基于改进粒子群优化的一致贴近度信息融合算法

贴近度表达了传感器的模糊测量,对信息融合的精确度有着至关重要的影响.用一致可靠测度来描述传感器的模糊测量,提出基于粒子群优化的一致贴近度融合算法,该算法建立了多目标可靠测度的数据模型,并定义多只传感器问的贴近度,利用改进的粒子群算法客观地确定模型中各种权值,根据一致可靠测度给出最终的融合算法,实例验证了算法的有效性.

Consensus closeness degree information fusion algorithm based on modified particle swarm optimization

The closeness degree describes the fuzzy measurements of sensors,and it plays an important role in the accuracy of information fusion.A consistent and reliable measure is used to describe the fuzzy measurement of the sensor,and a consistent approaching degree fusion algorithm based on particle swarm optimization is proposed.This algorithm establishes a multi-objective and reliable measure of the data model,and the approaching degree between multiple sensors is defined.Using the improved particle swarm optimization algorithm to determine the variety of weights of the model objectively,the ultimate fusion algorithm is given according to the consistent and reliable measurement,and give an example to verify the effectiveness of the algorithm.