Abstract
There are some deficiencies in the Monte Carlo localization algorithm based on rangefinder, which like location probability distribution of the k moment in the prediction phase only related to the localization of the k − 1 moment and the maximum and minimum velocity. And the influences of the motion condition on the movement of the mobile node at k moment are also not considered before the k − 1 moment. What is more is the process of selecting the effective particles is slow in the algorithm. Considering the situations above, this paper presented a Monte Carlo mobile node localization algorithm based on Newton interpolation, which uses the inheritedness of Newton interpolation, inheriting the historical trajectory prediction mechanism of the moving node to estimate the current moment’s movement speed and movement direction of the moving node, and optimized the moving node motion model, and used particle filter that is optimized by weight of importance to prevent particle collection depletion. The inference and simulation results show that the algorithm has improved the accuracy of the forecast using Newton interpolation. And this algorithm has effectively avoided the degradation of particles and improved the localization accuracy.
Highlights
In recent years, with the rapid development of micro electro mechanical systems and wireless communication technology, wireless sensor network has been applying in wisdom city, wisdom tourist, intelligent household, wisdom pension, and many other areas as a kind of brand-new information acquisition and data processing technology
Hu and Evans first proposed the algorithm that is based on Sequential Monte Carlo Localization algorithm [1, 2] and has achieved good results on location of mobile sensor network node
Frank et al first applied Monte Carlo localization (MCL) [3, 4] to robot location, and the core is based on Bayes filter location estimation to estimate the position distribution of mobile robot in state space using weighted particle set, but Bayes filtering made the previously measured data and current measurements relatively independent [5]
Summary
With the rapid development of micro electro mechanical systems and wireless communication technology, wireless sensor network has been applying in wisdom city, wisdom tourist, intelligent household, wisdom pension, and many other areas as a kind of brand-new information acquisition and data processing technology. Many scholars and researchers have taken advantages of the full study of RSSI, which means using ranging information to optimize the non-ranging location to propose a Monte Carlo localization algorithm based on rangefinder [10]. This algorithm used the distance information obtained by statistical model as the observation value in the filter stage of Monte Carlo algorithm, which significantly improves the location accuracy of MCL algorithm. A mobile node l localization algorithm, received signal strength indication improvement Monte Carlo localization, RSSI-IMCL, is proposed, and in this algorithm, Newton interpolation method is used to predict the location of the estimated nodes based on the historical trajectory of the node and optimize the speed and direction of the node movement. This paper optimized the particle filter algorithm about particle importance weights by introducing a weight impact factors, which made more particles being copied in the resampling process, effectively avoid the particle degeneration, so as to improve the localization accuracy of nodes
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call