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UAVForge was a Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center, Atlantic (SSC Atlantic) initiative to leverage the exchange of ideas among an international community united through common interests and inspired by creative thought. More than 140 teams and 3500 registered citizen scientists from 153 countries participated in this year long event. From several selection rounds, a core of nine teams competed in the fly-off event and in June 2012 Team HALO from the UK was declared the winner scoring 47.7 points out of a total of 60 points, with their co-axial tri-rotor, Y6 (VTOL) small Unmanned Aircraft System (sUAS).

In this paper, we focus on preamble-based time of arrival (TOA) estimation for orthogonal frequency division multiplexing (OFDM) systems in non-line-of-sight (NLOS) environments. Recent development in wireless communication-based positioning systems exploiting TOA methods faces a major challenge for the TOA estimation in NLOS condition. Because of the possible obstruction of the direct path, the signal component from direct propagation can be very weak and therefore, the performance of TOA estimation will be dramatically degraded. An accurate TOA estimation utilizing channel estimation is presented. The proposed approach consists of three stages. First, we obtain coarse integer TOA estimation by correlation detection. Second, Maximum-likelihood criterion is employed in channel impulse response estimation to get the fine integer TOA estimate. We exploit the multipath interference cancellation with channel equalization in frequency domain. Finally, to break the limitation by sampling interval, fractional TOA estimate by linear fit is obtained. Compared with the off-the-shelf method, the simulation results show that our method achieves more precise TOA estimation.