Adaptive target detection algorithm in compound-Gaussian clutter

This paper deals with the problem of adaptive detection for targets with known Doppler and unknown complex amplitude in compound Gaussian clutter. The speckle component of the clutter is modeled as an autoregressive(AR) process with unknown parameters. Basing on the generalized Likelihood ratio(GLR) criterion, we first estimate the AR parameters and the unknown complex amplitude by maximum likelihood estimation, and then propose an adaptive GLR detector. It has been shown that for large data records, the proposed detector is Constant False Alarm Rate with respect to the product of the driving signal’s variance of AR model and the texture component of the clutter. The numerical simulations show that the proposed detector has better performance than the Normalized Adaptive Matched Filter with two different clutter covariance matrix estimation approaches. The impacts of the target length and the AR model order on the proposed detector performance are analyzed and variously simulated in this paper.