Pulse Compression

Pulse compression is a signal processing technique commonly used by radar, sonar and echography to increase the range resolution as well as the signal to noise ratio. This is achieved by modulating the transmitted pulse and then correlating the received signal with the transmitted pulse.

Simple pulse

Signal description

The simplest signal a pulse radar can transmit is a sinusoidal-amplitude pulse, $A$ and carrier frequency, $f_0$, truncated by a rectangular function of width, $T$. The pulse is transmitted periodically, but that is not the main topic of this article; we will consider only a single pulse, $s$. If we assume the pulse to start at time $t=0$, the signal can be written the following way, using the complex notation:

:$s(t) = \begin A e^ &\text \; 0 \leq t < T \\ 0 &\text \end$

Range resolution

Let us determine the range resolution which can be obtained with such a signal. The return signal, written $r(t)$, is an attenuated and time-shifted copy of the original transmitted signal (in reality, Doppler effect can play a role too, but this is not important here.) There is also noise in the incoming signal, both on the imaginary and the real channel, which we will assume to be white and Gaussian (this generally holds in reality); we write $B(t)$ to denote that noise. To detect the incoming signal, matched filtering is commonly used. This method is optimal when a known signal is to be detected among additive white Gaussian noise.

In other words, the cross-correlation of the received signal with the transmitted signal is computed. This is achieved by convolving the incoming signal with a conjugated and time-reversed version of the transmitted signal. This operation can be done either in software or with hardware. We write $\langle s,r \rangle (t)$ for this cross-correlation. We have:

:$\langle s,r \rangle (t) = \int_^ s^\star(t')r(t+t') dt'$

If the reflected signal comes back to the receiver at time $t_r$ and is attenuated by factor $K$, this yields:

:$r(t)= \left\{ \begin{array}{ll} K A e^{2 i \pi f_0 (t\,-\,t_r)} +B(t) &\mbox{if} \; t_r \leq t < t_r+T \\ B(t) &\mbox{otherwise}\end{array}\right.$

Since we know the transmitted signal, we obtain:

:$\langle s,r \rangle (t) = KA^2\Lambda\left (\frac{t-t_r}{T} \right)e^{2 i \pi f_0 (t\,-\,t_r)} + B'(t)$

where $B'(t)$, is the result of the intercorrelation between the noise and the transmitted signal. Function $\Lambda$ is the triangle function, its value is 0 on \infty, -\frac{1}{2}\cup frac{1}{2}, +\infty/math>, it increases linearly on \frac{1}{2}, 0/math> where it reaches its maximum 1, and it decreases linearly on ,\frac{1}{2}/math> until it reaches 0 again. Figures at the end of this paragraph show the shape of the intercorrelation for a sample signal (in red), in this case a real truncated sine, of duration $T=1$ seconds, of unit amplitude, and frequency $f_0=10$ hertz. Two echoes (in blue) come back with delays of 3 and 5 seconds and amplitudes equal to 0.5 and 0.3 times the amplitude of the transmitted pulse, respectively; these are just random values for the sake of the example. Since the signal is real, the intercorrelation is weighted by an additional factor.

If two pulses come back (nearly) at the same time, the intercorrelation is equal to the sum of the intercorrelations of the two elementary signals. To distinguish one "triangular" envelope from that of the other pulse, it is clearly visible that the times of arrival of the two pulses must be separated by at least $T$ so that the maxima of both pulses can be separated. If this condition is not met, both triangles will be mixed together and impossible to separate.

Since the distance travelled by a wave during $T$ is $cT$ (where ''c'' is the speed of the wave in the medium), and since this distance corresponds to a round-trip time, we get:

{, class="wikitable" style="margin: auto"
, -
! Result 1
, -
, The range resolution with a sinusoidal pulse is $\frac{1}{2}cT$ where $T$ is the pulse Duration and, $c$, the speed of the wave.

Conclusion: to increase the resolution, the pulse length must be reduced.

