Abstract-A radar pulse that impinges upon a radar resolution area of the sea surface produces backscattered returns which are called sea clutter. For low grazing angles and very fine radar cell resolution areas, the clutter intensity distribution departs significantly from the exponential distribution. In this case, the clutter is said to display spiky behavior and the distribution of the intensity develops a much longer tail relative to the exponential distribution. Statistical analysis of collected data near a grazing angle of 0.2• at X-band from the sea off the coast of Kauai, Hawaii are examined relative to the log-normal, Weibull and K distributions. Based on an analogy of sea clutter and other disciplines including computer networks and finance, we also apply the Pareto distribution to the collected data. We also compare the data to the WW and KK two-population mixture distributions. Maximum likelihood estimation of the parameters of the distributions are obtained from the measured data. In all cases, the two population mixture distributions and the Pareto distribution are more accurate than the three classical distributions. However the Pareto distribution has the advantage of being an analytically tractable two parameter distribution while having similar accuracy to the five parameter WW and KK at critical values.
We investigate the application of compressive sensing (CS) to inverse synthetic aperture radar (ISAR) imaging of moving targets. We present our results for a simulated target immersed in different levels of sea clutter. Comparison between traditional and CS approaches to ISAR imaging reveal that our based CS algorithm offers some advantages compared to traditional ISAR imaging under certain limited operating conditions that are nevertheless of practical interest. We conclude by pointing out directions for future work in extending the results of this paper.
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