MHCTrajectorySampler provides a set of likely trajectories of an object (called RFID tag) detected by a set of RFID readers located within a map.

Trajectories are generated with a Markov Chain Monte Carlo algorithm: a Metropolis Hastings modified in order to satisfy a set of constraints implied by the particular map topology. This specialized algorithm has been called Metropolis Hastings with Constraints [MHC].

Program input consists of:

• A map file, represented as a set of rectangular areas called locations, in which are located the RFID readers.
• A set of constraints relative to the specified map. There are three supported types of constraints:
  1. Direct Unreachability [DU] (or Reachability [DR]) between two locations L1, L2 (specifies whether L2 is unreachable [or reachable] from L1 in a single time unit);
  2. Traveling Time [TT] between two locations L1, L2 (minimum number of time units needed to travel from L1 to L2);
  3. Latency [LT] of a location L (minimum number of time units needed to consider L as 'visited').
• A set of readings. Each reading is a pair <R,t>, where R is the set of RFID readers which have detected the tag at instant t (R can be an empty set).

MHCTrajectorySampler returns a set of likely trajectories of the RFID tag, useful to estimate the P(L|R) object, which represents the probability that the tag was in L while it was detected by the set R of readers.

To build project simply run make in main project directory (currently will be built only a simple unit test).

Project has been tested on Linux with GNU g++ compiler: Linux 3.12.13-gentoo SMP x86_64 gcc version 4.7.3 (Gentoo 4.7.3-r1 p1.4, pie-0.5.5)