|Abstract:||The concept of Location Based Services (LBSs) has been widely used these days due to the emerging business market, which include a lot of services such as emergency services, tracking assets, etc. With the introduction wireless networks and the growing number of wireless devices are all putting tremendous stress on legacy 802.11a/b/g/n Wi-Fi networks. With new innovations that allow for more reliable coverage, 802.11ac Wi-Fi technology addresses many challenges, allowing mobile device users to stream digital content between devices faster, and simultaneously connect more wireless devices to home and enterprise networks, while conserving battery power. Considerable interests have been introduced for its capabilities to provide localization in indoor environments. 802.11ac WiFi technology operates using fine timing resolution technology, resulting in highly accurate positioning regardless of environmental factors. Previous versions of indoor positioning relied on received signal strength indicator (RSSI) technology, where signal strength and performance can vary depending on environmental factors such as crowd density or temperature. In this paper, we propose a super-resolution ranging measurements technique using Fast Orthogonal Search (FOS) which can be used in 802.11ac Wi-Fi networks. This super-resolution ranging measurements are based on estimating the time of flight (or its differences) as measured via Direct Path (DP) from the transmitter to the receiver. Simulation testing is provided to show the improvements of the proposed approach over the conventional ranging estimation approaches. We explain the shortcomings of the conventional ranging estimation in legacy Wi-Fi localization approaches. These shortcomings arise when Line-Of-Sight (LOS) measurements from the transmitter to the receiver is followed closely by a number of multipath signals which arrive at the receiver end within a short time delay. Given that this delay can be smaller than the duration of the Rayleigh resolution of the system, the LOS signal and the multipath signals will suffer from overlapping and consequently introducing a bias in the time delay estimated by the peak detector. To overcome this problem, several super-resolution estimation algorithms have been introduced and classified as Least Squares (LS) and subspace methods. As for the LS methods, when the LOS signal and the multipath signals are spaced closer than the Rayleigh resolution, significant additional errors will exist due to the noise enhancement that arises from the ill-conditioned nature of the matrices that are involved in the LS operation. Therefore, sub-space methods have been introduced to overcome the noise enhancement problem related to LS methods. Sub-space methods are based on the Singular Value Decomposition (SVD) algorithm which splits the received signal into two orthogonal subspaces known as the signal subspace and the noise subspace using spectral (or Eigen) decomposition. However, if the received LOS signal and multipath signals are partially correlated, which is the case in closely spaced multipath signals, the Eigen value matrix is no longer diagonal (i.e. becomes defective), and therefore the received signal cannot be reconstructed due to the shortage of Eigen vectors which affects the SVD algorithm performance. In this paper, the correlation between closely spaced signals has been investigated. We propose a multi-resolution orthogonal search in the signal subspace in addition to projecting the received signal into two orthogonal sub-spaces: signal and noise. The proposed approach provides that the correlated rays are projected into different orthogonal bases. The Mean Square Error (MSE) is minimized to evaluate the contribution of the resolved signal(s) such that the sufficiency of the proposed multi-resolution orthogonal search, at each step is satisfied. Fast orthogonal search is employed in this process. Assessment of the proposed method in resolving correlated multipath signals is investigated through lab testing results to compare the probability of detection/resolution of the multi-resolution technique to conventional techniques.|
Proceedings of the 2016 International Technical Meeting of The Institute of Navigation
January 25 - 28, 2016
Hyatt Regency Monterey
|Pages:||324 - 332|
|Cite this article:||
Sheta, Bassem I., Youssef, Mohamed, "Position using Fast Orthogonal Search in Wi-Fi Technology," Proceedings of the 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2016, pp. 324-332.
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