 

{, border="0" style="margin-left: auto; margin-right: auto;"
, + Example (simple impulsion): transmitted signal in red (carrier 10 hertz, amplitude 1, duration 1 second) and two echoes (in blue).
! Before matched filtering
! After matched filtering
, -
,
,
, -
,
,

Required energy to transmit that signal

The instantaneous power of the transmitted pulse is $P(t) = , s, ^2(t)$. The energy put into that signal is:

:$E = \int_0^T P(t)dt = A^2 T$

Similarly, the energy in the received pulse is $E_r = K^2 A^2 T$. If $\sigma$ is the standard deviation of the noise, the signal-to-noise ratio (SNR) at the receiver is:

:$SNR = \frac{E_r}{\sigma^{2 = \frac{K^2 A^2 T}{\sigma^{2$

The SNR is proportional to pulse duration $T$, if other parameters are held constant. This introduces a tradeoff: increasing $T$ improves the SNR, but reduces the resolution, and vice versa.

Pulse compression by linear frequency modulation (or ''chirping'')

Basic principles

How can one have a large enough pulse (to still have a good SNR at the receiver) without poor resolution? This is where pulse compression enters the picture. The basic principle is the following:
* a signal is transmitted, with a long enough length so that the energy budget is correct
* this signal is designed so that after matched filtering, the width of the intercorrelated signals is smaller than the width obtained by the standard sinusoidal pulse, as explained above (hence the name of the technique: pulse compression).

In radar or sonar applications, linear chirps are the most typically used signals to achieve pulse compression. The pulse being of finite length, the amplitude is a rectangle function. If the transmitted signal has a duration $T$, begins at $t = 0$ and linearly sweeps the frequency band $\Delta f$ centered on carrier $f_0$, it can be written:

:$s_c(t) = \left\{ \begin{array}{ll} A e^{i 2 \pi \left( \left( f_0 \,-\, \frac{\Delta f}{2}\right) t \, + \, \frac{\Delta f}{2T}t^2 \, \right)} &\mbox{if} \; 0 \leq t < T \\ 0 &\mbox{otherwise}\end{array}\right.$

The chirp definition above means that the phase of the chirped signal (that is, the argument of the complex exponential), is the quadratic:

:$\phi(t) = 2\pi \left( \left( f_0 \,-\, \frac{\Delta f}{2}\right) t \, + \, \frac{\Delta f}{2T}t^2 \, \right)$

thus the instantaneous frequency is (by definition):

:f(t) = \frac{1}{2\pi}\left frac{d\phi}{dt}\right t = f_0-\frac{\Delta f}{2}+\frac{\Delta f}{T}t

which is the intended linear ramp going from $f_0 - \frac{\Delta f}{2}$ at $t = 0$ to $f_0 + \frac{\Delta f}{2}$ at $t = T$.

The relation of phase to frequency is often used in the other direction, starting with the desired $f(t)$ and writing the chirp phase via the integration of frequency:

:$\phi(t) = 2 \pi \int_0^t f(u)\,du$

Cross-correlation between the transmitted and the received signal

As for the "simple" pulse, let us compute the cross-correlation between the transmitted and the received signal. To simplify things, we shall consider that the chirp is not written as it is given above, but in this alternate form (the final result will be the same):

:$s_{c'}(t) = \begin{cases} A e^{2 i \pi \left (f_0 \,+\, \frac{\Delta f}{2T}t\right) t} &\mbox{if}\; -\frac{T}{2} \leq t < \frac{T}{2} \\ 0 &\mbox{otherwise} \end{cases}$

Since this cross-correlation is equal (save for the $K$ attenuation factor), to the autocorrelation function of $s_{c'}$, this is what we consider:

:$\langle s_{c'},s_{c'}\rangle (t) = \int_{-\infty}^{+\infty} s_{c'}^\star(-t')s_{c'}(t-t')dt'$

It can be shown that the autocorrelation function of $s_{c'}$ is:

:\langle s_{c'}, s_{c'}\rangle(t) = A^2T \Lambda \left(\frac{t}{T} \right) \mathrm{sinc} \left \Delta f t \Lambda \left( \frac{t}{T}\right) \righte^{2 i \pi f_0 t}

The maximum of the autocorrelation function of $s_{c'}$ is reached at 0. Around 0, this function behaves as the sinc (or cardinal sine) term, defined here as $sinc(x)=sin(\pi x)/(\pi x)$. The −3 dB temporal width of that cardinal sine is more or less equal to $T' = \frac{1}{\Delta f}$. Everything happens as if, after matched filtering, we had the resolution that would have been reached with a simple pulse of duration $T'$. For the common values of $\Delta f$, $T'$ is smaller than $T$, hence the ''pulse compression'' name.

