Research On Key Technology Of Wireless Location Based On Post-Disaster Complex Environment

With the rapid development of mobile communication and Internet of Things technology,location awareness has not only become a research hotspot,but also become an indispensable part of the security of people’s life and property.In emergency rescue,earthquake relief and other tasks,accurate location measurement is an important support to ensure the smooth development of rescue work.The traditional method of location parameter estimation is difficult to adapt to the complex wireless communication environment after disaster.It is of great significance to develop intelligent,joint and efficient estimation methods for complex environmental parameters.In this paper,due to the complexity of communication environment,the location of emergency positioning station is difficult and the measurement error of positioning parameters is large,and a series of joint positioning algorithms integrating multiple ranging parameters are proposed to reduce the wireless signal ranging error.The main work and innovation of this thesis are as follows:1.Aiming at the large randomness of building collapse after earthquake disaster,complex electromagnetic signal transmission environment and environmental dependence of channel model,the wireless channels in three typical collapsed environments are studied.A wireless channel modeling method for post-disaster environment is proposed based on joint sharpening of normalized power delay spectrum.The path loss model,root mean square delay extension model and tap delay line channel model for typical post-disaster environment are established by using this method.Compared with the comparison model,the root-mean spread delay of the channel model constructed in this paper is improved by 28.92%,16.39%and 20.63%in the three typical environments respectively.2.Aiming at the problems of serious stray signals in the post-disaster ruins environment,anisotropic propagation characteristics,and great influence on the positioning accuracy of the positioning system geometry configuration,an optimization method of anisotropic positioning geometry configuration in a changeable environment was proposed.In this method,the objective function of joint positioning is constructed,and the bidirectional search cross-optimization algorithm is proposed,and the optimal implementation solution of the objective function is obtained,which is transformed into the optimal station arrangement scheme of mobile base stations in the post-disaster environment.Compared with other optimization methods,the proposed method effectively eliminates local optimum and has better global optimization performance.After optimization,the geometric accuracy of positioning is improved by 16.18%.3.Aiming at the problems that wireless signal propagation is blocked,signal attenuation and non-line-of-sight propagation are serious in the post-disaster environment,and the distance and direction of signal acquisition by detection equipment are limited,an optimization method of post-disaster wireless positioning detection equipment based on semi-definite programming is proposed.The location model of multiple detection devices is constructed and the decision vector with variable nodes is generated.The optimization strategy of two-stage detection equipment is established,the reference node and penalty function are determined by the fast-rough positioning of the target,and the node selection algorithm based on semi-definite programming is used to find the optimal combination of nodes to complete the precise positioning.The performance of this algorithm is better than other known optimization algorithms for reference nodes,and the accuracy of locating nodes is higher than 90%.Compared with the fixed main nodes,the positioning accuracy of the optimization algorithm is improved by 15.8%.4.For post-disaster environment wireless signal multipath propagation is serious,unable to accurately identify the first diameter,wireless measurement accuracy problem,this paper proposes a post-disaster environment oriented based on prior learning environment data depth positioning signal measurement,the method of the positioning of the channel frequency response training,through deep learning intelligent extraction first time information and reach the point of view.Through offline training and data verification,the estimation accuracy of arrival time and arrival Angle of first approach path can reach 87%.Compared with the better rotation factor invariant method,the estimation accuracy of arrival time and arrival Angle is improved by 29.63%and 38.7%respectively.