Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Time series: data analysis and theory
Time series: data analysis and theory
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A unified method for measurement and tracking of contacts from anarray of sensors
IEEE Transactions on Signal Processing
Recursive EM and SAGE-inspired algorithms with application to DOA estimation
IEEE Transactions on Signal Processing - Part I
A robust statistical-based speaker's location detection algorithm in a vehicular environment
EURASIP Journal on Applied Signal Processing
Speaker Tracking Using Recursive EM Algorithms
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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We deal with recursive direction-of-arrival (DOA) estimation of multiple moving sources. Based on the recursive EM algorithm, we develop two recursive procedures to estimate the time-varying DOA parameter for narrowband signals. The first procedure requires no prior knowledge about the source movement. The second procedure assumes that the motion of moving sources is described by a linear polynomial model. The proposed recursion updates the polynomial coefficients when a new data arrives. The suggested approaches have two major advantages: simple implementation and easy extension to wideband signals. Numerical experiments show that both procedures provide excellent results in a slowly changing environment. When the DOA parameter changes fast or two source directions cross with each other, the procedure designed for a linear polynomial model has a better performance than the general procedure. Compared to the beamforming technique based on the same parameterization, our approach is computationally favorable and has a wider range of applications.