基于改进SL0压缩感知的WSN多目标定位

发布时间：2018-02-13 09:07

  本文关键词： 多目标定位 SL 压缩感知 无线传感网络　出处：《计算机工程与应用》2017年04期 　论文类型：期刊论文

【摘要】：为提高定位的精度与速度,将改进的平滑l_0(smoothed l_0,SL0)压缩感知算法应用于无线传感网络(WSN)定位中。首先通过感知区域的网格化,将定位问题转化为压缩感知问题,采用更陡峭的近似双曲正切函数去逼近l_0范数,将压缩感知重构中的l_0范数最小化问题转化为求解光滑函数最小值的最优化问题。其次,针对算法中因最速下降法"锯齿现象"导致的收敛速度慢、估计不精确等缺点,引入了混合优化算法,该算法结合了最速下降法和修正牛顿法的优点,提高了重构精度和速度。仿真结果表明,改进的SL0算法相对于匹配追踪(OMP)、基追踪(BP)、SL0算法等在定位精度与实时性上有了明显提高。.
[Abstract]:In order to improve the accuracy and speed of the location, the improved algorithm of smooth smooth L0 / SL0) compression perception is applied to the wireless sensor network (WSNs) localization. Firstly, the localization problem is transformed into the compressed sensing problem through the gridding of the perceptual region. The steeper approximate hyperbolic tangent function is used to approximate L _ 0 norm, and the minimization problem of L _ s _ 0 norm in compressed perception reconstruction is transformed into an optimization problem for solving the minimum value of smooth function. Aiming at the shortcomings of the steepest descent method, such as slow convergence rate and inaccurate estimation, a hybrid optimization algorithm is introduced, which combines the advantages of the steepest descent method and the modified Newton method. The simulation results show that the improved SL0 algorithm can improve the positioning accuracy and real time performance compared with the matching tracking algorithm.
【作者单位】： 燕山大学工业计算机控制工程河北省重点实验室;
【基金】：国家自然科学基金(No.61172095)
【分类号】：TP212.9;TN929.5
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本文编号：1507802