Adaptive Event-Triggered Localization Algorithm Based on Cubature Kalman Filter

This paper presents an adaptive event-triggered localization algorithm based on cubature Kalman filter (AETCKF), which addresses the challenge of moving targets based on received signal strength (RSS). Firstly, in order to improve communication and energy efficiency, an adaptive event triggering mechanism based on the distribution of anchors in the environment is developed, which guarantees that the number of triggered anchors is near the anticipated number, regardless of how anchors are distributed around the mobile node. Secondly, the fusion estimation of the distances is carried out using the cubature Kalman filter, where the distances between the service anchors and the mobile node is computed using the loss relationship between the energy and the path. Finally, a precise state estimate of the mobile node can be acquired for the fusion estimation step. Numerical simulation results show that the proposed method offers advantages in reducing energy consumption, communication burdens, and computational complexity while ensuring localization accuracy.