Effect of data reduction on target classification from inverse synthetic aperture radar (ISAR) images

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Effect of data reduction on target classification from inverse synthetic aperture radar (ISAR) images

Gorin, A.L.

This paper is a preliminary report on investigations into the effects of data reduction on ISAR image quality and classifier performance. The case considered here is the effect of decimating the ISAR radar returns. Decimation of the type described induces image aliasing because of the signal processing involved in ISAR image formation. This familiar effect is used to model the degradation of object boundaries, which are used in classification. Constraints on subsampling will be derived as a function of target size, image signal to noise ratio, and classifier-required image quality. Decimation is by no means offered as an optimal solution to achieving acceptable processing times or data-link rates. Other data compression techniques for the TSS need to be explored and their effect on ISAR image quality obtained. Furthermore, the relationship between data compression schemes and selection of radar design parameters such as pulse width, frequency spacing, bandwidth and dwell time need to be analyzed. These issues are the subject of ongoing study efforts. We will proceed in three main steps. The structure of a typical ISAR imaging and target classification system will be reviewed, and the need for data reduction analyzed. We will then define a measure of image quality and its effect on classifier performance. The question will then be posed, and answered, as to the effect on image quality of decimation of the ISAR Target Spectral Sequence (TSS).