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This paper introduces Unscented KalmanNet (UKN), a hybrid deep learning filter that augments the Unscented Kalman Filter with learned components to improve state estimation accuracy and covariance calibration under unknown noise statistics and model mismatch. Experiments show significant RMSE reductions over UKF and other KalmanNet variants.
This paper introduces KGPS, a Kalman-guided prompt selection method for adaptive RL finetuning of LLMs, which models prompt difficulty as a dynamic state to improve accuracy and rollout efficiency.
A practical guide to recursive filters (SMA, EMA, low-pass, and a tiny 1D Kalman) for smoothing noisy measurements with low latency and compute.
This paper proposes an edge-aware online tracking pipeline for thermal infrared UAV swarm tracking, featuring the Adaptive Kinematic Kalman Filter (AKKF) that balances efficiency and robustness under challenging conditions.
This paper presents CA-NKCF, a novel distributed latent state estimator combining partial domain knowledge with deep neural networks, achieving robust performance without noise statistics knowledge, outperforming traditional filters in linear, chaotic, and wireless tracking environments.
A University of Johannesburg working paper proposes a Kalman-filtered, momentum-extended Anticor algorithm (K-ACM) that, in a backtest from 1962–1984, produces an extreme return of $1 to $36 billion, though acknowledged as a backtest artifact due to unrealistic assumptions.
This paper introduces the Kalman Prototypical Network (KPN), a few-shot learning framework for fault detection in combined-cycle gas turbines. KPN models class prototypes as latent stochastic states to reduce variance and outperforms conventional methods on simulated leak detection tasks.
A tweet announces that University of Michigan's ROB 501, a 26-lecture series covering the mathematical foundations of robotics, is available for free on YouTube with open-source materials on GitHub.
A comprehensive guide explaining the Kalman filter and its application in building smarter trading systems, including mathematical foundations and production-grade examples.
This paper introduces a structured parameterization for noise models in ELTO-based Kalman filters, enabling dynamic adaptation to non-stationary processes and improving state estimation performance in noisy, time-varying environments.