Adaptive Learned State Estimation based on KalmanNet
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 remains insufficient. In this work we present Adaptive Multi-modal KalmanNet (AM-KNet), an advancement of KalmanNet tailored to the multi-sensor autonomous driving setting. AM-KNet introduces sensor-specific measurement modules that enable the network to learn the distinct noise characteristics of radar, lidar, and camera independently. A hypernetwork with context modulation conditions the filter on target type, motion state, and relative pose, allowing adaptation to diverse traffic scenarios. We further incorporate a covariance estimation branch based on the Josephs form and supervise it through negative log-likelihood losses on both the estimation error and the innovation. A comprehensive, component-wise loss function encodes physical priors on sensor reliability, target class, motion state, and measurement flow consistency. AM-KNet is trained and evaluated on the nuScenes and View-of-Delft datasets. The results demonstrate improved estimation accuracy and tracking stability compared to the base KalmanNet, narrowing the performance gap with classical Bayesian filters on real-world automotive data.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingSimilar Papers 제목 키워드 기반
Performance Evaluation of Deep Learning-Based State Estimation: A Comparative Study of KalmanNet
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 a linear dynamical system with Gaussian …
State EstimationUnsupervised Learned Kalman Filtering
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervis…
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them t…
Language ModelingLanguage ModellingLarge Language ModelState EstimationPractical Implementation of KalmanNet for Accurate Data Fusion in Integrated Navigation
The extended Kalman filter has been widely used in sensor fusion to achieve integrated navigation and localization. Efficiently integrating multiple sensors requires prior knowledge about their errors for setting the fil…
Sensor FusionState EstimationLatent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., wh…
Vocal Bursts Intensity Prediction