Research On Target Localization And Tracking Based On Relative Measurements

Since the 21 st century,wireless sensor has been playing an irreplaceable role in modern engineering environment because of its small size and strong scalability.Multiple micro sensors connect with each other to form a sensor network,which makes the information acquisition in the whole detected range more convenient and reliable.At present,using sensor networks to track and locate mobile targets will bring more innovative ideas to the emerging UAV measurement and control,unmanned driving and the other directions.And the thesis will also provide applicable algorithm theory for the use of 5G technology in unmanned driving.This thesis based on the sensors which can measure the relative distance between itself and the other targets.For non-linear sensor,energy-constrained projects and low data transmission efficiency issues,the following will discuss.This thesis discusses the positioning and tracking technology of the moving target in the distributed sensor network,and the selflocalization technology of the distributed sensor network through the mobile anchor.1.A hierarchical sensor network structure is designed to improve the integration efficiency.Through the hierarchical sensor structure,the lower layer and the upper layer of the sensor estimation information can be fused respectively.In this way,the data can be fused efficiently and the measurement information can be fully used.For the estimation algorithm,firstly,the initial position of the target is located by nonlinear optimization.Then the target is tracked by the repeated estimation process.The state of the moving target is estimated by the extended Kalman filter.The estimates can be fused by SCI algorithm.And the state is reset by the nonlinear optimization.Finally,whether to close the reset is determined by the difference before and after the reset.When the reset is turned off,only the extended Kalman filter can be used to track the target.By reducing the iterative process,the efficiency can be greatly improved.This algorithm not only guarantees the fast convergence of the estimation,but also uses nonlinear optimization to ensure the accuracy,reduces the calculation amount and channel transmission pressure.2.A sensor network with self-localization system is designed to deal with the problem in the sensor network.The main problem is how to obtain every sensor's position accurately.The designed sensor can measure the relative distance between itself and the moving anchor.The moving anchor can periodically send its own state signal to the surrounding sensors.The sensor can store multiple groups of measurements and signal data through the moving window,which can update the latest data and discard the oldest data.After the data is collected,the window is updated by using the data filter.When the error is too large,the group of data will be discarded.Finally,the sensor optimizes its own state according to many groups of data,and finally obtains its own state information accurately by updating the measurement data.Finally,the simulation software is used to verify the above algorithms.The verification process includes: designing the estimation problem in the same scene,using the algorithm designed in this thesis and the other algorithms to estimate.Then,the thesis uses the chart to compare the measured data in the fast,accuracy and so on.Finally,it is found that the performance of the algorithm in this thesis is better than the other ones,which is more conducive to engineering application.