Indoor Target Tracking Based on Extended Kalman Filter without Velocity Constraint

This paper presents an enhanced localization algorithm based on the Extended Kalman Filter (EKF). The algorithm takes into account the unknown motion state of the target, omitting the inclusion of velocity in the motion state equation. Additionally, we introduce a Human Motion Characteristics (HMC)-based optimization algorithm to address the issue of estimated locations with significant deviations. To evaluate the performance of the proposed algorithm, we conducted simulations using commercial-grade wireless ray propagation modeling software. The results demonstrate that the algorithm achieves a median error that is 25.8 cm lower than that of the least square (LS) algorithm. Notably, the algorithm exhibits improved localization accuracy, particularly in low signal-to-noise ratio (SNR) environments.