The Chip-based Adaptive synthetic aperture radar (SAR) Processor (CHASP) was developed to facilitate the precision processing of airborne SAR data of moving ship targets. CHASP has been extended to support the processing of RADARSAT-1 (R-1) Range Compressed (RC) data and applied to a set of 121 known ships in R-1 Fine mode data. It is shown that, for R-1 Fine mode data, CHASP-like algorithms can provide more ship information than can be deduced from image analysis alone. Based upon comparison of the ship residual frequency with the Doppler centroid of the background clutter, the ship radial speed can be estimated to within +-2 m/s. Azimuth travelling ships are the most difficult cases for length and speed estimation, but azimuth travelling ships can be readily recognised through a high modulation index of the power envelope in azimuth. The ship radar cross section (RCS) is well estimated for both range and azimuth travelling ships. Unfortunately, processing experiments could neither prove nor refute the expected SAR resolution dependence of the measured ship RCS. Single look complex (SLC) data could also be used as a starting point for CHASP-like analysis. However, this would introduce additional complexity since the internal details of the SAR processor must be completely known. In general, it is recommend that adaptive ship data analysis start from RC data. The most important outcome of this work is that, for R-1 Fine mode data, frequency tracking of the ship signature can provide an accurate estimate of the ship radial speed under generalized conditions. Normally, ship radial speed estimation relies upon the azimuth shift of the ship target relative to the ship wake location. Unfortunately, ship wakes are rather rarely visible in R-1 data.


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    Title :

    Adaptive Processing of RADARSAT-1 Fine Mode Data: Ship Parameter Estimation


    Contributors:

    Publication date :

    2007


    Size :

    98 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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