Performance Analysis of Probabilistic Neural Network Data Fusion Algorithms

• Main Authors: Kuan-Yu Lin, 林冠佑
• Other Authors: Li-Wei Fong
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/08209130999429651632

碩士 === 育達商業技術學院 === 資訊管理所 === 93 === The desired improvements of multi-sensor network tracking system rely on more accurate state estimates and less computation loads. An algorithm is presented to the problem of a distributed multi-sensor network track to track data fusion. For sensor level, to reduce the computational loads involved in physical implementation, the method is essentially based on the decoupling technique that Kalman filter gain formulations are recursively computed. For central level, an approach called Probabilistic Neural Network algorithm is utilized to process state estimation using track data transmitted from sensor level. Performance results for the proposed algorithm are compared with that of the sensor level, using computer simulations of typical target maneuvering scenarios.