本文關鍵詞： 多目標定位 SL 壓縮感知 無線傳感網絡　出處：《計算機工程與應用》2017年04期 　論文類型：期刊論文

【摘要】：為提高定位的精度與速度,將改進的平滑l_0(smoothed l_0,SL0)壓縮感知算法應用于無線傳感網絡(WSN)定位中。首先通過感知區(qū)域的網格化,將定位問題轉化為壓縮感知問題,采用更陡峭的近似雙曲正切函數(shù)去逼近l_0范數(shù),將壓縮感知重構中的l_0范數(shù)最小化問題轉化為求解光滑函數(shù)最小值的最優(yōu)化問題。其次,針對算法中因最速下降法"鋸齒現(xiàn)象"導致的收斂速度慢、估計不精確等缺點,引入了混合優(yōu)化算法,該算法結合了最速下降法和修正牛頓法的優(yōu)點,提高了重構精度和速度。仿真結果表明,改進的SL0算法相對于匹配追蹤(OMP)、基追蹤(BP)、SL0算法等在定位精度與實時性上有了明顯提高。.
[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.
【作者單位】： 燕山大學工業(yè)計算機控制工程河北省重點實驗室;
【基金】：國家自然科學基金(No.61172095)
【分類號】：TP212.9;TN929.5
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本文編號：1507802