Since the cardinal sine can have annoying sidelobes, a common practice is to filter the result by a window ( Hamming, Hann, etc.). In practice, this can be done at the same time as the adapted filtering by multiplying the reference chirp with the filter. The result will be a signal with a slightly lower maximum amplitude, but the sidelobes will be filtered out, which is more important.

{, class="wikitable" style="margin: auto"
, -
! Result 2
, -
, The distance resolution reachable with a linear frequency modulation of a pulse on a bandwidth $\Delta f$ is: $\frac{c}{2\Delta f}$ where $c$ is the speed of the wave.

 
{, class="wikitable" style="margin: auto"
, -
! Definition
, -
, Ratio $\frac{T}{T'} = T\Delta f$ is the pulse compression ratio. It is generally greater than 1 (usually, its value is 20 to 30).

 
{, border="0" style="margin:1em auto;"
, + Example (chirped pulse): transmitted signal in red (carrier 10 hertz, modulation on 16 hertz, amplitude 1, duration 1 second) and two echoes (in blue).
, -
,
,

Improving the SNR through pulse compression

The energy of the signal does not vary during pulse compression. However, it is now located in the main lobe of the cardinal sine, whose width is approximately $T' \approx \frac{1}{\Delta f}$. If $P$ is the power of the signal before compression, and $P'$ the power of the signal after compression, we have:

:$P\times T = P' \times T'$

which yields:

:$P'= P\times \frac{T}{T'}$

As a consequence:
 
{, class="wikitable" style="margin: auto"
, -
! Result 3
, -
, After pulse compression, the power of the received signal can be considered as being amplified by $T \Delta f$. This additional gain can be injected in the radar equation.

 
{, border="0" style="margin:1em auto;"
, + Example: same signals as above, plus an additive Gaussian white noise ($\sigma = 0.5$)
, -
,
,

Stretch processing

While pulse compression can ensure good SNR and fine range resolution in the same time, digital signal processing in such a system can be difficult to implement because of the high instantaneous bandwidth of the waveform ($\Delta f$ can be hundreds of megahertz or even exceed 1 GHz.)
Stretch Processing is a technique for matched filtering of wideband chirping waveform and is suitable for applications seeking very fine range resolution over relatively short range intervals.

Picture above shows the scenario for analyzing stretch processing. The central reference point(CRP) is in the middle of the range window of interest at range of $R_0$, corresponding to a time delay of $t_0$.

If the transmitted waveform is the chirp waveform:
:$x(t)=\exp\left(j\pi\frac{\Delta f}{T}(t)^2\right)\exp(j2\pi f_0(t)), 0\leq t\leq T$
then the echo from the target at distance $R_{b}$can be expressed as:
:$\bar{x}(t)=\rho \exp\left(j\pi\frac{\Delta f}{T}(t-t_{b})^2\right)\exp(j2\pi f_0(t-t_{b})), 0\leq t-t_{b}\leq T$
where $\rho$ is proportional to the scatterer reflectivity.
We then multiply the echo by $\exp(-j2\pi f_0 t)\exp\left(-j\pi\frac{\Delta f}{T}(t-t_0)^2\right)$ and the echo will become:
:$y(t)=\rho \exp\left(-j\frac{4\pi R_{b{\lambda}\right)\exp\left(-j2\pi\frac{\Delta f}{T}\delta t_{b}(t-t_0)\right)\exp\left(j\pi \frac{\Delta f}{T}(\delta t_{b})^2\right),t_0\leq t-\delta t_{b}\leq t_0+T$
where $\lambda$ is the wavelength of electromagnetic wave in air.

