運行在動態與未知環境下的多傳感器系統往往會面臨環境與自身結構的漸進式變化,導致一般的具有學習能力的融合方法很難適用. 本文提出了一種具有漸進學習能力的融合方法,它具有良好的自適應性和魯棒性. 該方法由一種名為接受域加權回歸(Receptive FieldWeighted Regression) 的漸進式學習算法和加權平均的融合算法組成.最后以三個攝像機聯合定位作為研究對象,對該方法進行了仿真,驗證了其有效性,同時還和基于BP 神經網絡的融合方法進行了比較.
關鍵詞: 　傳感器融合; 漸進式學習算法; 接受域加權回歸
Abstract : 　The multisensor systems under the dynamic and unknown environment often encounter the incremental modification of environment and its configuration. This results in the fact that the fusion methods with learning ability cannot be suitable any more under this condition. In this paper ,a new fusion method with incremental learning ability is proposed. This method utilizes an incre2 mental learning algorithm called Receptive Field Weighted Regression (RFWR) ,and weighted average is used as the fusion strategy , thus it is more adaptive and robust than previous ones. The problem of three cameras positioning is taken into account and the corre2 sponding simulation is implemented. Simulation results verify the effectiveness of this method. Comparison with BP neural network2 based fusion method is also provided.
Key words : 　sensor fusion ;incremental learning algorithm;RFWR