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Multi-Node RSS-based Localization with the Aid of Compressed Sensing: An l1-localization Approach

机译:借助压缩感知的基于RSS的多节点本地化:一种l1本地化方法

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In this work try we try to estimate the positions of multiple co-channel wireless nodes along with the unknown transmit power of them. The propagation channel is assumed to be log-normal shadowing model. We propose an unbiased estimator. The underlying complicated optimization problem has a combinatorial nature that selects the best grid points as the location of the targets. We then convert the combinatorial problem to a convex form by means of l1-minimization, or precisely a technique which is inspired by the theory of compressed sensing (CS). The performance of the estimator is justified to be good using simulations.
机译:在这项工作中,我们尝试估计多个同频道无线节点的位置以及它们的未知发射功率。假定传播通道为对数正态阴影模型。我们提出一个无偏估计量。潜在的复杂优化问题具有组合性质,该组合性质选择最佳网格点作为目标位置。然后,我们通过l1最小化或精确地受压缩感测(CS)理论启发的技术将组合问题转换为凸形。使用仿真可以证明估计器的性能良好。

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