GALILEO, the major contribution of the European Union to the Global Navigation Satellite System (GNSS), will be both independent and complementary to the current GPS. It is still in its design phase, and while the signals have to be finalized, the main specifications have already been confirmed. The Binary Offset Carrier (BOC) modulation is part of the current GALILEO signal plan. A BOC modulation multiplies a spreading code with a square wave sub-carrier that has a frequency multiple of the code rate. It creates a split spectrum with two main lobes shifted from the center frequency by the frequency of the sub-carrier. This modulated signal induces better tracking in white noise and better inherent multipath mitigation compared to the spreading code alone. However, it also makes acquisition more challenging and tracking potentially ambiguous due to its multiple peak autocorrelation function. As a result, an evaluation of its performance under different conditions and research on advanced tracking techniques are necessary to assess its robustness and advantages before final selection. This paper focuses on a specific BOC signal: the BOC(1,1). Working on a given signal, instead of trying to find a generic solution, offers the possibility to fully exploit this signal's characteristics to find a more relevant way to improve its performance and cancel its bias threat. A new innovative tracking technique dedicated to BOC(n,n) signals has therefore been developed using a synthesized local correlation function. It completely removes the sidepeak threat and allows clean acquisition and tracking of any BOC(n,n) signal while keeping the same sharp correlation main peak. Consequently, it does not need to check if tracking is done on the main peak. It also offers a good resistance to long-delay multipath. The particular case of BOC(1,1) is taken as an example throughout the paper due to the strong probability that it will be used by the L1 GALILEO civil signal. Both acquisition and tracking are studied and compared with the standard tracking algorithm, first theoretically and then using simulations.