Dynamic Multidimensional Scaling Algorithm for 3-D Mobile Localization

Localization in wireless sensor networks has attracted much attention in recent years. Existing 3-D localization methods suffer from low accuracy and low stability, especially for moving target localization and tracking. The main objective of this paper is to design a novel 3-D localization algorithm for GPS-denied environments that can achieve higher stability and accuracy of mobile localization without knowledge of both measurement noise statistics and target motion information. One of the key contributions is that in the proposed method, particle swarm optimization is combined with multidimensional scaling to improve the localization accuracy. Furthermore, a polynomial data fitting method is employed for location correction, which is applied especially to moving target scenarios to enhance adaptability to different movement patterns. The proposed method is evaluated by using our 3-D ultrawideband measurement-based indoor localization test bed. The experimental results evince that the algorithm proposed can achieve better performance in terms of higher stability and higher localization accuracy by comparing with existing approaches.