Abstract
An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.
Highlights
The results of the traditional and the proposed methods are compared to highlight the improvement offered by PA-extended Kalman filter (EKF) and sequentially-adaptive EKF (SA-EKF) as opposed to the traditional EKF
Two adaptive approaches for EKF were proposed that update the measurement noise covariance matrix based on the past measurements
SA-EKF works similar to PA-EKF, as it first waits for Ns measurements and sequentially updates the batch as new measurements are obtained
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The measurement-based adaptive KF algorithm and the improved Suge–Husa algorithm were proposed within the federated structure This method provides improved performance, the modeling of three different algorithms for the purposes of localization in the field of robotics is undesirable due to the high computational burden. This approach mainly focuses on the application to ships in the absence of the satellite navigation system. The proposed method provides improved performance over the existing methods, the application of adaptive weights to estimate the noise covariance matrices over the most recent measurements increases the computational burden of the approach.
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