A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers

Autor:	Petar M. Djuric, Cristina S. Maiz, Joaquín Míguez, Elisa M. Molanes-López
Rok vydání:	2012
Předmět:	Mathematical optimization Series (mathematics) Signal Processing Outlier Probability distribution Anomaly detection Probability density function Kalman filter Electrical and Electronic Engineering Particle filter Algorithm Quantile Mathematics
Zdroj:	IEEE Transactions on Signal Processing. 60:4611-4627
ISSN:	1941-0476 1053-587X
DOI:	10.1109/tsp.2012.2200480
Popis:	The literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data. Most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series. For a target tracking problem described by a nonlinear state-space model, we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm. The outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations, such as a concentration measure or an extreme upper quantile. We also provide asymptotic results about the convergence of the particle approximations of the predictive distribution (and its statistics) and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::92b76d95c6740616397705b7d3531496 https://doi.org/10.1109/tsp.2012.2200480 Zobrazit plný text záznamu