無線傳感器網(wǎng)絡中面向壓縮感知定位的動態(tài)字典算法
doi: 10.11999/JEIT161379
基金項目:
國家自然科學基金(61571463, 61371124, 61472445)
Dynamic Dictionary Algorithm for CS-based Localization in Wireless Sensor Networks
Funds:
The National Natural Science Foundation of China (61571463, 61371124, 61472445)
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摘要: 傳統(tǒng)的壓縮感知定位方法均假設目標準確落在某一預設的固定網(wǎng)格上。當目標偏離該網(wǎng)格，所采用的字典與真實稀疏表示字典之間存在失配，導致這些方法的定位性能大大降低。針對該問題，該文提出一種面向壓縮感知定位的動態(tài)字典算法。該算法將真實稀疏表示字典建模為一個以網(wǎng)格為參數(shù)的動態(tài)字典，從而將定位問題轉化為聯(lián)合稀疏重構和參數(shù)估計問題。利用一階泰勒展開對真實稀疏表示字典進行近似，將非凸的參數(shù)優(yōu)化問題松弛為凸優(yōu)化問題。仿真結果表明，相比于傳統(tǒng)的靜態(tài)字典算法，該文所提出的動態(tài)字典算法具有更好的性能。
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關鍵詞:
- 無線傳感器網(wǎng)絡 /
- 壓縮感知 /
- 動態(tài)字典 /
- 泰勒近似
Abstract: Traditional Compressive Sensing (CS)-based localization methods assume all targets fall on a pre- sampled and fixed grid. There will be mismatch between the adopted and actual sparsifying dictionaries when some targets fall off the grid, leading these methods to perform poorly. To address this problem, an efficient dynamic dictionary algorithm is developed for CS-based localization. To achieve this, the actual sparsifying dictionary is modeled as a parameterized dictionary with the grid viewed as adjustable parameters. By doing so, the localization problem is formulated as a joint sparse reconstruction and parameter estimation problem. Additionally, the non-convex parameter optimization problem is transformed into a tractable convex problem by approximating the actual sparsifying dictionary with its first Taylor expansion. Extensive simulation results show that the proposed dynamic dictionary algorithm provides better performance than the state-of-the-art fixed dictionary algorithms. -
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