Research On Node Localization And Target Tracking For Wireless Sensor Networks

Since sensor node's location in wireless sensor networks plays an important role in many application areas, nodes localization technology is one of the main supporting technologies in WSN. Currently, nodes localization research of wireless sensor networks mainly focused on localization based on beacon nodes and static nodes, which dependents strongly on the beacon nodes and lacks of consideration on the node mobility, So, it is very important to study beacon-free node localization, mobile nodes localization in WSN.Target tracking is a basic application of wireless sensor networks, and localization is the basis for tracking. The essence of target tracking is multi-node cooperative tracking with the key problems of processing distributed information and integrating effective information with the lowest energy cost, limited communications bandwidth and computing power.This dissertation studys node localization and target tracking technology by taking the beacon-free node localization, mobile nodes localization and target tracking as the main contents, and improving the positioning accuracy, reducing node energy consumption and prolonging network lifetime as the starting points, and citing a variety of algorithms and theories.The research work and achievements reflect in the following aspects:The dissertation proposed the beacon-free node localization algorithms based on the probability and based on dynamic clustering respectively. The thought of probability localization is from that Gaussian distribution of measurement results can be described by using the probability density function, and then the process of solving the localization point is the process of maximizing the probability density. As long as the nodes of network have the ability of distance or angle finding, localization can be achieved. While the thought of localization based on dynamic clustering is from cluster analysis and clustering algorithms in fuzzy identification, finding the root cluster head as the origin a virtual coordinate system of whole network by dynamic clustering. Localize nodes in turn through the node ranging function and the triangulation principle.The dissertation proposed a mobile beacon node optimal path acquisition method using ant colony algorithm which fuses number of hops into the pheromone and support multi-path. On the base of ant colony algorithm study, through the nodes distribution probability and transfer probability to calculate the sample's weight and determine the location that the mobile node may be existent, proposed a mobile node localization algorithm based on ant colony algorithm.Adopting maklink graph methods to establish the beacon node space movement model, the dissertation proposed a mobile beacon node path planning based on the particle swarm optimization algorithm. Through the estimate distance and the measuring distance between the beacon node and mobile nodes determine the fitness function of the particle swarm algorithm, and search for the optimal solution in the solution space in as the mobile node's location. And also this dissertation proposed a mobile nodes localization algorithm based on particle swarm algorithm and Monte Carlo algorithm.The dissertation proposed a collaborative tracking algorithm based on Bayesian method by using nodes organizational strategy of dynamic clustering and introducing particle filter. The algorithm is based the distance between sensor nodes and the target as well as the node energy to dynamically establish and remove cluster. The cluster head is the node which has the most energy. When the target moves, the cluster head through the node sleep/wake-up mechanism to form a new tracking cluster according to the cluster head election principle of largest energy and latest from the target, and wake up the nodes around the targets to track. This method maintains tracking continuity and achieves the tracking task by particle filter.Finally, the dissertation designed the battlefield target tracking system platform based on the WSN, including overall system architecture design, system structure and function design, wireless sensor nodes and clusters node design, as well as battlefield command and control center hardware platforms and software systems analysis and design.