The Study Of Localization Algorithm In Grid Radio Monitoring Network

The newly and vigorously developing radio technology and service have been widely used in all fields of social production and people’s daily life. The radio technology and its equipment are heading for high frequency, broadbandization and low power. When we use the radio equipment, a variety of radio interference is produced. With the accelerated urbanization in China, the buildings in urban are higher and higher, and the electromagnetic environment is more and more complex. Therefore, the traditional radio monitoring network can only make position in the areas which has strong signal. And the monitoring blind area and low locating accuracy which are the shortcoming of the traditional radio monitoring network are becoming more and more obvious. Thus, the grid radio monitoring network comes into being. And it is necessary to find a good performance locating algorithm to improve the performance of the grid monitoring system.This paper firstly introduces the basic principles and common methods of the grid radio locating technology and selects the TDOA location technology as the research object because of its outstanding superiorities, such as simple system, high locating accuracy, low investment of a single station and so on. And then, deduces and establishes the hyperbolic mathematical model of TDOA. On this basis, the three classical localization algorithms involving Chan, Fang, and Taylor, are deduced in detail, and their advantages and disadvantages are analyzed respectively. Moreover, simulates and compares its locating performance.According to the characteristics of the grid radio monitoring network, the largely collected data and high locating accuracy, this paper applies improved genetic algorithm to the grid radio monitoring system. At first, the paper introduces basic principle, operation and process, basic steps of the genetic algorithm. Subsequently, TDOA location mathematical model based on genetic algorithm is established, and the corresponding algorithm is proposed. Lastly, simulation experiment is carried out under the Gaussian environment and the COST259 channel environment. Compared with the Fang, Chan, and Taylor, the simulation results show that the genetic algorithm has higher locating accuracy and more robust performance, and can effectively improve the locating performance of the grid radio monitoring system.