In this paper we present an alternative approach to spoofing mitigation. We leverage the fact that under most attack modes, both the authentic and spoofed signals are received by the victim. Once an attack is detected by conventional spoofing detection means, the receiver scans the vicinity of each satellite signal’s correlation peak for secondary peaks. In the meantime, the navigation continues in dead reckoning mode, based on other sensors and the user’s dynamics model. A decision about which signals to trust is then cast in the position domain. Once sufficient secondary peaks are detected, several navigation solutions are created from combinations of main and secondary peaks. We show approaches to drastically reduce the number of considered solutions at every epoch. Through recursive Bayesian estimation of the likelihood of each set of peaks we determine unlikely sets that can be ignored in future epochs. We further reduce the computational load by sharing computations among peak combinations. As an example, we apply the procedure to the TEXBAT dataset. We demonstrate a detection of secondary peaks even in the scenario where the spoofer has the largest power advantage and recover the spoofed and authentic navigation solutions in all cases. We finally discuss how to select the authentic solution among the two using the results in the position domain, leveraging measurements from auxiliary sensors such as an IMU.