Estimating the position of mobile devices with high accuracy in indoor environments is of interest across a wide range of applications. Many methods and technologies have been proposed to solve the problem but, to date, there is no "silver bullet". This paper surveys research conducted on indoor positioning using time-based approaches in conjunction with the IEEE 802.11 Wireless Local Area Network standard (WiFi). Location solutions using this approach are particularly attractive due to the wide deployment of WiFi and because prior mapping is not needed. This paper provides an overview of the IEEE 802.11 standards and summarizes the key research challenges in 802.11 time-based positioning. The paper categorizes and describes the many proposals published to date, evaluating their implementation complexity and positioning accuracy. Finally, the paper summarizes the state-of-the-art and makes suggestions for future research directions.

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This letter presents a novel algorithm for Time Of Arrival (TOA) estimation for Orthogonal Frequency Division Multiplexing (OFDM) based transceivers. The algorithm processes the sampled baseband signal to obtain a high resolution estimate of the TOA of the OFDM symbol. In the first step, the algorithm obtains a sample resolution estimate of the TOA by finding the peak of the absolute value of the cross-correlation of the in-phase and quadrature received signals with the known transmitted symbol. In the second step, the algorithm refines this estimate to sub-sample resolution by estimating the phase delay of the received signal based on the gradient of a linear fit to the phase difference between the transmitted and received sub-carriers (in the frequency domain). The algorithm was applied to the Long Training Sequence (LTS) symbol of the IEEE Wireless Local Area Network (WLAN) 802.11g preamble. In real-world experiments, the algorithm was found to achieve a mean TOA estimation error of 49 cm in a low multi-path Line Of Sight (LOS) environment for ranges of 1-7 m.Introduction: Positioning devices with high accuracy in indoor environments is of interest in a wide range of applications. Current indoor location systems for the ubiquitous IEEE 802.11 WLAN standard are based on Received Signal Strength Indicator (RSSI) mapping and fingerprinting. The mapping process is time consuming and the maps are subject to changes in the environment, such as the movement of people and equipment. To date, the reported accuracy of 802.11 RSSI fingerprinting is roughly 3 m [1]. Range based positioning has the potential to eliminate the mapping step but requires high resolution TOA estimation. Previously, Nur et al [2] tackled this problem by increasing the RF sampling frequency to 1 GS/s and introducing an Improved FOCUSS for Arrival Time Estimation (IFATE) algorithm to estimate the channel and the TOA. In experiments and simulations, sub-meter accuracy (average error of 0.62 m) was achieved in indoor LOS environments. Their approach achieved high accuracy, however, it is costly in terms of power consumption and is not supported by conventional receiver architectures which typically have a 40 MHz sampling frequency. Frequency domain super resolution methods [3] have been applied to the conventional baseband signal. MUltiple Slgnal Classification (MUSIC) [4] and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) [5] separate the signal subspace from the noise subspace using eigen-decomposition of the correlation matrix. These super-resolution approaches have been shown to provide accuracies around 3-5 meters [6]. However, they require high Signal to Noise Ratio (SNR), need prior estimation of the number of multi-path components, and have high computational complexity [2].Herein, we propose a novel TOA estimation algorithm that uses the baseband signal from a conventional WLAN transceiver. To the best of the authors' knowledge, this is the first work which proposes a combined cross-correlation and phase adjus...

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