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phased.RangeEstimator System object

Range estimation

Description

The phased.RangeEstimator System object™ estimates the ranges of targets. Input to the estimator consists of a range-response or range-Doppler response data cube, and detection locations from a detector. When information about clusters of detections is available, the ranges are computed using cluster information. Clustering associates multiple detections into one extended detection.

To compute the detections for a range-response or range-Doppler cube:

  1. Define and set up a range estimator using the Construction procedure that follows.

  2. Call the step method to compute the range, using the properties you specify for the phased.RangeEstimator System object.

Note

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

estimator = phased.RangeEstimator creates a range estimator System object, estimator.

estimator = phased.RangeEstimator(Name,Value) creates a System object, estimator, with each specified property Name set to the specified Value. You can specify additional name and value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

Properties

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Source of the number of range estimates to report, specified as 'Auto' or 'Property'.

If you set this property to 'Auto', the number of reported estimates is determined from the number of columns in the detidx input to the step method. If cluster IDs are provided, the number of estimates is determined from the number of unique cluster IDs in the clusterids input to the step method.

If you set this property to 'Property', the number of reported estimates is obtained from the value of the NumEstimates property.

Data Types: char

The maximum number of range estimates to report, specified as a positive integer. The number of requested estimates can be greater than the number of columns in the detidx argument or the number of unique IDs in the clusterids argument of the step method. In that case, the remainder is filled with NaN.

Dependencies

To enable this property, set the NumEstimatesSource property to 'Property'.

Data Types: double

Option to accept cluster IDs as an input argument to the step method, specified as false or true. Setting this property to true enables the clusterids input argument.

Data Types: logical

Option to enable output of range estimate variances, specified as false or true. Range variances are returned by the rngvar output argument of the step method.

Data Types: logical

Root-mean-square range resolution of the detection, specified as a positive scalar. The value of the RMSResolution must have the same units as the rangegrid input argument of the step method.

Dependencies

To enable this property, set the value of the VarianceOutputPort property to true.

Data Types: double

Source of noise power values, specified as 'Property' or 'Input port'. Noise power is used to compute range estimation variance and SNR. If you set this property to 'Property', the value of the NoisePower property represents the noise power at the detection locations. If you set this property to 'Input port', you can specify noise power using the noisepower input argument, of the step method.

Data Types: char

Constant noise power value over the range-response or range-Doppler response data cube, specified as a positive real scalar. Noise power units are linear. The same noise power value is applied to all detections.

Dependencies

To enable this property, set the value of the VarianceOutputPort property to true and set NoisePowerSource to 'Property'.

Data Types: double

Methods

stepEstimate target range
Common to All System Objects
clone

Create System object with same property values

getNumInputs

Expected number of inputs to a System object

getNumOutputs

Expected number of outputs of a System object

isLocked

Check locked states of a System object (logical)

release

Allow System object property value changes

Examples

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To estimate the range and speed of three targets, create a range-Doppler map using the phased.RangeDopplerResponse System object™. Then use the phased.RangeEstimator and phased.DopplerEstimator System objects to estimate range and speed. The transmitter and receiver are collocated isotropic antenna elements forming a monostatic radar system.

The transmitted signal is a linear FM waveform with a pulse repetition interval (PRI) of 7.0 μs and a duty cycle of 2%. The operating frequency is 77 GHz and the sample rate is 150 MHz.

fs = 150e6;
c = physconst('LightSpeed');
fc = 77.0e9;
pri = 7e-6;
prf = 1/pri;

Set up the scenario parameters. The transmitter and receiver are stationary and located at the origin. The targets are 500, 530, and 750 meters from the radar along the x-axis. The targets move along the x-axis at speeds of –60, 20, and 40 m/s. All three targets have a nonfluctuating radar cross-section (RCS) of 10 dB. Create the target and radar platforms.

