Indoor ranging signal recovery via regularized CoSaMP

Yun-teng Lu, A. Finger
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Abstract

The indoor ranging signal recovery requires not only the high detection probability but also the excellent detection accuracy, which is related to the matched filter output SINR, especially for radio signal based location and positioning systems, where little deviation of time delay will yield radical error of position estimation. Furthermore, the ranging signals in multi-measurment channels demands the so-called sparse condition [1] for uniquely determining the results. Because the corresponding recovery matrix in terms of time-code frame is a wide matrix, which can not be orthogonalized between all columns any more. So far, many related recovery algorithms haven been developed, like optimization based l1-norm minimization and greedy approaches based OMP, ROMP and CoSaMP [2]. However, these algorithms are either not real-time enough or short of uniform performance in different scenarios. In this paper we will first introduce the novel ranging signals for higher time delay estimation, then develop the corresponding detection algorithm namely Regularized Compressive Sampling Mathing Pursuit (RCoSaMP), which outperforms the most conventional detection approaches.
基于正则化CoSaMP的室内测距信号恢复
室内测距信号的恢复不仅要求检测概率高,而且要求检测精度高,这与匹配滤波器输出信噪比有关,特别是对于基于无线电信号的定位系统,时间延迟的微小偏差会导致位置估计的严重误差。此外,多测量通道的测距信号需要所谓的稀疏条件[1]来唯一地确定结果。由于对应的时间码帧的恢复矩阵是一个宽矩阵,不能再在所有列之间进行正交。到目前为止,已经开发了许多相关的恢复算法,如基于优化的11范数最小化和基于贪心方法的OMP, ROMP和CoSaMP[2]。然而,这些算法要么实时性不够,要么在不同的场景下性能不统一。在本文中,我们将首先介绍用于更高时延估计的新型测距信号,然后开发相应的检测算法,即正则化压缩采样匹配追踪(RCoSaMP),该算法优于大多数传统的检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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