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Abstract Accurate position estimation is essential for unmanned aerial vehicles (UAVs), yet conventional filters can degrade when model mismatch and non-Gaussian measurement disturbances occur simultaneously. This study proposes a dual-robust unscented Kalman filter, termed H∞-GMMC-UKF, combining an H∞-motivated prior-covariance modification with a generalized maximum mixture correntropy measurement update.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
