Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances


Creative Commons License

Ardeshiri T., Ozkan E. , ORGUNER U. , Gustafsson F.

IEEE SIGNAL PROCESSING LETTERS, cilt.22, ss.2450-2454, 2015 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 22 Konu: 12
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/lsp.2015.2490543
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Sayfa Sayıları: ss.2450-2454

Özet

We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.