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Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond 被引量:8
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作者 Tian-cheng LIn jin-ya su +1 位作者 Wci LIU Juan M. CORCHADO 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第12期1913-1939,共27页
Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that... Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov-Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed 'Gaussian conjugacy' in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity. 展开更多
关键词 Kalman filter Gaussian filter Time series estimation Bayesian filtering Nonlinear filtering Constrained filtering Gaussian mixture MANEUVER Unknown inputs
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Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 被引量:8
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作者 Tian-Xiang Zhang jin-ya su +1 位作者 Cun-Jia Liu Wen-Hua Chen 《International Journal of Automation and computing》 EI CSCD 2019年第1期16-26,共11页
Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(N... Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture. 展开更多
关键词 Sentinel-2A REMOTE sensing image classification supervised learning PRECISION AGRICULTURE
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