Abstract
The adaptive detection of range-spread targets in non-Gaussian clutter is addressed. The clutter is modeled as spherically invariant random vector. After the sample covariance matrix based on secondary data, is inserted, in place of the true normalized covariance matrix, into the test, two adaptive detectors are derived, which are called as ANSDD-GLRT and ASDD-GLRT. The constant false alarm rate property for both detectors is proved. The experimental results show that the ANSDD-GLRT converges faster than the ASDD-GLRT, while the latter can reject the unwanted signal more effectively.
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