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.%传统的压缩感知定位方法均假设目标准确落在某一预设的固定网格上.当目标偏离该网格,所采用的字典与真实稀疏表示字典之间存在失配,导致这些方法的定位性能大大降低.针对该问题,该文提出一种面向压缩感知定位的动态字典算法.该算法将真实稀疏表示字典建模为一个以网格为参数的动态字典,从而将定位问题转化为联合稀疏重构和参数估计问题.利用一阶泰勒展开对真实稀疏表示字典进行近似,将非凸的参数优化问题松弛为凸优化问题.仿真结果表明,相比于传统的静态字典算法,该文所提出的动态字典算法具有更好的性能.
展开▼
