DBSCAN Clusterer

Cluster detections

Since R2021a

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Libraries:
Radar Toolbox

Description

Cluster data using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN Clusterer block can cluster any type of data. The block can also solve for the clustering threshold (epsilon) and can perform data disambiguation in two dimensions.

Examples

Automotive Adaptive Cruise Control Using FMCW and MFSK Technology

Model an automotive radar in Simulink that includes adaptive cruise control.

Open Script

Ports

Input

X — Input data

N-by-P real-valued matrix

Input data, specified as a real-valued N-by-P matrix, where N is the number of data points to cluster. P is the number of feature dimensions. The DBSCAN algorithm can cluster any type of data with appropriate Minimum number of points in a cluster and Cluster threshold epsilon settings.

Data Types: double

Update — Enable automatic update of epsilon

false (default) | true

Enable automatic update of the epsilon estimate, specified as false or true.

• When true, the epsilon threshold is first estimated as the average of the knees of the k-NN search curves. The estimate is then added to a buffer of size L, set by the Length of cluster threshold epsilon history parameter. The final value of epsilon is calculated as the average of the L-length epsilon history buffer. If Length of cluster threshold epsilon history is set to one, the estimate is memory-less. Memory-less means that each epsilon estimate is immediately used and no moving-average smoothing occurs.

• When false, a previous epsilon estimate is used. Estimating epsilon is computationally intensive and not recommended for large data sets.

Dependencies

To enable this port, set the Source of cluster threshold epsilon parameter to Auto and set the Maximum number of points for 'Auto' epsilon parameter.

Data Types: Boolean

AmbLims — Ambiguity limits

1-by-2 real-valued vector (default) | 2-by-2 real-valued matrix

Ambiguity limits, specified as a 1-by-2 real-valued vector or 2-by-2 real-valued matrix. For a single ambiguity dimension, specify the limits as a 1-by-2 vector [MinAmbiguityLimitDimension1,MaxAmbiguityLimitDimension1]. For two ambiguity dimensions, specify the limits as a 2-by-2 matrix [MinAmbiguityLimitDimension1, MaxAmbiguityLimitDimension1; MinAmbiguityLimitDimension2,MaxAmbiguityLimitDimension2].

Clustering can occur across boundaries to ensure that ambiguous detections are appropriately clustered for up to two dimensions. The ambiguous columns of the input port data X are defined using the Indices of ambiguous dimensions parameter. The AmbLims parameter defines the minimum and maximum ambiguity limits in the same units as used in the Indices of ambiguous dimensions columns of the input data X.

Dependencies

To enable this port, select the Enable disambiguation of dimensions check box.

Data Types: double

Output

Idx — Cluster indices

N-by-1 integer-valued column vector

Cluster indices, returned as an N-by-1 integer-valued column vector. Cluster IDs represent the clustering results of the DBSCAN algorithm. A value equal to '-1' implies a DBSCAN noise point. Positive Idx values correspond to clusters that satisfy the DBSCAN clustering criteria.

Dependencies

To enable this port, set the Define outputs for Simulink block parameter to Index or Index and ID.

Data Types: double

Clusters — Alternative cluster IDs

1-by-N integer-valued row vector

Alternative cluster IDs, returned as a 1-by-N row vector of positive integers. Each value is a unique identifier indicating a hypothetical target cluster. This argument contains unique positive cluster IDs for all points including noise. In contrast, the Idx output argument labels noise points with '–1'. Use this output as input to Phased Array System Toolbox™ blocks such as Range Estimator and Doppler Estimator.

Dependencies

To enable this port, set the Define outputs for Simulink block parameter to Cluster ID or Index and ID.

Data Types: double

Parameters

Define outputs for Simulink block — Type of cluster data output

Index and ID (default) | Cluster ID | Index

Type of cluster data output, specified as:.

• Index and ID –- Enables the Idx and Clusters output ports.

• Cluster ID –- Enables the Clusters output port only.

• Index –- Enables the Idx output port only.

Source of cluster threshold epsilon — Epsilon source

Property (default) | Auto

Epsilon source for cluster threshold:

• Property — Epsilon is obtained from the Cluster threshold epsilon parameter.

