Conditional Maximum Likelihood Estimation and Cramér-Rao Lower Bound for High-Speed Multitarget Motion Parameter Estimation in Homogeneous Cluttered Environments

This article presents a focused study on multitarget motion parameter estimation under homogeneous clutter. We first derive an exact maximum likelihood estimation (MLE) formulation that extends the conventional Radon-Fourier transform framework to handle multiple targets and clutter. To reduce the high computational load of joint multitarget MLE, we introduce a conditional MLE (C-MLE) framework that decouples the parameters. In C-MLE, we estimate only one target parameter at each step while keeping the other parameters fixed. We then update the parameters of all targets in turn. Theoretical analysis and experiments show that, compared with existing methods, C-MLE can remove the interference from known targets and improve the estimation of the remaining targets. In addition, we derive the Cramér-Rao lower bound (CRLB) as a benchmark for performance evaluation. Finally, simulation results show that the proposed C-MLE achieves lower mean-squared error and approaches the CRLB in high-signal-to-clutter-plus-noise ratio regime, which confirms its strong performance in multitarget cluttered scenarios.