In the Kalman filter (KF), the estimated state is a linear combination of the one-step
prediction and measurement. The two combination weights depend on the prediction mean …

Existing robust state estimation methods are generally unable to distinguish model
uncertainties (state outliers) from measurement outliers as they only exploit the current …

Autonomous navigation at high speeds in off-road environments necessitates robots to
comprehensively understand their surroundings using onboard sensing only. The extreme …

Due to the unfavorable interference of non-Gaussian noise, abnormal system states, and
rough measurement errors, dynamic state estimation (DSE) plays an important role in the …

Accurate estimation of dynamic states is the key to monitoring power system operating
conditions and controlling transient stability. The inevitable non-Gaussian noise and …

To address the interference of outliers on the estimation of state and measurement noise
covariance matrix, an adaptive maximum correntropy cubature Kalman filter with variational …

Due to inaccurate noise modeling and only using current measurement, existing robust
filtering algorithms cannot obtain good state estimation results when measurement is …

Robust state estimation is critical for enabling reliable autonomous robot operations in
challenging environments. To estimate the state, heterogeneous sensor fusion is commonly …

Multi-sensor Fusion (MSF) algorithms are critical components in modern autonomous
driving systems, particularly in localization and AI-powered perception modules, which play …

This study proposes an external force estimation method based on the error probability
model and a sliding window variational adaptive Kalman filter (SWVAKF) for the problem of …