计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 118-122.doi: 10.11896/j.issn.1002-137X.2019.04.019
吴健1, 孙保明2
WU Jian1, SUN Bao-ming2
摘要： 传统的压缩感知定位方法将物理空间离散化为一个固定网格,并假设所有目标准确地落在该网格上,从而将定位问题转化为稀疏重构问题。事实上,目标的随机性导致很难找到满足上述假设的固定网格,进而引起字典失配问题,使得定位性能急剧下降。针对该问题,文中提出一种基于字典优化的压缩感知定位方法,将稀疏字典建模为以网格为参数的参数化字典,通过动态调整网格不断优化稀疏字典,从而将定位问题转化为联合参数优化的稀疏重构问题,并在变分贝叶斯推理框架下解决该问题。仿真结果表明,与传统的压缩感知定位方法相比,所提方法具有更强的可靠性和鲁棒性。
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