8 citations · 9 across the 5 of their papers we have counts for
3 papers · 1 filter
Adaptive Learned State Estimation based on KalmanNet
Arian Mehrfard, Bharanidhar Duraisamy, Stefan Haag +2
Hybrid state estimators that combine model-based Kalman filtering with learned components have shown promise on simulated data, yet their performance on real-world automotive data…
Performance Evaluation of Deep Learning-Based State Estimation: A Comparative Study of KalmanNet
Arian Mehrfard, Bharanidhar Duraisamy, Stefan Haag +1
Kalman Filters (KF) are fundamental to real-time state estimation applications, including radar-based tracking systems used in modern driver assistance and safety technologies. In…
UNIFY: Multi-Belief Bayesian Grid Framework based on Automotive Radar
Stefan Haag, Bharanidhar Duraisamy, Daniel Pfrommer +3
Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examin…