Sum and difference monopulse for ULA
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.
estimate the DOA according to the properties of
The behavior of
step is specific to each object in
Starting in R2016b, instead of using the
H = phased.SumDifferenceMonopulseTracker creates
a tracker System object,
H. The object uses
sum and difference monopulse algorithms on a uniform linear array
H = phased.SumDifferenceMonopulseTracker( 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 (
Handle to sensor array
Specify the sensor array as a handle. The sensor array must
Signal propagation speed
Specify the propagation speed of the signal, in meters per second, as a positive scalar.
Default: Speed of light
System operating frequency
Specify the operating frequency of the system in hertz as a positive scalar. The default value corresponds to 300 MHz.
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.
|step||Perform monopulse tracking using ULA|
|Common to All System Objects|
Create System object with same property values
Expected number of inputs to a System object
Expected number of outputs of a System object
Check locked states of a System object (logical)
Allow System object property value changes
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
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
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
The conventional response of this array to any incoming plane wave is given by 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
(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
and is show in the polar plot below
The monopulse response curve is obtained by dividing the difference pattern by the sum pattern and taking the real part.
To use the monopulse response curve to obtain the arrival angle of a narrowband signal, x, compute
and invert the response curve, φ = R-1(z), to obtain φ.
The response curve is not single valued and can be inverted only when arrival angles lie within the mainlobe. The figure below shows the center portion of the monopulse response curve in the mainlobe for a 4-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 insures 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 .
 Seliktar, Y. Space-Time Adaptive Monopulse Processing. Ph.D. Thesis. Georgia Institute of Technology, Atlanta, 1998.
 Rhodes, D. Introduction to Monopulse. Dedham, MA: Artech House, 1980.
Usage notes and limitations:
See System Objects in MATLAB Code Generation (MATLAB Coder).