Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking

Abstract

Localization always plays a critical role in wireless sensor networks for a wide range of applications including military, healthcare, and robotics. Although the classical multidimensional scaling (MDS) is a conventionally effective model for positioning, the accuracy of this method is affected by noises from the environment. In this paper, we propose a solution to attenuate noise effects to MDS by combining MDS with a Kalman filter. A model is built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution. From that, a linear tracking system is developed. The characteristics of the algorithm are examined through simulated experiments and the results reveal the advantages of our method over conventional works in dealing with the above challenges. Besides, the method is simplified with a linear filter; therefore it suits small and embedded sensors equipped with limited power, memory, and computational capacities well.