电光与控制, 2018, 25 (12): 5, 网络出版: 2018-12-17  

基于HMM和信息熵的分布式传感器协同任务规划研究

Distributed Sensor Task Collaborative Planning Based on HMM and Information Entropy
作者单位
1 西北工业大学电子信息学院,西安 710129
2 沈阳飞机设计研究所,沈阳 110035
摘要
为了研究多平台传感器最优化任务规划问题中的随机性和不确定性因素,以隐马尔可夫模型(HMM)建模思想和信息熵理论为支撑,以平台传感器执行任务的单位损耗所带来的信息增益为目标函数,建立了基于多HMM过程的传感器动态规划模型,研究了多HMM过程的传感器动态规划执行步骤并进行了仿真。同时研究了在特殊情况下使用多HMM过程进行传感器动态调度的相关问题,为多平台传感器最优化任务规划问题的不确定性和随机性因素的建模与分析奠定了基础。
Abstract
In order to study the random and uncertainty factors in task planning of multi-platform sensor optimization, the Hidden Markov Model (HMM) modeling theory and the information entropy theory were used to support the unit loss of the task.The information gain brought by the unit loss of the platform sensor was taken as the target function.The dynamic programming model of the sensor based on the multi-HMM process was established, and the steps of dynamic programming for the multi-sensor task planning problem were set and simulated.At the same time, we also discussed the dynamic scheduling of the multiple sensors in special cases using the multi-HMM process, which lays the foundation for the modeling and analysis of the uncertainty and stochastic factors for the multi-platform sensor scheduling optimization problem.

张耀中, 姚康佳, 郭操. 基于HMM和信息熵的分布式传感器协同任务规划研究[J]. 电光与控制, 2018, 25(12): 5. ZHANG Yao-zhong, YAO Kang-jia, GUO Cao. Distributed Sensor Task Collaborative Planning Based on HMM and Information Entropy[J]. Electronics Optics & Control, 2018, 25(12): 5.

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