AMTCC-PARAFAC: A convergent tensor framework for DOD–DOA–Doppler estimation in bistatic MIMO radar under impulsive noise

To address the severe performance degradation of parameter estimation in impulsive noise environments, this paper proposes a novel tensor decomposition framework based on adaptive maximum total complex correntropy (AMTCC) for robust joint parameter estimation in bistatic MIMO radar systems. In the proposed method, for the first time, the AMTCC criterion to reconstruct the parallel factor (PARAFAC) cost function, marking the initial integration of complex correntropy theory with tensor decomposition. To optimize performance, we incorporate an adaptive kernel bandwidth selection mechanism that dynamically adjusts to impulsive noise environments, significantly enhancing parameter estimation accuracy. Then, we develop a novel PARAFAC algorithm based on the AMTCC and apply it to target parameter estimation in bistatic MIMO radar. The proposed algorithm eliminates FLOS methods’ need for prior noise knowledge while concurrently suppressing complex noise components and enabling automatic parameter pairing. Furthermore, we provide theoretical analyses: (1) analyzed complex correntropy’s impulsive noise suppression via nonlinear kernels, (2) proved the boundedness of the AMTCC cost function, (3) analyzed robustness advantages of AMTCC-PARAFAC over existing decompositions and its methodological positioning, (4) derived parameter Cramér–Rao bounds under $\alpha$-stable noise, and (5) determined target identifiability limits through factor matrices’ Kruskal rank and dimension constraints. Simulation results demonstrate that the proposed algorithm effectively suppresses complex-domain impulsive noise while eliminating FLOS methods’ dependence on prior noise statistics, achieving superior parameter estimation accuracy and automatic pairing capability in $\alpha$-stable noise environments.