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How do you estimate the detection SNR for a radar transmitting an LFM (chirp) waveform? I'm looking for an SNR equation that is a function of chirp bandwidth and compressed (or uncompressed) pulsewidth.

Do you use the uncompressed pulsewidth or the compressed pulsed width? Do you add a term for pulse compression gain (proportional to the time bandwidth product = bandwidth*pulsewidth?) to the radar range equation?

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According to Mahafza, "For a given set of radar parameters, and as long as the transmitted pulse remains unchanged, then the SNR is also unchanged regardless of the signal bandwidth. More precisely, when pulse compression is used, the detection range is maintained while the range resolution is drastically improved by keeping the pulse width unchanged and by increasing the band-width."

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  • $\begingroup$ I think the SNR you are talking about is the SNR without pulse compression, as matched filtering introduces the processing gain so you should also add this processing gain too. $\endgroup$
    – Zeeshan
    Apr 3, 2017 at 7:36
  • $\begingroup$ you can also look here might be this will provide some help signal detection- Pulse Compression SNR Gain $\endgroup$
    – Zeeshan
    Apr 3, 2017 at 7:47

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With pulse compression, the SNR improves by a factor equal to the time-bandwidth product (synonymous with pulse compression gain).

In the equation you listed, $\tau$ can be interpreted as the uncompressed pulse width, in which case that you'd need to add a factor for the pulse compression gain.

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    $\begingroup$ I'm pretty sure that you'd only add a pulse compression gain term with the compressed pulse width. $\endgroup$
    – random_dsp_guy
    Apr 1, 2017 at 18:50
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    $\begingroup$ @Seth, A little late to this but you are correct. At the end of the day, SNR is determined by the energy put into the pulse and not on the modulation used. You would include the compression gain when using the compressed pulsewidth, which then ultimately yields the real pulse width, ergo the energy put into the pulse. $\endgroup$
    – Envidia
    Jan 10, 2020 at 15:37

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