## 1 Introduction  
Giving the limited power and processing capability in a sensor more, a critical challenge of target tracking is how to acquire suitable data and perform information processing at the local level through cooperative communication and networking in the vicinity of the target. Thus, scalability and the need to conserve energy lead to the idea of hierarchically organizing the sensors, which can represent the target state and incorporate statistical models for the sensing schedule and target positioning. This paper aimsto develop a fully distributed method for cooperative target tracking in wireless sensor networks from two perspectives: (1) energy-balanced tracking and (2) improved estimation accuracy. The first perspective is to build up an energy-balanced tracking network architecture. In this work, the concept of leader-based information processing is conducted to automatically achieve cooperative sensor scheduling with multiple tasking sensors in a cluster-based network topology based on sensor residual energy level, target information, and estimation quality. To avoid the ambiguity, the clusterhead and the cluster members refer to the original network topology, whereas the leader and the sub-cluster members refer to the sensor group for the tracking task. Random timers and local criteria are used to determine the tracking responsibility of the clusters. Afterwards, a sub-cluster of the corresponding cluster for the tracking task is formed by a leader, which can be a clusterhead or a cluster member in the original cluster-based network. The second perspective is to explore the behaviors/characteristics of a target such that supplementary information can be applied to improve estimation accuracy. Within the sub-cluster, the sensing nodes provide their measurements to the leader. Upon receiving the measurements, the leader fuses the local estimates from the sub-cluster members and reports it to the clusterhead. When the target moves out the region of the current active sub-cluster, the leader needs to trigger the leader handoff procedure (detailed in Section 3.4). As shown in [1], compared with the dynamic clustering approach in a flat network topology, the static clustering approach incurs a larger location error since a clusterhead may not be a good local controller for estimating the location and reporting the event due to target movement. However, given a fixed hierarchical network topology, dynamic clustering approaches may not be feasible. Therefore, considering a cluster-based network topology, we introduce a distributed cooperative target tracking system, _Two-level Clustering Approach via Timer_ (TCAT), which aims to improve the energy efficiency and provide good estimation accuracy. Here level-one clustering indicates the original network topology with control of clusterheads. Level-two clustering means the sub-cluster formation for the tracking task with control of tasking leaders. Therefore, the information flow goes through the sub-cluster members to the leader, and then to the clusterhead, and vice versa. Accordingly, the TCAT scheme performs target localization in four phases: (I) Tasking Leader Selection; (II) Choosing the Sub-Cluster Members; (III) Target Positioning; and (IV) Sub-Cluster Member Reselection and Leader Handoff. In Phases I and II, random timers and local information are applied to adaptively select a tasking leader and sub-cluster members for the localization task. In Phase III, the Bayesian particle filter [2,3] is used to estimate the unknown target position from state equations. The objective is to find feasible position to minimize the error of the state vector. After obtaining the initial position estimate, the localization adjustment problem can be solved by applying a neural networking model, which focuses on improving positioning accuracy. Afterwards, the covariance intersection algorithm [4] is adopted to perform estimation fusion. In order to maintain tracking stability, Phase IV performs the leader handoff task.