• Auto — Epsilon is estimated automatically using a k-nearest neighbor (k-NN) search. The search is calculated with k ranging from one less than the value of Minimum number of points in a cluster to one less than the value of Maximum number of points for 'Auto' epsilon. The subtraction of one is needed because the neighborhood of a point includes the point itself.

Cluster threshold epsilon — Cluster neighborhood size

10.0 (default) | positive scalar | positive real-valued 1-by-P row vector

Cluster neighborhood size for a search query, specified as a positive scalar or real-valued 1-by-P row vector. P is the number of clustering dimensions in the input data X.

Epsilon defines the radius around a point inside which to count the number of detections. When epsilon is a scalar, the same value applies to all clustering feature dimensions. You can specify different epsilon values for different clustering dimensions by specifying a real-valued 1-by-P row vector. Using a row vector creates a multi-dimensional ellipse search area, which is useful when the data columns have different physical meanings such as range and Doppler.

Minimum number of points in a cluster — Minimum number of points required for cluster

3 (default) | positive integer

Minimum number of points required for a cluster, specified as a positive integer. This parameter defines the minimum number of points in a cluster when determining whether a point is a core point.

Maximum number of points for 'Auto' epsilon — Maximum number of points required for cluster

10 (default) | positive integer

Maximum number of points in a cluster, specified as a positive integer. This property is used to estimate epsilon when the object performs a k-NN search.

Dependencies

To enable this parameter, set the Source of cluster threshold epsilon parameter to Auto.

Length of cluster threshold epsilon history — Length of cluster threshold epsilon history

10 (default) | positive integer

Length of the stored cluster threshold epsilon history, specified as a positive integer. When set to one, the history is memory-less. Then, each epsilon estimate is immediately used and no moving-average smoothing occurs. When greater than one, the epsilon value is averaged over the history length specified.

Example: 5

Data Types: double

Enable disambiguation of dimensions — Turn on disambiguation

off (default) | on

Check box to enable disambiguation of dimensions, specified as false or true. When checked, clustering occurs across boundaries defined by the values in the input port AmbLims at execution. Ambiguous detections are appropriately clustered. Use the Indices of ambiguous dimensions parameter to specify those column indices of X in which ambiguities can occur. Up to two ambiguous dimensions are permitted. Turning on disambiguation is not recommended for large data sets.

Data Types: Boolean

Indices of ambiguous dimensions — Indices of ambiguous dimensions

1 (default) | positive integer | 1-by-2 vector of positive integers

Indices of ambiguous dimensions, specified as a positive integer or 1-by-2 vector of positive integers. This property specifies the column indices of the input port data X in which disambiguation can occur. A positive integer corresponds to a single ambiguous dimension in the input data matrix X. A 1-by-2 length row vector of indices corresponds to two ambiguous dimensions. The size and order of Indices of ambiguous dimensions must be consistent with the AmbLims input port value.

Example: [3 4]

Dependencies

To enable this parameter, select the Enable disambiguation of dimensions check box.

Data Types: double

Simulate using — Block simulation method

Interpreted Execution (default) | Code Generation

Block simulation, specified as Interpreted Execution or Code Generation. If you want your block to use the MATLAB^® interpreter, choose Interpreted Execution. If you want your block to run as compiled code, choose Code Generation. Compiled code requires time to compile but usually runs faster.

Interpreted execution is useful when you are developing and tuning a model. The block runs the underlying System object™ in MATLAB. You can change and execute your model quickly. When you are satisfied with your results, you can then run the block using Code Generation. Long simulations run faster with generated code than in interpreted execution. You can run repeated executions without recompiling, but if you change any block parameters, then the block automatically recompiles before execution.

This table shows how the Simulate using parameter affects the overall simulation behavior.

When the Simulink^® model is in Accelerator mode, the block mode specified using Simulate using overrides the simulation mode.

Acceleration Modes

Block Simulation	Simulation Behavior
	Normal	Accelerator	Rapid Accelerator
Interpreted Execution	The block executes using the MATLAB interpreter.	The block executes using the MATLAB interpreter.	Creates a standalone executable from the model.
Code Generation	The block is compiled.	All blocks in the model are compiled.

For more information, see Choosing a Simulation Mode (Simulink).

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2021a