Distributed Location Estimation Research Articles

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

Distributed estimation using online semi‐supervised particle filter for mobile sensor networks

This study proposes an improved particle filter by incorporating semi-supervised machine learning for location estimation in mobile sensor networks (MSNs). A time-varying prior model is learned online as the likelihood of particle filter in order to adapt to dynamic characteristics of state and observation. Thanks to semi-supervised learning, the proposed particle filter can improve efficiency and accuracy, where the amount of available labelled training data is limited. The authors compare the proposed algorithm with the particle filter based on supervised learning. The algorithms are evaluated for received signal strength indicator (RSSI)-based distributed location estimation for MSN in which communication bandwidth and accuracy of the range measurement are limited. First, experimental results show that the semi-supervised algorithm can learn suddenly-changed RSSI characteristics while the supervised learning cannot. Second, the proposed particle filter is more accurate and robust against variations of the environment such as new obstacle configurations. Furthermore, the suggested particle filter shows low statistical variability during repeated experiments, confirmed by much smaller error deviation than the compared particle filter.

Asymmetric Event-Driven Localization Algorithm in Constrained Space

Existing technologies are inapplicable to localization in constrained space, especially when considering environmental factors. These methods with low localization accuracy cannot meet the location requirements in constrained space, for they usually call for lots of computation time and process resources. Moreover, they are easily interfered by environmental factors and attacks from other users. Consequently, in order to improve location accuracy in constrained space, an asymmetric event-driven localization algorithm (AELA) is proposed in this paper, which is based on the combination of event distribution and anchor node achieving a distributed location estimation strategy, so that it can satisfy the localization requirement of constrained space and achieve the high-accuracy localization with a small amount of events and anchor nodes. Meanwhile, to improve the accuracy of the algorithm, a set of candidate events are adopted to prune the event which does not meet location accuracy requirements. We finally perform experiments in indoor corridors, and the results show that the proposed algorithm has higher performances not only on localization accuracy and energy consumption but also on anti-interference ability than RSSI and MSP.

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

Distributed location estimation method for mobile terminals based on SOM algorithm

AbstractAd hoc networks have excellent capabilities for data transmission in an environment with an incomplete communications infrastructure, and in mobility and portability of terminals. Routing protocols between terminals in an ad hoc network can be broadly divided into location‐aware routing, based on location information on the terminals, and non‐location‐aware routing, which is not based on location information. In the former, routing can be made accurate and easy by effectively utilizing location information. Hitherto, the use of the global positioning system (GPS) has been most often treated as the means of acquiring terminal location information. However, in location estimation by GPS, electromagnetic waves from multiple location estimation satellites must be received, and the method cannot be applied effectively in an environment in which that condition is not satisfied. This paper proposes the following method of location estimation. Fixed access points whose absolute locations are already known are placed in the network. Then, by using self‐organizing maps (SOM), the mutual location information among the terminals is organized, and the location of the home terminal is successively inferred. The feasibility and accuracy of estimation in the proposed method are examined by a computer simulation. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 87(4): 73–81, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10098