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In this paper, sequential parametric detection problem is addressed for non-Gaussian correlated clutter. It is well known that the assumption of normally distributed clutter leads, mostly, to analytical expressions of the threshold as well the distribution of detection statistic. Nevertheless, due to the resolution improvement of recent sensing instruments such as high resolution radar, the Gaussian assumption is unrealistic since the clutter is nonhomogeneous. As a consequence, using non-Gaussian assumption of the clutter prevents, mostly, of obtaining analytical expressions of the threshold and the distribution of detection statistics. In this work, we overcome this issue by use of the so-called bootstrap techniques for dependent data. Numerical simulations reveal that our proposed method outperforms the classical and sequential non-bootstrap based detection schemes in terms of probability of detection and selects the optimum sample size needed to achieve the required detection performances.

BOOTSTRAP BASED SEQUENTIAL DETECTION IN NON-GAUSSIAN CORRELATED CLUTTER