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Map matching means determining the location of a mobile with respect to a road network description stored in a digital map. This problem is usually addressed using Global Positioning System (GPS)-like fixes. Unfortunately, there are many situations in urban areas where few satellites are visible because of outages due to tall buildings. In this paper, map matching is solved using raw GPS measurements (pseudoranges and Doppler measurements), avoiding the necessity to compute a global position. The problem is formalized in a general Bayesian framework to handle noise, which can perform multihypothesis map matching when there is not enough information to make unambiguous decisions. This tightly coupled GPS-map fusion has to simultaneously cope with identifying the road and estimating the mobile's position on that road. A marginalized particle filter is proposed to efficiently solve this hybrid estimation problem. Real experimental results are reported to show that this approach can be initialized with fewer than four satellites. It can also track the location with only two satellites once the road selection has been solved.