چكيده به لاتين
Multi Sensor Systems are network of several sensors or radars. Each sensor located in a different environment and generates extractable data and information about environments and Moving Objects. One of the important issues for using Sensor/Radar data in multi sensor systems is the integration and fusion of received data. This data reports the position and attributes of moving objects that has been extracted from several sensors, this sensors located at different position. Due to the limited range of the sensors, their homogeneity and non-synchronization and the need for information intersection to make optimal decisions about the environment around the multi-sensor system, it is necessary that received data combining in a certain process and to be produce comprehensive data. Data Fusion has different levels, and the topics related to it are very wide. In a general classification, there are four levels: Objects Recognition and Estimation, Combination of Attributes, Analysis and Decision Making, Knowledge Extraction. Most of the challenges and unresolved issues in the our country are related to the first levels of integration, because most of the sensors in the our country and their communication infrastructure have not good quality. Furthermore without performing processes of level first and two, it will not be practically possible to extract knowledge and make decisions about the data received from the sensors. In this research, common methods in data integrating and data fusion for multi-sensor data are introduced and described, and then advantages and disadvantages of different methods will be evluated. Then a optimal method based-on potential and exist needs will be designed. After reviewing the theoretical concepts and designing an optimal algorithm for data fusion, a system will be implemented and presented finally that performs the data fusion process at the level one and two and do visualization of moving objects with desirable quality.
