step

System object: phased.SumDifferenceMonopulseTracker
Package: phased

Perform monopulse tracking using ULA

Syntax

ESTANG = step(H,X,STANG)

Description

ESTANG = step(H,X,STANG) estimates the incoming direction ESTANG of the input signal, X, based on an initial guess of the direction.

    Note:   H specifies the System object™ on which to run this step method.

    The object performs an initialization the first time the step method is executed. This initialization locks nontunable properties and input specifications, such as dimensions, complexity, and data type of the input data. If you change a nontunable property or an input specification, the System object issues an error. To change nontunable properties or inputs, you must first call the release method to unlock the object.

Input Arguments

H

Tracker object of type phased.SumDifferenceMonopulseTracker.

X

Input signal, specified as a row vector whose number of columns corresponds to number of channels.

STANG

Initial guess of the direction, specified as a scalar that represents the broadside angle in degrees. A typical initial guess is the current steering angle. The value of STANG is between –90 and 90. The angle is defined in the array's local coordinate system. For details regarding the local coordinate system of the ULA, type phased.ULA.coordinateSystemInfo.

Output Arguments

ESTANG

Estimate of incoming direction, returned as a scalar that represents the broadside angle in degrees. The value is between –90 and 90. The angle is defined in the array's local coordinate system.

Examples

Determine the direction of a target at around 60 degrees broadside angle of a ULA.

ha = phased.ULA('NumElements',4);
hstv = phased.SteeringVector('SensorArray',ha);
hmp = phased.SumDifferenceMonopulseTracker('SensorArray',ha);
x = step(hstv,hmp.OperatingFrequency,60.1).';
est_dir = step(hmp,x,60);

Algorithms

The tracker uses a sum-and-difference monopulse algorithm to estimate the direction. The tracker obtains the difference steering vector by phase-reversing the latter half of the sum steering vector.

For further details, see [1].

References

[1] Seliktar, Y. Space-Time Adaptive Monopulse Processing. Ph.D. Thesis. Georgia Institute of Technology, Atlanta, 1998.

[2] Rhodes, D. Introduction to Monopulse. Dedham, MA: Artech House, 1980.

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