Work

Improved maximum correntropy criterion Kalman filter with adaptive behaviors for INS/UWB fusion positioning algorithm

Year: 2024

Type: article

Abstract: Accurate indoor robot navigation cannot be achieved without reliable indoor positioning techniques. Ultra-wideband (UWB) is among the most dependable techniques currently available. However, the complexity of indoor environments results in signal transmissions that are susceptible to interference from obstacles, which in turn reduces positioning accuracy. Inertial Navigation System (INS) is an autonomous navigation system that is free from interference in indoor environments and unaffected by non-line-of-sight (NLOS) conditions. This paper proposes a new joint INS and UWB positioning method utilizing the Maximum Correntropy Criterion Kalman Filter (MCCKF). This approach effectively cope with the interference of measurement outliers and extend the design of the adaptive mechanisms to enhance the performance of the localization system. For UWB positioning of tag nodes, an improved Particle Swarm Optimization combined with kmeans (PSO-kmeans) method is used to reduce the impact of NLOS errors on positioning. Finally, the INS is calibrated by AMCCKF fused positioning results. The results of simulations and experiments demonstrate that the proposed AMCCKF fusion algorithm effectively suppresses the impact of anomalous measurements, enhances positioning accuracy and robustness, thereby improving its practicality in real-world environments.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2024.09.065

Authors: Yan Wang, Shengqing Fu, Fuhui Wang

Institution: Northeastern University

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,300

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hebei Province