Page 367 - Fundamentals of Radar Signal Processing
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motion is detected and compensated, the MTI filter null will not be centered on

               the clutter spectrum and cancellation will be poor.
                     The largest source of clutter offset and spreading is radar platform motion.
               Recall from Chap. 3, Eq. (3.5), that the motion-induced clutter bandwidth is





                                                                                                       (5.72)


               The offset in center frequency of the clutter spectrum can be as much as a few
               kilohertz  for  fast  aircraft  when  forward  looking,  while  the  motion-induced
               spectral  spread  can  be  tens  to  a  few  hundreds  of  hertz  for  a  sidelooking
               configuration. This clutter spreading adds to the intrinsic spread of the clutter
               spectrum due to internal motion and can often be the dominant effect determining
               the  observed  clutter  spectral  width  and  therefore  determining  the  MTI

               performance limits.




               5.3   Pulse Doppler Processing

               Pulse  Doppler  processing  is  the  second  major  class  of  Doppler  processing.
               Recall that in MTI processing the fast time-slow-time data matrix is highpass
               filtered  in  the  slow-time  dimension,  yielding  a  new  fast-time/slow-time  data
               sequence in which the clutter components have been attenuated. Pulse Doppler
               processing  differs  in  that  filtering  in  the  slow-time  domain  is  replaced  by

               explicit  spectral  analysis  of  the  slow-time  data  for  each  range  bin.  Target
               detection  is  then  performed  directly  on  the  range-Doppler  matrix  of  data.
               Because  the  range-Doppler  matrix  is  the  fundamental  data  quantity  in  pulse-
               Doppler processing, the first step is to form it from the fast-time/slow-time CPI
               matrix by computing the one-dimensional spectrum of the slow-time signal in
               each range bin. The spectral analysis is most commonly by far performed using
               the fast Fourier transform  (FFT)  algorithm  to  compute  the discrete  Fourier
               transform (DFT) as shown in Fig. 5.15, but other techniques can also be used.

               The computed DFT is a frequency-sampled version of the DTFT of the slow-
               time signal. The figure emphasizes the fact that the number of frequency samples
               K does not have to equal the number of slow-time samples M; often K > M as
               shown.
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