivalent units on the datasheet).

• Set Bias instability to the long-term bias stability specification.

• Set Decay factor to a value near 1 (such as 0.99) for sensors with slow-drifting bias. Use a smaller value for sensors with faster-varying bias.

• Set Constant bias to 0 for a calibrated sensor, or to the expected residual offset.

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