Unmanned aerial vehicle (UAV) localization is typically performed through sensor fusion to leverage the complementary strengths of multiple sensors. However, establishing reliable localization redundancy with a single sensor remains fundamentally important for system reliability. In particular, external forces during flight, such as collisions, wind effects, or payload changes, induce significant discrepancies between control commands and the actual motion, making robust localization an inherently challenging task. This study explores a simultaneous localization and calibration (SLAC) algorithm for UAVs that improves localization accuracy by directly estimating the unknown external disturbances and compensating for their effects. A finite-memory SLAC (FM-SLAC) algorithm uses only recent information from a finite interval and has shown strong robustness in two-dimensional localization for mobile robots. However, in three-dimensional (3D) UAV localization, it exhibited numerical instability during the computation of the large matrix inversions included in the filter gain. To overcome this drawback, this study proposes a novel unscented information-form finite-memory approach for SLAC (UIFM-SLAC). By employing an unscented transformation (UT), an unscented finite-memory SLAC (UFM-SLAC) is developed to avoid the calculation of the Jacobian matrix and the inverse of large matrices required in the conventional FM-SLAC. Furthermore, an information form is integrated into the framework to establish the UIFM-SLAC, which reduces dependence on initial estimates, thereby eliminating numerical instabilities in high-dimensional models and mitigating accuracy loss caused by the linearization of nonlinear dynamics. Experimental comparisons with standard and state-of-the-art algorithms for robust UAV localization were conducted, and the results demonstrated the superior accuracy and robustness of the UIFM-SLAC.

In this paper, we address a distributed control algorithm designed to counter cyberattacks and faults that may occur when operating multiple unmanned aerial vehicles. The proposed algorithm utilizes a finite-memory-based observer to prevent critical control issues arising from the generation and sharing of abnormal information.

Heterogeneous sensor networks (HSNs) are networks composed of different types of sensors, which have the advantage of collecting information about different characteristics of the target. This work addresses the problem of tracking the position of target objects in indoor spaces using HSNs. We propose a secure and reliable target tracking algorithm that remains effective even under cyber attacks during the information transmission process of HSNs. The proposed algorithm is designed to have a finite-memory structure that does not continuously remember past errors and can use the Kullback-Leibler divergence to determine the confidence/trust level of sensor information under cyber attacks. We conducted experiments to track the position of a mobile robot in an HSN consisting of cameras and LiDAR, and demonstrated that the proposed algorithm provides secure and reliable tracking performance under cyber attacks.

In this paper, we propose a new robot localization method for wireless sensor networks (WSNs). The proposed localization method, finite memory filtering based localization (FMFL), considers the refined measurement estimates pose of a mobile robot, unlike existing estimators that use an infinite impulse response (IIR) structure. We design an estimator to minimize the Frobenius norm of the gain matrices to give it high robustness. The method shows excellent performance in environments, where noise information is unknown and in which sudden disturbances are inserted. Moreover, even when the observation is temporarily impossible due to the non-line-of-sight (NLOS) situation or the temporary failure of sensors, the proposed localization scheme still ensures high robustness, unlike existing methods. The performance of the proposed method is experimentally compared with existing methods in a harsh environment.