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一种具有渐进学习能力的融合方法
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2009-07-08
打马过草原
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运行在动态与未知环境下的多传感器系统往往会面临环境与自身结构的渐进式变化,导致一般的具有学习能力的融合方法很难适用. 本文提出了一种具有渐进学习能力的融合方法,它具有良好的自适应性和鲁棒性. 该方法由一种名为接受域加权回归(Receptive FieldWeighted Regression) 的渐进式学习算法和加权平均的融合算法组成.最后以三个摄像机联合定位作为研究对象,对该方法进行了仿真,验证了其有效性,同时还和基于BP 神经网络的融合方法进行了比较.
关键词: 传感器融合; 渐进式学习算法; 接受域加权回归
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
关键词: 传感器融合; 渐进式学习算法; 接受域加权回归
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
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