Sensing, processing and communication capabilities are enabled on each sensor node. WSNs are attractive because they can be deployed in nearly any kind of environment without wired connections. More recently, the availability of inexpensive hardware (such as microphones) that are able to ubiquitously capture multimedia content from the environment has fostered the development of wireless multimedia sensor networks (WMSNs) [1,2]. WMSN is equipped by wirelessly interconnected devices that allow retrieving multimedia data, such as video and audio streams. As sensor nodes usually work in unsupervised area, the battery can not be recharged or replaced. To prolong the lifetime of WMSN, energy efficiency becomes a crucial issue.The target tracking application of WMSN is investigated, where acoustic sensors are adopted to localize the target.

Each sensor node can acquire acoustic signals from the target. In centralized networks, there are usually sink nodes for global processing and control. To enhance the resilience of WMSN against sensor node failures or congestion conditions around the sink node, self-organizing and distributed decision of sensor nodes are useful approaches [3]. Considering the target tracking performance, a distributed architecture of WMSN should be exploited to avoid large communication overheads in the centralized approach. For the sensor nodes, it is assumed that the low-power sleep mode is supported by their operation system, i.e., sensor node can switch between active mode and sleep mode.

As described in [4], the power consumption of sleep mode is usually several orders of magnitude less than that of active mode. Energy saving can be achieved by sending sensor node to sleep as much as possible when there is no sensing, processing or communication task. To enhance the energy efficiency and the detection accuracy of WMSN, the sleep coordination and collaborative localization of sensor nodes can be performed with the prior target motion information derived from target tracking procedure. Carfilzomib Therefore, target position forecasting is necessary during target tracking.As the state model of target motion is nonlinear, so target tracking is usually treated as nonlinear estimation problems [5]. The classical method is extending the standard Kalman filter to nonlinear system by local linearizing all nonlinear models around certain points, which is so called Extended Kalman filter (EKF) [6]. In practical, the target may have high maneuvers. Some algorithms have been proposed for maneuvering target tracking, such as unscented Kalman filter (UKF) [7] and unscented particle filter (UPF) [8]. However, these algorithms are computation-expensive under the constraints of limited processing capability.