Indoor dynamic environment lo calization algorithm based on transfer learning

The accuracy of the traditional indoor fingerprint lo calization system is limited bymany factors,such as the density of the reference location node in the fingerprint database and the characteristics of the indoor environment.When the indoor environment changes dynami cally,the RSS fluctuates,and usually does notmeet the assumption of the same distribution.Therefore,it was difficult to obtain high-precision requirements for conventional fingerprint positioningmethod.Aiming at the problem that the traditional algorithm couldn’t locate accurately,an algorithm based on the indoor fingerprint database was designed and implemented.The algorithm adopted the idea ofmigration learning to embed different data sets into the latent feature space,and the adverse effects of environmental changes on the system weremitigated.The simulation results show that the average positioning error of this algorithm is 1.23m.