Buffer-Aided-Based Resilient State Estimation for Mobile Robot Localization With State Saturation: A Probabilistic-Encoding Case

• Published in: IEEE transactions on industrial informatics pp. 1 - 10
• Main Authors: Huang, Cong, Zhu, Li, Ding, Weiping, Liu, Ling, Yang, Shichun
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Angular velocity; Buffer-aided mechanism (BAM); Estimation; Location awareness; mobile robot localization (MRL); Mobile robots; probabilistic encoding–decoding (PED) scheme; Probabilistic logic; resilient state estimation; Sensor systems; Sensors; State estimation; state saturation; Transportation; Upper bound
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2025.3575813

In this article, the encoding-decoding-based resilient state estimation problem is investigated for the mobile robot localization with state saturation under the buffer-aided mechanism. To reduce the impact of the intermittent measurements on estimation performance, the buffer-aided mechanism is introduced to store the untransmitted measurements when the transmission is impermissible. The unbiased probabilistic encoding-decoding approach is employed to improve the efficiency and security of the transmission. In addition, the state saturation modeled by a signum function is considered for a more accurate depiction of real-world engineering. The main objective of this article is to establish a buffer-aided-based resilient state estimator for the state-saturated mobile robot localization subject to the probabilistic encoding-decoding, such that the minimal upper bound on the estimation error covariance is guaranteed by appropriately designing the desired estimator gain. Finally, the effectiveness of the proposed estimation scheme is evaluated on a mobile robot platform with buffer capacities of <inline-formula><tex-math notation="LaTeX">M=2, 4, 6</tex-math></inline-formula>. The influence of different encoding interval/gain variation coefficients on the state estimation accuracy is also discussed by conducting comparison-based analyses. Furthermore, when compared to the extended Kalman filtering, the proposed estimation scheme achieves significant improvement in estimation performance while only increasing the computational time by 62.4%.