Environmental-adaptive RSS-based indoor localization
Environmental-adaptive RSS-based indoor localization
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作者：
Wang Ting-tin
提出了一种新的两步字典学习（DL）框架来动态调整过完备基（Overcomplete Basis，又称Overcomplete Basis）。字典）用于匹配RSS测量的变化，然后稀疏解可以更好地表示位置估计。此外，提出了一种改进的重加权l1范数最小化算法，以提高稀疏信号的重建性能。实验结果表明，该方案的有效性，目标的位置可以从噪声信号中获得，即使目标的数量是未知的先验。
A novel two-step dictionary learning(DL) framework was proposed to dynamically adjust the overcomplete basis(a.k.a. dictionary) for matching the changes of the RSS measurements, and then the sparse solution can better represent location estimations. Moreover, a modified re-weighting l1 norm minimization algorithm was proposed to improve reconstruction performance for sparse signals. The effectiveness of the proposed scheme is demonstrated by experimental results where the locations of targets can be obtained from noisy signals, even if the number of targets is not known a priori.