摘要
为了有效地检测感知区域内的信息,须研究传感器配置问题。传感器融合在信息检测中起着重要作用,影响着传感器配置结果。但是,已有的传感器配置方法,却较少地考虑传感器融合在传感器配置过程中的作用。针对传感器配置研究的这一不足,提出了一种传感器配置方法。首先基于证据(D—S)理论,建立了传感器配置模型;然后提出了一种改进的粒子群优化算法(Particle Swarm Optimization,PSO),以求解出传感器配置结果。仿真实验表明:提出的方法是有效的,与对比方法相比,在给定检测精度下,明显地减少了传感器数量,降低配置代价,改善了配置结果。
Sensors deployment is one the key technologies to effectively finish target - detecting in the field to be sensed. Sensors fusion has an important effect on sensors deployment, but the known existing ways rarely take the effect of sensors fusion on sensors deployment into account. To overcome the shortage of the study of sensors deployment, this paper puts forward a new sensor deployment method, in which a model of sensor deployment based on the D - S evidence theory is established. To work out deployment model, the paper presents an improved discrete particle swarm optimizer （PSO）. The simulation shows： compared with the contrast method, under the conditions of a fixed detecting precision, the new way has a distinct decrease in the number of deployment sensors.
出处
《计算机仿真》
CSCD
2007年第6期311-314,共4页
Computer Simulation
基金
全国优秀博士学位论文作者专项资金资助(200237)
关键词
传感器配置
证据理论
优化模型
粒子群
Sensor deployment
D - S evidence theory
Optimized model
PSO
