Maximum likelihood estimation of transition probabilities using analytical center cutting plane method for unknown maneuvering emitter tracking by a wireless sensor network

We consider the problem of unknown maneuvering emitter tracking by a wireless sensor network using the interacting multiple models (IMM) with the TDOA and FDOA measurements. Essential to this tracking framework is the Markov transition probability matrix (TPM) governing the jumps between multiple dynamic motion models for the maneuvering target. In practice, the TPM is unknown and has to be estimated. In this paper, we consider the maximum likelihood (ML) estimation of the TPM and propose a recursive algorithm to update the ML TPM estimate using the analytical center cutting plane method (ACCPM). Compared to the general batch ML method, the resulting recursive ML estimation method has a much lower per sample complexity. Simulation results show the efficacy of the proposed method with improved tracking performance.