🤖 AI Summary
In Kalman filtering, high estimation uncertainty and difficulty in jointly identifying inputs, system dynamics, and hidden states arise from inaccurate initial parameter guesses and model–structure mismatches. To address this, we propose a unified variational identification framework grounded in the Kullback–Leibler (KL) divergence. Our method integrates probabilistic graphical models with variational inference, jointly modeling input signals, unknown system dynamics, and latent states as an information-minimization optimization problem—thereby reducing reliance on strong prior structural assumptions. Crucially, KL divergence serves as a unifying objective that simultaneously drives the co-estimation of inputs, dynamics, and states, enhancing both accuracy and robustness under model mismatch and noise corruption—particularly in nonlinear systems. Experimental results on synthetic and real-world systems demonstrate significant reductions in modeling error and estimation uncertainty compared to conventional approaches.