Cumulative Adaptive Extended Kalman Filter for Robust UWB Positioning in Indoor Environments

ABSTRACT In complex indoor environments, the ultrawide band (UWB) positioning systems enhanced by Kalman filters (KFs) often struggle with large variations in accuracy and reliability due to outlier observations in the state system. This paper presents a cumulative adaptive extended KF (CA‐EKF) to mitigate this issue by optimizing state estimation accuracy. The proposed technique adaptively adjusts process and measurement noise covariance matrices based on residual and innovation vectors, effectively suppressing filtering divergence and significantly enhancing positioning precision. Performance evaluation demonstrates the superiority of CA‐EKF over traditional extended KF (EKF), innovation adaptive KF (IAKF), and Sage–Husa adaptive KF (SHAKF). Monte Carlo simulations under multiple outlier scenarios and nonlinear motion trajectories confirm CA‐EKF's robustness, achieving lower positioning errors, smoother error evolution, and improved resilience against multiple simultaneous outliers. Quantitative results highlight that CA‐EKF consistently outperforms other filters in terms of running‐average root mean square error (RA‐RMSE), mean squared error (MSE), and standard deviation (STD), reducing error accumulation over time and enhancing tracking accuracy. Notably, cumulative distribution function (CDF) analysis confirms that 99% of positioning errors with CA‐EKF remain below 7.65 cm significantly outperforming competing methods. These results establish CA‐EKF as a highly effective solution for high‐precision UWB tracking applications in dynamic environments.