Moving Horizon Estimation with Pre-Estimation (MHE-PE) for 3D space debris tracking during atmospheric re-entry

Suwantong, Rata; Bertrand, Sylvain; Dumur, Didier; Beauvois, Dominique

doi:10.1109/cdc.2014.7039390

Cited by 4 publications

(4 citation statements)

References 16 publications

(24 reference statements)

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“…Content may change prior to final publication. Regression based estimator introduced and use an auxiliary estimator to describe the dynamics of the state over the horizon [135], [136]. MHE-PE is found to be more effective compared to MHE [137] for crop start date estimation in tropical area [68].…”

Section: Tasks Study Devices Findingsmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Tasks Study Devices Findingsmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…Monte Carlo simulations of 100 trajectories of the debris during 20 s starting from an altitude close to 70 km have been performed assuming a spherical debris, with a varying β (t), as done in [3]. The minimum, the mean and the maximum of the altitude of the debris at each instant among all the simulated trajectories are shown in figure 1.…”

Section: A Simulation Of Real Trajectories and Measurementsmentioning

confidence: 99%

“…Its optimization parameters are generally the initial state at the begining of the horizon and the process noise sequence over the horizon. The MHE has been proven to be more robust against model errors, poor initialization and bad tuning compared to classical filters such as the Extended Kalman Filter, the Unscented Kalman Filter and the particle filters [1][2] [3]. For discrete time autonomous nonlinear systems with additive process and measurement noises, formulations of the MHE exist in the literature in both deterministic and stochastic frameworks.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis and robustness assessment of deterministic and stochastic nonlinear moving horizon estimators

Suwantong¹,

Bertrand

Dumur

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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This paper proposes a discussion on the classification of the formulations of nonlinear Moving Horizon Estimators (MHE) of the literature into two categories: deterministic and stochastic. The stability of the dynamics of the estimation error is discussed for the MHEs in both frameworks. This paper also provides full explicit formulation of the stability conditions for the MHE in the deterministic framework, which were not given in the literature. Furthermore, robustness of MHE in both frameworks with respect to model errors is investigated through a simulation example of space object tracking. Comparison with other more classical estimators such as EKF, UKF and particle filter is also achieved.

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“…The target tracking is widely used in various fields such as military like radar tracking [5,7,24], public safety including pedestrian tracking [6,35], medicine study [3,8], space science [2,21,27]. The aim of target tracking is to continuously detect the measurements from the targets, and further estimate the states of targets, the number of targets and their trajectories.…”

Section: Introductionmentioning

confidence: 99%

A linear multisensor PHD filters via the measurement product space

Liu¹,

Chen²,

Wen³

et al. 2017

J. Nonlinear Sci. Appl.

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The probability hypothesis density (PHD) is the first moment of RFS. Its integral over any region gives the expectation number of targets in that region. In the finite set statistics (FISST) framework, the PHD recursion, or PHD filter, approximate the multi-target Bayes recursion. This paper deals with the multisensor PHD filter under a linear correlation condition through multisensor product space and the measurement dimension extension (MDE) approach, which remains the similar appearance like the conventional PHD filters except the product space and some parameters in the filters. However, in the product space the dimension extended measurements may greatly increase the computational load. Therefore, we propose a fast algorithm for the linear multisensor PHD (LM-PHD) filters to increase the running speed and with cost of slightly sacrificing the tracking performance.

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Moving Horizon Estimation with Pre-Estimation (MHE-PE) for 3D space debris tracking during atmospheric re-entry

Cited by 4 publications

References 16 publications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Stability analysis and robustness assessment of deterministic and stochastic nonlinear moving horizon estimators

A linear multisensor PHD filters via the measurement product space

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