After conducting sampling and discrete Fourier transform on y(t) the sinusoid frequency $F_{b}$ can be solved:
:$F_{b}=-\delta t_{b}\frac{\Delta f}{T}(Hz)$
and the differential range $\delta R_{b}$ can be obtained:
:$\delta R_{b}=-\frac{cTF_{b{2\Delta f}$

To show that the bandwidth of y(t) is less than the original signal bandwidth $\Delta f$, we suppose that the range window is $R_{w} = \frac{cT_{w{2}$ long. If the target is at the lower bound of the range window, the echo will arrive $t_0-T_{w}/2$ seconds after transmission; similarly, If the target is at the upper bound of the range window, the echo will arrive $t_0+T_{w}/2$ seconds after transmission.
The differential arrive time $\delta t_{b}$ for each case is $-T_{w}/2$ and $T_{w}/2$, respectively.

We can then obtain the bandwidth by considering the difference in sinusoid frequency for targets at the lower and upper bound of the range window:
$\Delta f_{s} = F_{b,\text{near-F_{b,\text{far = -\frac{\Delta f}{T}(-T_{w}/2-T_{w}/2) = \frac{T_{w{T} \Delta f$
As a consequence:
 
{, class="wikitable" style="margin: auto"
, -
! Result 4
, -
, Through stretch processing, the bandwidth at the receiver output is less than the original signal bandwidth if $T_{w} < T$, thereby facilitating the implementation of DSP system in a linear-frequency-modulation radar system.

 
To demonstrate that stretch processing preserves range resolution, we need to understand that y(t) is actually an impulse train with pulse duration T and period $T_{trans}$, which is equal to the period of the transmitted impulse train. As a result, the Fourier transform of y(t) is actually a sinc function with Rayleigh resolution $\frac{1}{T}$. That is, the processor will be able to resolve scatterers whose $F_{b}$ are at least $\Delta F_{b}=1/T$ apart.

Consequently,
:$\frac{1}{T}=\left\vert \frac{\Delta f}{T}\Delta(\delta t_{b}) \right\vert \Rightarrow \left\vert \Delta(\delta t_{b})\right\vert =\frac{1}{\Delta f}$
and,
:$\Delta(\delta R_{b})=\frac{c\Delta(\delta t_{b})}{2}=\frac{c}{2\Delta f}$
which is the same as the resolution of the original linear frequency modulation waveform.

Stepped-frequency waveform

Although stretch processing can reduce the bandwidth of received baseband signal, all of the analog components in RF front-end circuitry still must be able to support an instantaneous bandwidth of $\Delta f$. In addition, the effective wavelength of the electromagnetic wave changes during the frequency sweep of a chirp signal, and therefore the antenna look direction will be inevitably changed in a Phased array system.

Stepped-frequency waveforms are an alternative technique that can preserve fine range resolution and SNR of the received signal without large instantaneous bandwidth. Unlike the chirping waveform, which sweeps linearly across a total bandwidth of $\Delta f$ in a single pulse, stepped-frequency waveform employs an impulse train where the frequency of each pulse is increased by $\Delta F$ from the preceding pulse. The baseband signal can be expressed as:
:$x(t)=\sum_{m=0}^{M-1}x_{p}(t-mT)e^{j2\pi m\Delta F(t-mT)}$
where $x_{p}(t)$ is a rectangular impulse of length $\tau$ and M is the number of pulses in a single pulse train. The total bandwidth of the waveform is still equal to $\Delta f=M\Delta F$, but the analog components can be reset to support the frequency of the following pulse during the time between pulses. As a result, the problem mentioned above can be avoided.

