phased.SumDifferenceMonopulseTracker

Sum and difference monopulse for ULA

Description

The SumDifferenceMonopulseTracker object implements a sum and difference monopulse algorithm on a uniform linear array.

To estimate the direction of arrival (DOA):

Define and set up your sum and difference monopulse DOA estimator. See Construction.
Call step to estimate the DOA according to the properties of phased.SumDifferenceMonopulseTracker. The behavior of step is specific to each object in the toolbox.

Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

Construction

H = phased.SumDifferenceMonopulseTracker creates a tracker System object, H. The object uses sum and difference monopulse algorithms on a uniform linear array (ULA).

H = phased.SumDifferenceMonopulseTracker(Name,Value) creates a ULA monopulse tracker object, H, with each specified property Name set to the specified Value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

Properties

`SensorArray`	Handle to sensor array Specify the sensor array as a handle. The sensor array must be a `phased.ULA` object. Default: `phased.ULA` with default property values
`PropagationSpeed`	Signal propagation speed Specify the propagation speed of the signal, in meters per second, as a positive scalar. You can specify this property as single or double precision. Default: Speed of light
`OperatingFrequency`	System operating frequency Specify the operating frequency of the system in hertz as a positive scalar. The default value corresponds to 300 MHz. You can specify this property as single or double precision. Default: `3e8`
`NumPhaseShifterBits`	Number of phase shifter quantization bits The number of bits used to quantize the phase shift component of beamformer or steering vector weights. Specify the number of bits as a non-negative integer. A value of zero indicates that no quantization is performed. You can specify this property as single or double precision. Default: `0`

Methods

step	Perform monopulse tracking using ULA

Common to All System Objects
`release`	Allow System object property value changes

Examples

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Find Target Direction Using Monopulse Tracker

Open Live Script

Determine the direction of a target at a 60.1° broadside angle to a ULA starting with an approximate direction of 60°

array = phased.ULA('NumElements',4);
steervec = phased.SteeringVector('SensorArray',array);
tracker = phased.SumDifferenceMonopulseTracker('SensorArray',array);
x = steervec(tracker.OperatingFrequency,60.1).';
est_dir = tracker(x,60)

est_dir = 
60.1000

Algorithms

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Monopulse Algorithm

The sum-and-difference monopulse algorithm is used to the estimate the arrival direction of a narrowband signal impinging upon a uniform linear array (ULA). First, compute the conventional response of an array steered to an arrival direction φ₀. For a ULA, the arrival direction is specified by the broadside angle. To specify that the maximum response axis (MRA) point towards the φ₀ direction, set the weights to be

$w_{s} = (1, e^{i k d \sin ϕ_{0}}, e^{i k 2 d \sin ϕ_{0}}, \dots, e^{i k (N - 1) d \sin ϕ_{0}})$

where d is the element spacing and k = 2π/λ is the wavenumber. An incoming plane wave, coming from any arbitrary direction φ, is represented by

$v = (1, e^{i k d \sin ϕ}, e^{i k 2 d \sin ϕ}, \dots, e^{i k (N - 1) d \sin ϕ})$

The conventional response of this array to any incoming plane wave is given by $w_{s}^{H} v (φ)$ and is shown in the polar plot below as the Sum Pattern. The array is designed to steer towards φ₀ = 30°.

The second pattern, called the Difference Pattern, is obtained by using phased-reversed weights. The weights are determined by phase-reversing the latter half of the conventional steering vector. For an array with an even number of elements, the phase-reversed weights are

$w_{d} = - i (1, e^{i k d \sin ϕ_{0}}, e^{i k 2 d \sin ϕ_{0}}, \dots, e^{i k N / 2 d \sin ϕ_{0}}, - e^{i k (N / 2 + 1) d \sin ϕ_{0}}, \dots, - e^{i k (N - 1) d \sin ϕ_{0}})$

(For an array with an odd number of elements, the middle weight is set to zero). The multiplicative factor –i is used for convenience. The response of the difference array to the incoming vector is

$w_{d}^{H} v (φ)$

This figure shows the sum and difference beam patterns of a four-element uniform linear array (ULA) steered 30° from broadside. The array elements are spaced at one-half wavelength. The sum pattern shows that the array has its maximum response at 30° and the difference pattern has a null at 30°.

The monopulse response curve is obtained by dividing the difference pattern by the sum pattern and taking the real part.

$R (φ) = R e (\frac{w_{d}^{H} v (φ)}{w_{s}^{H} v (φ)})$

To use the monopulse response curve to obtain the arrival angle, φ, of a narrowband signal, x, compute

$z = R e (\frac{w_{d}^{H} x}{w_{s}^{H} x})$

and invert the response curve, φ = R^-1(z), to obtain φ.

The response curve is not generally single valued and can only be inverted when arrival angles lie within the main lobe where it is single valued This figure shows the monopulse response curve within the main lobe of the four-element ULA array.

There are two desirable properties of the monopulse response curve. The first is that it have a steep slope. A steep slope ensures robustness against noise. The second property is that the mainlobe be as wide as possible. A steep slope is ensure by a larger array but leads to a smaller mainlobe. You will need to trade off one property with the other.

For further details, see [1].

Data Precision

This System object supports single and double precision for input data, properties, and arguments. If the input data X is single precision, the output data is single precision. If the input data X is double precision, the output data is double precision. The precision of the output is independent of the precision of the properties and other arguments.

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.

Extended Capabilities

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

Usage notes and limitations:

See System Objects in MATLAB Code Generation (MATLAB Coder).

Version History

Introduced in R2011a

phased.SumDifferenceMonopulseTracker

Description

Construction

Properties

Methods

Examples

Find Target Direction Using Monopulse Tracker

Algorithms

Monopulse Algorithm

Data Precision

References

Extended Capabilities

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

Version History

See Also

Topics

phased.SumDifferenceMonopulseTracker

Description

Construction

Properties

Methods

Examples

Find Target Direction Using Monopulse Tracker

Algorithms

Monopulse Algorithm

Data Precision

References

Extended Capabilities

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

Version History

See Also

Topics

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