5. Conclusion and Future Work
In this paper, localization accuracy with respect to the standard Horus technique is achieved at the expense of O(N3) computational complexity. This complexity arises from wi-fi signal prediction at each location using Gaussian process regression (GPR). To further reduce it, network segmentation having complexity O(N3), N ≤ N, can be employed. Future work includes developing a real-time positioning system that localize as we go, i.e, calibration-free localization. We collect approximately ten wi-fi signal snap-shots at each location to minimize the effect of small-scale fading. The performance of this localization system will be affected if we collect a very few number of snap-shots. This is because wi-fi signal is not very reliable. In this case, at some locations, corresponding wi-fi signal may be treated as outliers. If the number of such outliers is less than the 50% of the total wi-fi observations at all locations, Gaussian process with Student-t likelihood can be utilized, in this context to deal with outliers. Although, conditional posterior is intractable for this non-Gaussian likelihood, and approximation is required with Markov chain Monte Carlo, Laplace approximation, or expectation propagation algorithm