To calculate the distance of the target corresponding to a delay $t_{l}+\delta t$, individual pulses are processed through the simple pulse matched filter:
:$h_{p}(t)=x^*_{p}(-t)$
and the output of the matched filter is:
:$y_{m}(t)=s^*_{p}(t-(t_{l}+\delta t)-mT)e^{j2\pi m\Delta F(t-(t_{l}+\delta t)-mT)}$
where
:$s^*_{p}(t-(t_{l}+\delta t)-mT)=x_{p}(t-(t_{l}+\delta t)-mT)*h_{p}(t)$
If we sample $y_{m}(t)$ at $t=t_{l}+mT$, we can get:
:
y ,ms^*_{p}(\delta t)e^{j2\pi m\Delta F\delta t}

where l means the range bin l.
Conduct DTFT (m is served as time here) and we can get:
:
Y ,\omega\sum_{m=0}^{M-1}y ,m^{-j\omega m}=s^*_{p}(\delta t)\sum_{m=0}^{M-1}e^{j(\omega-2\pi\Delta F\delta t)m}

,and the peak of the summation occurs when $\omega=2\pi\Delta F\delta t$.

Consequently, the DTFT of y ,m/math> provides a measure of the delay of the target relative to the range bin delay $t_{l}$:
$\delta t=\frac{\omega_{p{2\pi \Delta F}=\frac{f_{p{\Delta F}$
and the differential range can be obtained:
:$\delta R=\frac{cf_{p{2\Delta F}$
where c is the speed of light.

To demonstrate stepped-frequency waveform preserves range resolution, it should be noticed that Y ,\omega/math> is a sinc-like function, and therefore it has a Rayleigh resolution of $\Delta f_{p}=1/M$. As a result:
:$\Delta(\delta t)=\frac{1}{M\Delta F}=\frac{1}{\Delta f}$
and therefore the differential range resolution is :
:$\Delta(\delta R)=\frac{c}{2\Delta f}$
which is the same of the resolution of the original linear-frequency-modulation waveform.

Pulse compression by phase coding

There are other means to modulate the signal. Phase modulation is a commonly used technique; in this case, the pulse is divided in $N$ time slots of duration $\frac{T}{N}$ for which the phase at the origin is chosen according to a pre-established convention. For instance, it is possible to not change the phase for some time slots (which comes down to just leaving the signal as it is, in those slots) and de-phase the signal in the other slots by $\pi$ (which is equivalent of changing the sign of the signal). The precise way of choosing the sequence of $\{0, \pi \}$ phases is done according to a technique known as Barker codes. It is possible to code the sequence on more than two phases (polyphase coding). As with a linear chirp, pulse compression is achieved through intercorrelation.

The advantagesJ.-P. Hardange, P. Lacomme, J.-C. Marchais, ''Radars aéroportés et spatiaux'', Masson, Paris, 1995, , page 104. Available in English: ''Air and Spaceborne Radar Systems: an introduction'', Institute of Electrical Engineers, 2001, of the Barker codes are their simplicity (as indicated above, a $\pi$ de-phasing is a simple sign change), but the pulse compression ratio is lower than in the chirp case and the compression is very sensitive to frequency changes due to the Doppler effect if that change is larger than $\frac{1}{T}$.

See also

* Continuous-wave radar
* Spread spectrum
* Chirp compression

Notes

{{reflist

Further reading

* Nadav Levanon, and Eli Mozeson. Radar signals. Wiley. com, 2004.
* Hao He, Jian Li, and Petre Stoica
Waveform design for active sensing systems: a computational approach
Cambridge University Press, 2012.
* M. Soltanalian
Signal Design for Active Sensing and Communications
Uppsala Dissertations from the Faculty of Science and Technology (printed by Elanders Sverige AB), 2014.
* Solomon W. Golomb, and Guang Gong
Signal design for good correlation: for wireless communication, cryptography, and radar
Cambridge University Press, 2005.
* Fulvio Gini, Antonio De Maio, and Lee Patton, eds. Waveform design and diversity for advanced radar systems. Institution of engineering and technology, 2012.
* John J. Benedetto, Ioannis Konstantinidis, and Muralidhar Rangaswamy.
Phase-coded waveforms and their design
" IEEE Signal Processing Magazine, 26.1 (2009): 22-31.
* Ducoff, Michael R., and Byron W. Tietjen. "Pulse compression radar." Radar Handbook (2008): 8-3.

Signal processing
Radar signal processing