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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...
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex...
In practice, the transition probability matrix (TPM) in the approach to track a maneuvering target is often unknown. We propose a new method to estimate the optimal TPM according to the maximum a posteriori (MAP) or maximum likelihood (ML) criterion via convex optimization. We apply the proposed method to the nonlinear/non- Gaussian cases, where the interacting multiple model (IMM) particle filter...
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