Numtgts = 3;
tgtpos = zeros(Numtgts);
tgtpos(1,:) = [500 530 750];
tgtvel = zeros(3,Numtgts);
tgtvel(1,:) = [-60 20 40];
tgtrcs = db2pow(10)*[1 1 1];
tgtmotion = phased.Platform(tgtpos,tgtvel);
target = phased.RadarTarget('PropagationSpeed',c,'OperatingFrequency',fc, ...
    'MeanRCS',tgtrcs);
radarpos = [0;0;0];
radarvel = [0;0;0];
radarmotion = phased.Platform(radarpos,radarvel);

Create the transmitter and receiver antennas.

txantenna = phased.IsotropicAntennaElement;
rxantenna = clone(txantenna);

Set up the transmitter-end signal processing. Create an upsweep linear FM signal with a bandwidth of one half the sample rate. Find the length of the PRI in samples and then estimate the rms bandwidth and range resolution.

bw = fs/2;
waveform = phased.LinearFMWaveform('SampleRate',fs, ...
    'PRF',prf,'OutputFormat','Pulses','NumPulses',1,'SweepBandwidth',fs/2, ...
    'DurationSpecification','Duty cycle','DutyCycle',0.02);
sig = waveform();
Nr = length(sig);
bwrms = bandwidth(waveform)/sqrt(12);
rngrms = c/bwrms;

Set up the transmitter and radiator System object properties. The peak output power is 10 W and the transmitter gain is 36 dB.

peakpower = 10;
txgain = 36.0;
txgain = 36.0;
transmitter = phased.Transmitter( ...
    'PeakPower',peakpower, ...
    'Gain',txgain, ...
    'InUseOutputPort',true);
radiator = phased.Radiator( ...
    'Sensor',txantenna,...
    'PropagationSpeed',c,...
    'OperatingFrequency',fc);

Set up the free-space channel in two-way propagation mode.

channel = phased.FreeSpace( ...
    'SampleRate',fs, ...
    'PropagationSpeed',c, ...
    'OperatingFrequency',fc, ...
    'TwoWayPropagation',true);

Set up the receiver-end processing. Set the receiver gain and noise figure.

collector = phased.Collector( ...
    'Sensor',rxantenna, ...
    'PropagationSpeed',c, ...
    'OperatingFrequency',fc);
rxgain = 42.0;
noisefig = 1;
receiver = phased.ReceiverPreamp( ...
    'SampleRate',fs, ...
    'Gain',rxgain, ...
    'NoiseFigure',noisefig);

Loop over the pulses to create a data cube of 128 pulses. For each step of the loop, move the target and propagate the signal. Then put the received signal into the data cube. The data cube contains the received signal per pulse. Ordinarily, a data cube has three dimensions where the last dimension corresponds to antennas or beams. Because only one sensor is used, the cube has only two dimensions.

The processing steps are:

  1. Move the radar and targets.

  2. Transmit a waveform.

  3. Propagate the waveform signal to the target.

  4. Reflect the signal from the target.

  5. Propagate the waveform back to the radar. Two-way propagation enables enables you to combine the return propagation with the outbound propagation.

  6. Receive the signal at the radar.

  7. Load the signal into the data cube.

Np = 128;
dt = pri;
cube = zeros(Nr,Np);
for n = 1:Np
    [sensorpos,sensorvel] = radarmotion(dt);
    [tgtpos,tgtvel] = tgtmotion(dt);
    [tgtrng,tgtang] = rangeangle(tgtpos,sensorpos);
    sig = waveform();
    [txsig,txstatus] = transmitter(sig);
    txsig = radiator(txsig,tgtang);
    txsig = channel(txsig,sensorpos,tgtpos,sensorvel,tgtvel);
    tgtsig = target(txsig);
    rxcol = collector(tgtsig,tgtang);
    rxsig = receiver(rxcol);
    cube(:,n) = rxsig;
end

Display the data cube containing signals per pulse.

imagesc([0:(Np-1)]*pri*1e6,[0:(Nr-1)]/fs*1e6,abs(cube))
xlabel('Slow Time {\mu}s')
ylabel('Fast Time {\mu}s')
axis xy

Create and display the range-Doppler image for 128 Doppler bins. The image shows range vertically and speed horizontally. Use the linear FM waveform for match filtering. The image is here is the range-Doppler map.

ndop = 128;
rangedopresp = phased.RangeDopplerResponse('SampleRate',fs, ...
    'PropagationSpeed',c,'DopplerFFTLengthSource','Property', ...
    'DopplerFFTLength',ndop,'DopplerOutput','Speed', ...
    'OperatingFrequency',fc);
matchingcoeff = getMatchedFilter(waveform);
[rngdopresp,rnggrid,dopgrid] = rangedopresp(cube,matchingcoeff);
imagesc(dopgrid,rnggrid,10*log10(abs(rngdopresp)))
xlabel('Closing Speed (m/s)')
ylabel('Range (m)')
axis xy

Because the targets lie along the positive x-axis, positive velocity in the global coordinate system corresponds to negative closing speed. Negative velocity in the global coordinate system corresponds to positive closing speed.

