Improved Minimum Description Length CFAR Based on Weighted Log-Likelihood Estimation

Minimum description length CFAR (MDL-CFAR) can improve the detection performance in clutter edge environment, but the detection performance is poor in multi-target environment. In this paper, an improved minimum description length CFAR based on weighted log-likelihood estimation is proposed. The minimum description length method is used for clutter edge location determination, and the final selected sample data set is subjected to weighted log-likelihood estimation to obtain the background clutter power estimate. Comparing the advantages and disadvantages of CFAR algorithm based on minimum description length and CFAR detection algorithm based on weighted log-likelihood estimation, the proposed improved minimum description length CFAR based on weighted log-likelihood estimation (WLL-MDL-CFAR), which combines the advantages of WLL-CFAR and MDL-CFAR algorithms, effectively improves the detection performance in different environments. And at the same time, the ability to maintain a constant false alarm in the clutter-edge environment is guaranteed.