无线传感器网络下移动扩散源追踪算法

发布时间：2018-03-11 15:40

本文选题：离散化浓度模型　切入点：无线传感器网络　出处：《控制理论与应用》2014年02期 　论文类型：期刊论文

【摘要】：与移动瞬时源追踪相比,移动扩散源追踪相对困难.本文分析了移动扩散源扩散过程,给出了离散化浓度场模型.将连续线源目标追踪问题转化为离散点源目标追踪的次优问题,提出了一种离散化移动扩散源追踪算法.在该算法中,先采用约束最小二乘方法估计目标实时位置、到达时间等相关参数,并进一步采用仅针对位置序列的Sage-Husa卡尔曼滤波方法优化位置估计.该算法克服了一般基于动态序列的追踪方法无法直接应用于离散移动扩散源追踪问题的不足.在仿真实验中,分别在匀速率平滑曲线运动与变速非平滑曲线运动的情形下进行追踪实验,分析了追踪精度与采样间隔以及观测节点密度的关系.仿真结果说明了提出的移动扩散源追踪算法的有效性.
[Abstract]:Compared with moving instantaneous source tracing, mobile diffusion source tracking is relatively difficult. In this paper, the diffusion process of mobile diffusion source is analyzed. In this paper, a discrete concentration field model is given. The problem of continuous line source target tracking is transformed into a suboptimal problem of discrete point source target tracking, and a discrete moving diffusion source tracking algorithm is proposed. First, the constrained least square method is used to estimate the real time position, arrival time and other related parameters of the target. Furthermore, the Sage-Husa Kalman filter for position sequence is used to optimize the position estimation. The algorithm overcomes the shortcoming that the general tracking method based on dynamic sequence can not be directly applied to the discrete mobile diffusion source tracking problem. In the case of uniform rate smoothing curve motion and variable speed non-smooth curve motion, the tracking experiments are carried out respectively. The relationship between the tracking accuracy and the sampling interval and the density of the observed nodes is analyzed. The simulation results show the effectiveness of the proposed tracking algorithm for moving diffusion sources.
【作者单位】：武汉科技大学冶金自动化与检测技术教育部工程研究中心;
【基金】：国家自然科学基金资助项目(61171160) 湖北省高等学校优秀中青年科技创新团队计划资助项目(T201302)
【分类号】：TP212.9;TN929.5

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