Estimate the noise power after matched filtering. Create a constant noise background image for simulation purposes.

mfgain = matchingcoeff'*matchingcoeff;
dopgain = Np;
noisebw = fs;
noisepower = noisepow(noisebw,receiver.NoiseFigure,receiver.ReferenceTemperature);
noisepowerprc = mfgain*dopgain*noisepower;
noise = noisepowerprc*ones(size(rngdopresp));

Create the range and Doppler estimator objects.

rangeestimator = phased.RangeEstimator('NumEstimatesSource','Auto', ...
    'VarianceOutputPort',true,'NoisePowerSource','Input port', ...
    'RMSResolution',rngrms);
dopestimator = phased.DopplerEstimator('VarianceOutputPort',true, ...
    'NoisePowerSource','Input port','NumPulses',Np);

Locate the target indices in the range-Doppler image. Instead of using a CFAR detector, for simplicity, use the known locations and speeds of the targets to obtain the corresponding index in the range-Doppler image.

detidx = NaN(2,Numtgts);
tgtrng = rangeangle(tgtpos,radarpos);
tgtspd = radialspeed(tgtpos,tgtvel,radarpos,radarvel);
tgtdop = 2*speed2dop(tgtspd,c/fc);
for m = 1:numel(tgtrng)
    [~,iMin] = min(abs(rnggrid-tgtrng(m)));
    detidx(1,m) = iMin;
    [~,iMin] = min(abs(dopgrid-tgtspd(m)));
    detidx(2,m) = iMin;
end

Find the noise power at the detection locations.

ind = sub2ind(size(noise),detidx(1,:),detidx(2,:));

Estimate the range and range variance at the detection locations. The estimated ranges agree with the postulated ranges.

[rngest,rngvar] = rangeestimator(rngdopresp,rnggrid,detidx,noise(ind))
rngest =

  499.7911
  529.8380
  750.0983


rngvar =

   1.0e-04 *

    0.0273
    0.0276
    0.2094

Estimate the speed and speed variance at the detection locations. The estimated speeds agree with the predicted speeds.

[spdest,spdvar] = dopestimator(rngdopresp,dopgrid,detidx,noise(ind))
spdest =

   60.5241
  -19.6167
  -39.5838


spdvar =

   1.0e-05 *

    0.0806
    0.0816
    0.6188

Definitions

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Algorithms

The phased.RangeEstimator System object estimates the range of a detection by following these steps:

  1. Input a range-processed response data cube obtained from either the phased.RangeResponse or phased.RangeDopplerResponse System object. The first dimension of the cube represents the fast-time or equivalent range of the returned signal samples. Only this dimension is used to estimate detection range. All others are ignored.

  2. Input a matrix of detection indices that specify the location of detections in the data cube. Each column denotes a separate detection. The row entries designate indices into the data cube. You can obtain detection indices as an output of the phased.CFARDetector or phased.CFARDetector2D detectors. To return these indices, set the OutputFormat property of either CFAR detector to 'Detection index'.

  3. Optionally input a row vector of cluster IDs. This vector is equal in length to the number of detections. Each element of this vector assigns an ID to a corresponding detection. To form clusters of detections, the same ID can be assigned to more than one detection. To enable this option, set the ClusterInputPort property to true.

  4. When ClusterInputPort is false, the object computes the range for each detection. The algorithm finds the response values at the detection location and at two adjacent indices in the cube along the range dimension. Then, the algorithm fits a quadratic curve to the magnitudes of the range response at these three locations and finds the location of the peak. When detections occur at the first or last sample in the range dimension, the range response is estimated from a two-point centroid. The estimation is at the location of the detection index and at the sample adjacent to the detection index.

    When ClusterInputPort is true, the object computes range for each cluster. The algorithm finds the indices of the largest response value in the cluster and fits a quadratic formula to that detection in the same way as for individual detections.

  5. Convert the fractional index values of the fitted peak locations to range. To convert the indices, choose appropriate units for the rnggrid input argument of the step method. You can use values for rnggrid obtained from either the phased.RangeResponse or phased.RangeDopplerResponse System objects.

The object computes the estimated range variance using the Ziv-Zakai bound.

References

[1] Richards, M. Fundamentals of Radar Signal Processing. 2nd ed. McGraw-Hill Professional Engineering, 2014.

[2] Richards, M., J. Scheer, and W. Holm. Principles of Modern Radar: Basic Principles. SciTech Publishing, 2010.

Extended Capabilities

Introduced in R2017a

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