Before delving into the topic of pulse compression, it is necessary to briefly discuss the advantages of pulse RADAR over CW RADAR. The main difference between the two is with duty cycle (time high vs total time). For CW RADARs this is 100% and pulse RADARs are typically much lower. The efficiency of this comes with the fact that the scattered signal can be observed when the signal is low, making it much more clear. With CW RADARs (which are much less common then pulse RADARs), since the transmitter is constantly transmitting, the return signal must be read over the transmitted signal. In all cases, the return signal is weaker than the transmitter signals due to absorption by the target. This leads to difficulties with continuous wave RADAR. Pulse RADARs can also provide high peak power without increasing average power, leading to greater efficiency.
“Pulse Compression” is a signal processing technique that tries to take the advantages of pulse RADAR and mitigate its disadvantages. The major dilemma is that accuracy of RADAR is dependent on pulse width. For instance, if you send out a short pulse you can illuminate the target with a small amount of energy. However the range resolution is increased. The digital processing of pulse compression grants the best of both worlds: having a high range resolution and also illuminate the target with greater energy. This is done using Linear Frequency Modulation or “Chirp modulation”, illustrated below.
As shown above, the frequency gradually increases with time (x axis).
A “matched filter” is a processing technique to optimize the SNR, which outputs a compressed pulse.
Range resolution can be calculated as follows:
Resolution = (C*T)/2
Where T is the pulse time or width.
With greater range resolution, a RADAR can detect two objects that are very close. As shown this is easier to do with a longer pulse, unless pulse compression is achieved.
It can also be demonstrated that range resolution is proportional to bandwidth:
Resolution = c/2B
So this means that RADARs with higher frequencies (which tend to have higher bandwidth), greater resolution can also be achieved.