Principles of Modern Radar - Volume 2 1891121537

6.8 Autofocus 251Range (m)50403020100−10−20Range (m)50403020100−10−20FIGURE 6-21Example SARimagery [15]exhibiting phaseerror effects: (a) Noerror. (b) Quadraticerror. (c) Sinusoidalerror. (d) Randomerror.−30−30−40−50−50−40−30−20−10010Cross-Range (m)(a)20304050−40−50−50−40−30−20−10010Cross-Range (m)(b)203040505050404030302020Range (m)100−10Range (m)100−10−20−20−30−30−40−50−50−40−30−20Cross-Range (m)(c)−1001020304050−40−50−50−40−30−20Cross-Range (m)(d)−1001020304050the fact that phase errors occuring at the radar result in spatially invariant effects in thecorresponding image. This spatial invariance is a strong advantage because it allows asignificant amount of averaging to be performed over the scene to obtain a good estimateof the phase error. For cases in which the artifacts are localized, the image can be chippedinto small regions and analyzed separately. This is the case for stripmap SAR, for example.Autofocus techniques are characterized as being either parametric or nonparametric.The former operate by estimating the values associated with a phase error model, such asthe quadratic coefficent β or some other polynomial fit, while the latter do not. Parametrictechniques are generally good for correcting low-frequency errors, and nonparametricapproaches are well-suited to removing random errors. There is some degree of overlapbetween the two approaches, but it is reasonable to include both in a practical SARimage formation processor since they are complementary. A parametric algorithm is firstapplied to remove the quadratic phase component and improve focus. This is followed bya nonparametric technique whose purpose is to remove the random phase error to improveimage contrast. We now give an example of each type.