Investigation Of Mobile Wsn Localization Technology Based On Particle Filter

Localization plays a crucial role in wireless sensor networks (WSN) as most of WSN applications need the awareness of the node's location. Localization algorithms with high accuracy and low complexity are very important for WSN. Traditional wireless sensor networks localization algorithms only apply to a static situation, when the nodes move, lots of energy will be consumed by the periodically repeated localization. Therefore, it is urgent to study the mobile localization and tracking algorithm which is suited for mobile wireless sensor networks. This thesis focuses on mobile wireless sensor networks location techniques based on Sequential Monte Carlo (SMC) method and target tracking based on particle filter (PF).First of all, this thesis introduces an overview of wireless sensor networks localization algorithms at home and on abroad, and analyzes the classical localization algorithms of static wireless sensor networks and mobile wireless sensor networks, and then compares the performance of classical location algorithms. The criterion of performance evaluation of mobile wireless sensor networks localization is also summarized.Secondly, this thesis introduces the particle filter which is suit for non-linear, non-Gaussian, the Bayesian filtering theory and particle filter (PF) are described in detail. The properties of Extended Kalman filter (EKF), unscented Kalman filter (UKF) and standard particle filter are simulated and analyzed. Take the energy-constrained characteristics of the wireless sensor networks into account, this thesis introduces five operating mode of particle filter which are applied in wireless sensor networks, simulates and analyzes the target tracking algorithms of distributed particle filter in wireless sensor networks.Thirdly, this thesis analyzes the performance and characteristics of Monte Carlo localization (MCL) and its improved algorithm, such as Monte Carlo localization Boxed (MCB), Multi-hop-based Monte Carlo localization (MMCL), then compares their performance from the aspects of localization accuracy, sample number, node density, anchor node density, node velocity and localization ratio base on MATLAB simulation. Based on MCB algorithm we propose a sample Adaptive Monte Carlo Localization Boxed (AMCB) mobile localization algorithm, simulation results demonstrate that the proposed algorithm produces good localization accuracy as well as low computational cost compared with MCL and MCB. Aim at the speciality of the localization accuracy of MMCL algorithm is high when the anchor density is low, take the merit of DRL dynamically changing of anchor's flooding-hop into account, we propose a Adaptive Multi-hop-based Monte Carlo Localization (AMMCL) algorithm. Simulation results show that the proposed algorithm produces better localization accuracy as well as low communication cost compared with MMCL.Finally, the research work of this paper is concluded and the future research topics are presented.