Arithmetic Analysis And Software Realization Of Groud Multi-sensor Data Fusion

The operating principle and detecting property of ground multiple sensors are described in detail. On the basis of data fusion theory and algorithms, not only common target identification algorithms, Bayes reasoning and Dempster-Shafer evidence theory, are analyzed respectively and compared for the characteristics of ground sensors, but also the algorithms widely used in ground target tracking, nearest neighbour method, dynamic multi-factor method, joint probabilistic data association algorithm, fuzzy data association algorithm, are analyzed theoretically. With the measurement characteristics of ground sensors considered, the algorithms are compared, and their advantages and disadvantages are analyzed. Based on common algorithms, an improved fuzzy data association algorithm, logic inference and fuzzy similarity algorithm, is presented.The emphasis is put on the thorough analysis of ground multi-sensor data fusion algorithm based on logic inference and fuzzy similarity. Combined with the measurement characteristics of ground sensors, the data fusion model and alogrithm based on logic inference and fuzzy similarity are given. Meanwhile, Dempster-Shafer evidence theory is used for target identification and least square method is selected for target trajectory smoothing.Not only the improved fuzzy data association algorithm is expounded theoretically, but also the algorithm is realized with software, which is described in detail. The algorithm and software are validated with a great deal of data simulation and practical outfield experiment. Simulation and test results show that the processing results of the algorithm coincide with practical cases on the whole, and both the algorithm and the software are effective. With logic inference and fuzzy similarity algorithm used, ground multi-sensor data fusion problem is solved.