Robust tracking of moving sound source using scaled unscented particle filter

Tracking an active sound source involves the modeling of non-linear non-Gaussian systems. To address this problem, this paper proposed scaled unscented particle filter (SUPF) algorithm for tracking moving sound source. The particle filter part of the SUPF provides the general probabilistic framework to handle non-linear non-Gaussian systems, and the scaled unscented Kalman filter (SUKF) part of the SUPF generates better proposal distributions by taking into account the most recent observation. Meanwhile, models used in SUPF algorithm were also explored for the sound source motion, observation and the likelihood of the sound source location in the light of the Langevin process. Compared with the conventional PF approach, the simulated results demonstrated that the SUPF algorithm had superior tracking performance.     

Markov programming with policy constraints

In the theory and applications of Markov decision processes introduced by Howard and subsequently developed by many authors, it is assumed that actions can be chosen independently at each state. A policy constrained Markov decision process is one where selecting a given action in one state restricts the choice of actions in another. This note describes a method for determining a maximal gain policy in the policy constrained case. The method involves the use of bounds on the gain of the feasible policies to produce a policy ranking list. This list then forms a basis for a bounded enumeration procedure which yields the optimal policy.