Particle Filtering

Djurić, Petar M.; Kotecha, J.H.; Zhang, J.; Huang, Yufei; Ghirmai, Tadesse; Bugallo, Mónica F.; Mı́guez, Joaquı́n

doi:10.1109/msp.2003.1236770

Cited by 842 publications

(387 citation statements)

References 42 publications

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“…Moreover, in very dry climates, the very low SM temporal variability precluded an improvement of the rainfall-runoff simulations when SM was assimilated. Matgen et al [19], in a tiny catchment in Luxembourg, used the Particle Filter [41] for assimilating both in-situ and satellite SM observations into the Bibmodel [42]. They found that, while the assimilation of in-situ data was successful, the use of ASCAT data did not lead to significant advantages and that the assimilation of SM is more efficient for flood forecasting during the transition periods (i.e., spring and fall seasons).…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

et al. 2015

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Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions. OPEN ACCESSRemote Sens. 2015, 7 11404

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Section: Introductionmentioning

confidence: 99%

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

et al. 2015

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“…Only in the case that a single target appears, with no false alarms, is there no association problem and optimal Bayesian filters can be applied, such as Kalman filters under ideal conditions, or suboptimal Bayesian filters like Multiple Models (IMM) (Yeddanapudi et al 1997) for realistic maneuvering situations and Particle Filters (PF) (Arulampalam et al 2002;Djuric et al 2003) in non-Gaussian conditions.…”

Section: Probabilistic Methodsmentioning

confidence: 99%

Boosting video tracking performance by means of Tabu Search in intelligent visual surveillance systems

et al. 2010

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In this paper, we present a fast and efficient technique for the data association problem applied to visual tracking systems. Visual tracking process is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc.We introduce a Tabu Search algorithm which performs a search on an indirect space. A novel problem formulation allows us to transform any solution into the real search space, which is needed for fitness calculation, in linear time. This new formulation and the use of auxiliary structures yields a fast transformation from a blob-to-track assignment space to the real shape and position of tracks space (while calculating fitness in an incremental fashion), which is key in order to produce efficient and fast results. Other previous approaches are based on statistical techniques or on evolutionary algorithms. These techniques are quite efficient and robust although they cannot converge as fast as our approach.

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“…The particle filtering algorithm (Djuric et al, 2003) is a sequential Monte Carlo method. The algorithm is powerful in approximating non-Gaussian probability distributions.…”

Section: Particle Filteringmentioning

confidence: 99%