Thus, autoregressive moving average (ARMA) model [9] can be taken into account for target tracking. The forecasting efficiency of ARMA model has been justified in many applications, such as power system [10]. However, the high uncertainty of maneuver which brings estimation error into the forecasting process should be handled in this case. Otherwise, it would degrade the performance of target tracking, or even miss the target (when the target detection is directed by the forecasting results). Therefore, the estimation error of model should be compensated, where artificial neural networks can be considered. As radial basis function networks (RBFNs) [11] have excellent performance on computation precision and convergence speed, it can be employed here.

As mentioned earlier, the forecasted target position can be utilized to schedule the operation mode of sensor nodes in order to save energy. Also, more reasonable decision can be made with the forecasting results when multiple sensor nodes localize the target collaboratively. In addition, the data delivery and query/response process should be exploited under the distributed architecture of WMSN.Considering target tracking performance and the energy efficiency of WMSN, an energy-efficient target tracking method is proposed with robust target forecasting. Firstly, a totally distributed architecture is proposed, i.e. without the requirement of a sink node. The regular geometric structure of WMSN is considered to obtain stable coverage and connectivity. Especially, the honeycomb configuration is utilized to provide the most efficient coverage with specified sensor node number.

Then, a novel algorithm is proposed for target position forecasting, which is so-called ARMA-RBF. It is a combination of ARMA model and RBFN. According to the historical target positions, the parameters of ARMA model are estimated dynamically and the RBFN is trained to compensate the estimation error. Meanwhile, the data delivery approach is presented to support the distributed processing of sensor nodes. With the forecasting results, sensor nodes are awakened to active mode for the future detection task. As multiple sensor nodes can acquire the acoustic signals, the target is localized via committee decision [12]. With energy attenuation model of acoustic signal, the committee decision is realized by RBFN, which is trained in advance to depict the mapping from related signal energy feature to target position.

Carfilzomib Furthermore, the sensor-to-observer routing scheme for reporting the target position is discussed in the network with honeycomb configuration. Experiments analysis is presented to justify the efficiency of the proposed target tracking method while the localization accuracy improvement and energy saving of WMSN are illustrated.The rest of this paper is organized as follows. Section 2 presents the related work of this research.