We present a precise and robust GPS spoofing mitigation method, which is based on a position likelihood distribution. Compared to existing spoofing mitigation methods, this maximum likelihood method is less prone to selecting the wrong satellite signals when (spoofed) duplicates are received. The presented method operates from GPS signal snapshots as short as one millisecond and detects all likely receiver positions. Even with spoofing signals stronger than the authentic satellite signals, the actual receiver position is still found through iterative dampening of the strongest signals. The spoofing mitigation capability is evaluated on the de facto standard TEXBAT spoofing dataset. We reach median position errors introduced by spoofing below 19 m and keep the maximum error below 222 m for all TEXBAT scenarios. This is six times more accurate than the best previous work, which only detects spoofing attacks, but does not mitigate them.

A differential acoustic OFDM technique is presented to embed data imperceptibly in existing music. The method allows playing back music containing the data with a speaker without users noticing the embedded data channel. Using a microphone, the data can be recovered from the recording. Experiments with smartphone microphones show that transmission distances of 24 meters are possible, while achieving bit error ratios of less than 10 percent, depending on the environment. Furthermore, we present a user study which shows that many people do not recognize the added data channel in music, even when being informed about the experiment and therefore actively listening for the data transmission. Depending on the source music, data rates of 300 to 400 bits per second are achieved.

This paper presents an efficient traffic scheduling algorithm for vehicles such as cars, trains or ships. We provide guarantees for deadlock and starvation freedom, therefore ensuring progress for each vehicle in the system. Our method tolerates vehicles which do not disappear from the traffic network once they reach their destination, but rather continue towards subsequent destinations. Therefore, vehicles can run indefinitely. We introduce the concept of "safe spots", which are locations where a vehicle can stop without ever blocking another vehicle. Using such safe spots, we divide routes into short segments, which reduces the number of routing alternatives exponentially, thus allowing real-time traffic allocation.

GPS is used for outdoor localization in a large variety of applications. Current receivers consume too much power for energy-constrained situations like continuous location tracking on small wearable devices. Mainly, this is due to the large amount of GPS signal that has to be decoded to compute the first position fix. While Coarse-Time Navigation (CTN) can reduce the necessary signal to a few milliseconds, it is not robust to noise. Collective Detection (CD) of satellites can mitigate noise to some degree, but the basic method is computationally expensive. We show how CD can be solved optimally and efficiently. Furthermore, we improve the accuracy of CD by exploiting the shape of the likelihood function. All our results are based on real-world signal observations and we achieve localization accuracies of less than 25 meters using a single millisecond of signal. When using 10 consecutive millisecond samples the accuracy improves to less than 10 meters.

The standard method for outdoor localization is GPS, because it is globally available, relatively accurate and receivers are inexpensive. However, GPS does not work well indoors due to low signal strength.