Self-Organizing Target Tracking Based On UWSNs

Underwater target tracking is one of the important research in marine science and technol-ogy,which provides key technologies for the development of marine resources and defense of marine safety.With the advantages of multi-platform detection and real-time data sharing,un-derwater target tracking technology based on Underwater Wireless Sensor Networks(UWSNs)has drawed more and more attention.However,the complicated underwater environment will lead to insufficient measurement information and poor tracking accuracy,while the energy re-source is limited for data processing and transmission.Therefore,the main problem in target tracking based on UWSNs is how to solve the contradiction between tracking accuracy and en-ergy consumption.To this end,based on the self-organizing ability of UWSNs,this dissertation improves tracking accuracy and energy efficiency from several aspects,such as measurement model correction,node depth adjustment,node energy allocation and data fusion.Overall,the main contributions of this dissertation can be summarized as follows:Firstly,to avoid the measurement model deviation caused by variation of underwater acous-tic propagation speed,this dissertation proposes a measurement model correction based target tracking algorithm.A correction parameter is introduced to solve the influence of acoustic speed variation,and it is estimated using accumulated information of past time.The self-organizing correction of measurement model is realized by iterative updating of correction parameters,and provides more accurate measurement model and data.Secondly,due to the sparse deployment of UWSNs,this dissertation proposes a node depth adjustment based target tracking to improve measurement information.The dynamic node depth adjustment scheme is designed using the relation between node depth and tracking accuracy.It can obtain more complete measurement information by self-organizing adjustment of node-target geometry.Thirdly,for the purpose of reducing information loss caused by energy allocation in UWSNs,this dissertation proposes a non-myopic energy allocation algorithm for target tracking.To achieve the self-organizing energy allocation,the measurement is valued to determine quantiza-tion bit.The overall tracking accuracy and energy efficiency is maximized with limited energy resource.Finally,considering the measurement difference of each node in multi-source data fusion,this dissertation proposes a mutual information based fusion algorithm for target tracking.The mutual information between quantized measurement and target state is derived to determine fusion weight for nodes.A weighted fusion algorithm is designed based on mutual information,to reduce information loss caused by energy allocation in UWSNs.In general,this dissertation focuses on self-organizing target tracking in UWSNs.Com-plete theoretical results are achieved by improving tracking accuracy and energy efficiency from various aspects,which contribute to the research and development of self-organization and tar-get tracking in UWSNs.