## 2 Related Work  
Basics of sensor nodes positioning for localization and location tracking are discussed in textbooks such as in [6]. Some sensor nodes' localization approaches like ToA [7], TDoA [7] and AoA [8] use the measuring time difference of signal propagation to compute the distances between sensor nodes while some other approaches use the signal strength [6]. If we can measure the distance of a sensor node from a few number of location-aware neighboring nodes, we can evaluate its position using trilateration or multilateration techniques [6]. But in target tracking using sound sensation, the basics of locating a target object is completely different from localization, although there are some similarities too. Using a logical object tracking tree structure for in-network object tracking by considering the topology of network is another approach for target tracking [9]. Organizing sensor nodes as a logical tree facilitates the in-network data processing and reduces the total communication cost of object tracking. Studies by [PERSON] _et al._[10] show that acoustic target tracking using WSNs with satisfactory accuracy is possible. They claim three sensing coverage is sufficient for target tracking in 2-dimensional space. The basics of acoustic target tracking are discussed in their paper. Quality ranking is used to decide the quality of tracking results and quality-driven redundancy suppression and contention resolution is effective in improving the information throughput. Like with many other researchers, they assume that all randomly distributed sensor nodes are location-aware [11]. They discuss several factors that influence the accuracy of target tracking and their potential problems. The accuracy of their results is however not excellent and is greatly influenced by the number of location estimation samples. Their studies had shown a higher error margin than it is possible to achieve. Target tracking studies can be divided into two categories of single target tracking and multiple target tracking. The main aim in multiple target tracking is to separate multiple moving targets from each other [12]. Multiple target tracking using resource limited distributed sensor networks by selecting appropriate fusion mechanism, sensor utility metric and a sensor tasking approach is discussed in [12]. Message-pruning hierarchy trees with the aim of decreasing communication cost and query delay is another approach for multiple target tracking. This method uses a publish-subscribe tracking method [13]. This method is further extended by [PERSON] _et al._[14] by presenting two message-pruning structures for tracking moving objects and taking into account the physical topology of a sensor network for reflecting real communication cost [14]. They have formulated the object tracking problem as an optimization problem. Another method for target tracking is the Bayesian framework presented by [PERSON] _et al._[4] who have developed particle filters that use data association techniques based on probabilistic data associations. They have studied the tracking of target objects using acoustic sensors that are randomly distributed in a field. Hierarchical classification architecture of sensing subsystem of VigilNet surveillance system is discussed in [15]. This architecture enables efficient information processing for classification and detection of different targets by expanding the processing task in various levels of the system. Using particle filtering is another approach for multiple target tracking [16]. [PERSON] _et al._[17] have used WSNs for real-time target tracking with guaranteed deadlines. They have studied the tradeoffs between system properties when meeting real-time constraints. The relations between sensor density and speed of a moving target and wake-up delay of sensor nodes are discussedtoo. Distributed data association of sensors' measurements are used in multiple target tracking [18].