Sensor selection algorithm based on weighted distance in wireless sensor networks tracking
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摘要: 节点选择存在于无线传感器网的目标跟踪问题中,主要任务是从多个传感器中选取合适节点跟踪当前目标,满足跟踪精度、算法计算量的要求.提出一种适用于测角传感器节点的加权距离选择法,该算法利用目标状态预测的分布及节点的探测模型,通过计算节点距目标几何距离及加权系数,选择具有最小加权距离的传感器点进行探测,避开了贝叶斯滤波,在减少计算量的同时具有很好的选择精度.仿真结果表明,本算法大幅度减少了计算量,同时可达到和熵值法相当的跟踪定位效果.Abstract: A new sensor selection methods for bearings-only sensors, weighted-distance node selection (WNS) was proposed. Based on probability distribution function (PDF) of target estimates and sensing-model, the sensors with the minimum weighted-distance were activated in the next snapshot. Since the existing mahalanobis distance measure, though yields a good precision with low computational complexity, fits only range sensors and the algorithms based on the PDF, such as entropy method, are computationally burdensome, WNS avoids the Bayes filtering process and achieves alluring precision. Simulation results show that the computation complexity is indeed reduced while a comparable selection precision is achieved as entropy method.
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Key words:
- wireless sensor networks /
- Bayes filter /
- target tracking /
- sensor selection problem
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