Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances

Download
2015-12-01
Ardeshiri, Tohid
Özkan, Emre
Orguner, Umut
Gustafsson, Fredrik
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.
IEEE SIGNAL PROCESSING LETTERS

Suggestions

Noise Estimation for Hyperspectral Imagery using Spectral Unmixing and Synthesis
DEMİRKESEN, CAN; Leloğlu, Uğur Murat (2014-09-25)
Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized wit...
Real-Time Detection of Interharmonics and Harmonics of AC Electric Arc Furnaces on GPU Framework
Uz-Logoglu, Eda; Salor, Ozgul; Ermiş, Muammer (2019-11-01)
In this paper, a method based on the multiple synchronous reference frame analysis is recommended and implemented to detect time-varying harmonics and interharmonics of rapidly fluctuating asymmetrical industrial loads. The experimental work has been carried out on a typical three-phase alternating current arc furnace installation. In the recommended method, the reference frame is rotated in both directions at speeds corresponding to the positive and negative sequences of all harmonics and all interharmonic...
Noise reduction using anisotropic diffusion filter in inverse electrocardiology.
Gavgani, Alireza Mazloumi; Serinağaoğlu Doğrusöz, Yeşim (2012-01-01)
Filtering has been widely used in biomedical signal processing and image processing applications to cancel noise effects in signals recorded from the body. However, it is important to keep the desired characteristics of the physiological signal of interest while suppressing the noise characteristics. In this study, we used anisotropic diffusion filter (ADF) to cancel the noise on the body surface potentials measurements (BSPM) with the goal of improving the corresponding solutions of the inverse problem of ...
Comparison of PWM and PFM induction drives ID in,egarding audible noise and vibration for household applications
Ertan, Hulusi Bülent (2004-11-01)
This paper is aimed at comparing the performance of pulse frequency modulation (PFM) and pulsewidth modulation (PWM) techniques regarding audible noise generated from inverter-driven induction motors. For the purpose of illustrating the performance of the two modulation techniques, a drive developed for washing machine applications is considered. First, the measured and simulated harmonic content of this inverter is compared with the measured harmonic spectrum of a three-phase input-output commercial variab...
Real-Time Detection of Interharmonics and Harmonics of AC Electric Arc Furnaces on GPU Framework
Uz-Logoglu, Eda; Salor, Ozgul; Ermiş, Muammer (2017-10-05)
In this paper, a method based on the multiple synchronous reference frame (MSRF) analysis is recommended and implemented to detect time-varying harmonics and interharmonics of rapidly fluctuating asymmetrical industrial loads. The experimental work has been carried out on a typical three-phase alternating current arc furnace (AC EAF) installation. In the recommended method, the reference frame is rotated in both directions at speeds corresponding to the positive and negative sequences of all harmonics and a...
Citation Formats
T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, “Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances,” IEEE SIGNAL PROCESSING LETTERS, pp. 2450–2454, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43860.