A Novel Adaptive State Estimation Model: Kalman Filter Coupled With Neural Networks

ABSTRACT Traditional motion models often cannot describe real‐world motion systems accurately when using the Kalman filter (KF) for target tracking. This paper aims to achieve an adaptive estimation of motion states and proposes a KF coupled with neural networks (NNs). First, an adaptive estimation framework is proposed for motion state recognition and target tracking, which couples different NN models with the classical KF. Second, an adaptive NN filtering algorithm is introduced. This filter utilizes NNs to learn the motion patterns of the target and the total Gaussian probability density of the state sequence and performs iterative updates within the framework of the KF. Finally, simulation results on the KITTI dataset demonstrate the proposed filter's high estimation accuracy. Compared to traditional KFs, this filter achieves the prediction of target states through a data‐driven approach, thereby avoiding issues related to fixed motion models and parameters during the filtering process.