Location Estimation via Sparse Signal Reconstruction in Subsampled Overcomplete Dictionaries for Wireless 4G Networks

Abstract

We present a simple and effective means for position estimation designed to be deployed in urban and dense multipath environments characteristic of 4G wireless networks. To address the multipath channel of such environments a fingerprinting scheme is proposed. One of the drawbacks to this class of methods is the large initial cost associated with establishing a database matrix. This issue is addressed by using a multi-channel filtering method adapted from the H.264 video standard to recover the incomplete data. Position estimation is accomplished via a modified knearest neighbor approach to pattern matching. We show through simulation that not only are we able to achieve compelling fidelity in the reconstructed databases from highly incomplete data, but that we are able to do so at a relatively low computational cost. Finally, our results demonstrate that we are able to achieve accurate position estimates vis-à-vis severe undersampling and noisy channel conditions.

Multi-targets device-free localization based on sparse coding in smart city

“…As shown in Table 1, the existing DFL methods mainly include fingerprint recognition, geometric methods, and compressive sensing (CS), where fingerprint-based localization is also integrated into other localization methods [19][20][21][22] to convert the localization problems into fingerprint matching problems. Collecting the fingerprint training database is always the first step, and then some machine learning classifier is adopted to match from the new fingerprint to determine the location of the target.…”

“…Then, similar method is adopted to further reduce the row dimension. The reduction from two dimensions can improve 19 Seifeldin et al, 20 Molina et al, 21 and Roth et al 22 Fingerprint matching Numerous nodes Zhang et al 23…”

“…m q1 is related to the largest singular value s 1 and represents the major characteristic of W q . Therefore, after SVD is performed on all the total K sample matrices, the semi-transform matrix U can be obtained as equation (22), which is constituted by the eigenvectors related to the maximum eigenvalues of all RPs U = u 11 , u 21 , :::, u q1 , :::,…”