UniformPoissonModel

Uniform Poisson clutter model

Since R2025a

Description

A UniformPoissonModel object contains a clutter model that assumes a Poisson distribution of clutter with a uniform clutter density. A Poisson distribution of clutter is a common assumption in many multi-object tracking algorithms. In this model, the clutter is uniformly distributed across the sensor's entire measurement space. You can use this model to define the clutter model for a CustomSensor object.

Creation

To create a UniformPoissonModel object, use the sensorClutterModel function with the "uniform-poisson" input argument. For example:

clutterModel = sensorClutterModel("uniform-poisson")

Properties

ClutterDensity — Spatial density of clutter measurements

1e-6 (default) | positive scalar

Spatial density of clutter measurements, specified as a positive scalar.

clutterDensity is defined as the number of clutter measurements per unit volume of the measurement space. The volume of measurement space is connected to the measurement model of the sensor. For example, if a radar reports azimuth and range measurements, its clutter density can be calculated as:

$$\text{ClutterDensity}=\frac{\text{FlaseAlarmRate}}{\text{AzimuthResolution}\cdot \text{RangeResolution}}$$

The denominator represents the volume of one resolution cell of the radar. The FalseAlarmRate specifies the average number of false alarms appearing in one resolution cell per update. Similarly, if you would like to calculate clutter density of a camera reporting bounding box as [u;v;w;h], it can be computed as:

$$\text{ClutterDensity}=\frac{\text{NumFalseAlarmsPerImage}}{\max U\cdot \max V\cdot \max W\cdot \max H}$$

The denominator represents the volume of the measurement space assuming minimum values of each measurement is 0. The numerator specifies the number of false alarms detected on average in one image.

Example: 5e-6

Version History

Introduced in R2025a