GPS World, February 2016

Looking at Figure 13 it can be seen that the vehicle kept crossing its path near one location which can be determined to be an estimate of the location of the signal source It is worth noting that the baseline method does take four steps to get in the region of the signal source and then another four or five steps for the user to be confident that the vehicle is in the vicinity of the signal source POMDP Localization With a baseline determined the POMDP approach was executed from the same starting location and used the simple max bearing method for determining bearing from each location This localization took a mere two steps and three measurements to be able to locate the signal source FIGURE 14 shows the state updates as the vehicle made subsequent measurements After the first measurement is made at the starting location the vehicle is able to immediately narrow down the location of the signal source to a small region within the grid Unlike the simple method of moving slowly in the direction of the max bearing the POMDP method can make large changes in order to get to the next best location to make a measurement When running this algorithm we had an assumption that when the vehicle is in the same cell as the signal source a null measurement would be made Unfortunately near and over the signal source resulted in noisy measurements and that noise resulted in location of the signal source being off by one cell Effect on Algorithm The experiments in this paper were performed to obtain a better observation model for the localization algorithm Previously the model assumed 10 degree noise except when the vehicle was in the same cell as the jammer there the modeled assumed a null measurement would be obtained These assumptions were used in the experimental trajectory shown in FIGURE 15 and affected the selected trajectory The vehicle always moved toward regions with high probability of containing the jammer the dark red cells Because we assumed that rotation would only yield a null measurement when over the jammer receiving a null observation after rotating would convince the vehicle that the jammer was in its current cell For this reason the vehicle moves to regions with high probability of containing the jammer it hoped to receive this high information measurement and solve the problem with a single rotation Experimental results have shown that measurement noise increases greatly close to the jammer Our new model assumes 40 degree noise if the jammer is in any of the adjacent grid cells when the vehicle rotates and 13 degree noise if the jammer is farther away If the vehicle rotates in the same cell containing the jammer it no longer receives a null measurement Instead it can receive any measurement with uniform probability Generating a policy with this new model leads to different trajectories A simulated rerun of the experimental trajectory from Figure 15 is shown in FIGURE 16 The vehicle avoids the darker cells which indicate higher probability of containing the jammer Instead the vehicle chooses to rotate in cells it believes are farther away from the jammer to avoid possible measurement noise CONCLUSION This article presents the development of the localization component of a UAV to locate the source of a GPS jamming signal For the scenarios tested modeling the localization as a POMDP is a viable solution that can locate a static signal source in very few steps It is faster and has greater confidence than a simple greedy search baseline solution Through extensive test flights using a single directional antenna and rotation based measurements three different bearing methods have been analyzed All three methods suffered when near the signal source due to the antenna FEBRUARY 2016 WWW GPSWORLD COM GPS WORLD 55