A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

Abstract

Since the wireless sensor network (WSN) has the performance of sensing, processing, and communicating, it has been widely used in various environments. The node localization is a key technology for WSN. The accuracy localization results can be achieved in ideal environment. However, the measurement may be contaminated by NLOS errors in rough environment. The NLOS errors could result in big localization error. To overcome this problem, we present a mobile node localization algorithm using TDOA and RSS measurements. The proposed method is based on Kalman framework and utilizes the general likelihood ratio method to identify the propagation condition. Then the modified variational Bayesian approximation adaptive Kalman filtering is used to mitigate the NLOS error. It could estimate the mean and variance of measurement error. The simulation results demonstrate that the proposed method outperforms the other methods such as Kalman filter and ∞ filter.

Powerful mobile nodes for enhancing wireless sensor networks' security and lifetime

“…Given the importance of precise location of nodes in RCSFs, a mobile method has been proposed by Chu and Cheng-dong [18] for RCSFs in LOS / NLOS environments. The measurements used in the proposed method are time difference by arrival (TDOA) and received signal strength (RSS) [19].…”

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

“…The KF achieves this goal by deriving analytics equations based on multivariate normal distributions and linear projections. For this reason, KF is excellent in estimating information in linear and normal distribution environments [ 8 , 9 , 10 , 11 , 12 , 13 ]. Conversely, PF utilizes a set of discrete points or particles.…”

Distributed networked localization using neighboring distances only through a computational topology control approach

“…In the studies of concurrent algorithms, many works focus on the problem of localization refinement, where rough position estimates are desired to be refined to more accurate ones. [12][13][14][15][16] Toward this goal, the position estimate of each node is often considered as a distribution rather than a single point, and nodes then refine their position estimates by modeling the distribution characteristics, by intersecting the neighboring distributions, and by fusing data within the intersections. If the models of both the localization process and the noises are available, then estimates can be refined by making the noisy measurements best fit the model from the perspective of probability distribution, and thus, a refined localization result with extremely high precision would be obtained.…”