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Increasing the duration of a transmitted pulse increases its energy and improves target detection capability. Conversely, reducing the duration of a pulse improves the range resolution of the radar.

For a rectangular pulse, the duration of the transmitted pulse and the processed echo are effectively the same. Therefore, the range resolution of the radar and the target detection capability are coupled in an inverse relationship.

Pulse compression techniques enable you to decouple the duration of the pulse from its energy by effectively creating different durations for the transmitted pulse and processed echo. Using a linear frequency modulated pulse waveform is a popular choice for pulse compression.

The complex envelope of a linear FM pulse waveform with increasing instantaneous frequency is:

where β is the bandwidth and τ is the pulse duration.

If you denote the phase by Θ(t), the instantaneous frequency is:

which is a linear function
of *t* with slope equal to β/τ.

The complex envelope of a linear FM pulse waveform with decreasing instantaneous frequency is:

Pulse compression waveforms have a time-bandwidth product, βτ, greater than 1.

To create a linear FM pulse waveform, use `phased.LinearFMWaveform`.
You can customize certain characteristics of the waveform, including:

Sample rate

Duration of a single pulse

Pulse repetition frequency

Sweep bandwidth

Sweep direction (up or down), corresponding to increasing and decreasing instantaneous frequency

Envelope, which describes the amplitude modulation of the pulse waveform. The envelope can be rectangular or Gaussian.

The rectangular envelope is as follows, where τ is the pulse duration.

The Gaussian envelope is:

Number of samples or pulses in each vector that represents the waveform

This example shows how to create a linear FM
pulse waveform using `phased.LinearFMWaveform`. The
example illustrates specific property settings.

Create a linear FM pulse with a sample rate of 1 MHz, a pulse duration of 50 μs with an increasing instantaneous frequency, and a sweep bandwidth of 100 kHz. The amplitude modulation is rectangular.

hfm1 = phased.LinearFMWaveform('SampleRate',1e6,... 'PulseWidth',5e-5,'PRF',1e4,... 'SweepBandwidth',1e5,'SweepDirection','Up',... 'Envelope','Rectangular',... 'OutputFormat','Pulses','NumPulses',1);

This example shows how to design a linear FM pulse waveform, plot the real part of the waveform, and plot one pulse repetition interval.

Design a linear FM pulse waveform with a duration of 100 μs, a bandwidth of 200 kHz, and a PRF of 1 kHz. Use the default values for the other properties. Compute the time-bandwidth product and plot the real part of the pulse waveform.

hfm = phased.LinearFMWaveform('PulseWidth',100e-6,... 'SweepBandwidth',2e5,'PRF',1e3); disp(hfm.PulseWidth*hfm.SweepBandwidth) plot(hfm)

Use the `step` method to obtain your
pulse waveform signal. Plot the real and imaginary parts of one pulse
repetition interval.

y = step(hfm); t = unigrid(0,1/hfm.SampleRate,1/hfm.PRF,'[)'); figure; subplot(2,1,1) plot(t,real(y)) axis tight; title('Real Part'); subplot(2,1,2); plot(t,imag(y)); xlabel('Seconds'); title('Imaginary Part'); axis tight;

This example shows how to plot the ambiguity function of the linear FM pulse waveform.

Define and set up the linear FM waveform.

hlfm = phased.LinearFMWaveform('PulseWidth',100e-6,... 'SweepBandwidth',2e5,'PRF',1e3);

Generate samples of the waveform.

x = step(hlfm);

Create a 3-D surface plot of the ambiguity function for the waveform.

[afmag_lfm,delay_lfm,doppler_lfm] = ambgfun(x,... hlfm.SampleRate,hlfm.PRF); surf(delay_lfm*1e6,doppler_lfm/1e3,afmag_lfm,... 'LineStyle','none'); axis tight; grid on; view([140,35]); colorbar; xlabel('Delay \tau (\mus)'); ylabel('Doppler f_d (kHz)'); title('Linear FM Pulse Waveform Ambiguity Function');

The surface has a narrow ridge that is slightly tilted. The tilt indicates better resolution in the zero delay cut. For a more detailed analysis of waveforms using the ambiguity function, see Waveform Analysis Using the Ambiguity FunctionWaveform Analysis Using the Ambiguity Function.

This example shows how to compute and plot the ambiguity function magnitudes for a rectangular and linear FM pulse waveform. The zero Doppler cut (magnitudes of the autocorrelation sequences) illustrates pulse compression in the linear FM pulse waveform.

Create a rectangular waveform and a linear FM pulse waveform having the same duration and PRF. Generate samples of each waveform.

hrect = phased.RectangularWaveform('PRF',2e4); hfm = phased.LinearFMWaveform('PRF',2e4); xrect = step(hrect); xfm = step(hfm);

Compute the ambiguity function magnitudes for each waveform.

[ambrect,delay] = ambgfun(xrect,hrect.SampleRate,hrect.PRF,... 'Cut','Doppler'); ambfm = ambgfun(xfm,hfm.SampleRate,hfm.PRF,... 'Cut','Doppler');

Plot the ambiguity function magnitudes.

subplot(211); stem(delay,ambrect) title('Autocorrelation of Rectangular Pulse'); axis([-5e-5 5e-5 0 1]); set(gca,'XTick',1e-5 * (-5:5)) subplot(212); stem(delay,ambfm) xlabel('Delay (seconds)'); title('Autocorrelation of Linear FM Pulse'); axis([-5e-5 5e-5 0 1]); set(gca,'XTick',1e-5 * (-5:5))

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