DAKF: Tracking Nonstationary Dynamics with Precision-Weighted Excitation

Tracking changing dynamics requires deciding when to retain a model and when to adapt from noisy, dependent observations. We introduce the Dual-Arm Kalman Filter (DAKF), coupling retained-model filtering and adaptive identification through hard selection and majority synchronization. Precision-weighted persistent excitation (PW-PE) yields a finite-sample identification bound under calibrated conditional noise, with explicit filtering-bias and drift costs and a matching scalar precision lower bound. A filtered-response bias bound connects this guarantee to model mismatch, state uncertainty and selection. Experiments cover synthetic changes, variance diagnostics and recorded UAV braking, with covariance-matched classical baselines and an official KalmanNet implementation. Synthetic ablations support the architecture; the braking benchmark uses common ground-truth initialization, with cold-start sensitivity reported separately.