Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances

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

IEEE SIGNAL PROCESSING LETTERS, cilt.22, sa.12, ss.2450-2454, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 12
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1109/lsp.2015.2490543
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2450-2454
  • Anahtar Kelimeler: Adaptive smoothing, Kalman filtering, noise covariance, Rauch-Tung-Striebel smoother, sensor calibration, time-varying noise covariances, variational Bayes, MAXIMUM-LIKELIHOOD, MODELS
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

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.