Research on Indoor Positioning Method of Smart Trash Can Based on RFID

Xuan, Liang; Shen, Yongkang; Gan, Tianxiang; Zhang, Chunfei

doi:10.1109/iccc56324.2022.10065720

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…To address problems such as the inability of single-GNSS positioning to meet production and construction needs, Yang Yuanxi proposed the concept of integrated positioning, navigating, and timing (PNT), the core of which is not to rely excessively on the GNSS and to use all available PNT information sources to implement all-space target positioning, navigation, and timing services [ 6 ]. In the indoor environment of GNSS rejection, many scholars have conducted research on different positioning technologies, such as ultra-wide band (UWB) positioning technology [ 7 ], the inertial navigation system (INS) [ 8 ], wireless local area network (WLAN) positioning technology [ 9 ], radio-frequency identification (RFID) positioning technology [ 10 ], and Bluetooth positioning technology [ 11 ]. Existing communication means, including WLAN, RFID, and Bluetooth, are unable to achieve accurate indoor positioning because of easy disruption; UWB has attracted a great deal of interest because of its high temporal resolution, high reliability, and good obstacle-penetration capabilities [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB

Ren

Liu

Dai

et al. 2023

Sensors

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With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments.

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