STVF: Spatial-Temporal Variational Filtering for Localization in Underwater Acoustic Sensor Networks

Abstract

Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes’ locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node’ location is computed based on its previous location; in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial–temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP.

Research on WSN Node Localization and Identity Authentication System

“…The above equations may not be applicable in practice, but are only the simplest models to show that location and identity information can be separated from each other in the same system. There are many more complex but precise methods to control the effects of multi-path and Doppler effects on sound signals, such as adaptive filtering algorithms, Doppler shift cancellation algorithms and blind signal separation algorithms [17]. These algorithms have relatively complete systems and different algorithms have different adaptation scenarios and constraints, which are not the focus of this paper and will not be discussed in detail here.…”

Three-Dimensional UWSN Positioning Algorithm Based on Modified RSSI Values

“…Model. Wireless sensor networks on land generally require two-dimensional planar positioning, whereas UWSNs in special underwater environments require three-dimensional positioning [15][16][17]. Such requirements cannot be met by traditional positioning algorithms.…”

ProLo: Localization via Projection for Three-Dimensional Mobile Underwater Sensor Networks

“…Yan et al [21] proposed an asynchronous localization scheme that uses an iterative least squares estimator to predict the mobility of sensor nodes and localize the sensor nodes. Hu et al [22] proposed a localization method using variational filtering. Their method utilizes the spatial correlation and temporal dependency information to improve the accuracy.…”