Particle Filtering With Soft State Constraints for Target Tracking

Liu, Cunjia; Li, Baibing; Chen, Wenhua

doi:10.1109/taes.2019.2908292

Cited by 22 publications

(14 citation statements)

References 38 publications

(57 reference statements)

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“…For instance, many unknown or uncertain parameters in the AquaCrop model have physical meanings and therefore are with upper and lower bounds. This parameter bound information, if taken into account properly, can further improve the estimation performance of particle filter (Liu et al, 2019;López-Negrete et al, 2011;Amor et al, 2016). It is also discovered in this study that the sensitivity of various parameters in crop models may vary significantly in different crop growth stages, that is, a parameter being sensitive in stage A may become insensitive in stage B and vice versa (Xing et al, 2017).…”

Section: Introductionmentioning

confidence: 51%

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State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Zhang

Liu

et al. 2021

Computers and Electronics in Agriculture

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Crop models play a paramount role in providing quantitative information on crop growth and field management. However, its prediction performance degrades significantly in the presence of unknown, uncertain parameters and noisy measurements. Consequently, simultaneous state and parameter estimation (SSPE) for crop model is required to maximize its potentials. This work aims to develop an integrated dynamic SSPE framework for the AquaCrop model by leveraging constrained particle filter, crop sensitivity analysis and UAV remote sensing. Both Monte Carlo simulation and one winter wheat experimental case study are performed to validate the proposed framework. It is shown that: (i) the proposed framework with state/parameter bound and parameter sensitivity information outperforms conventional particle filter and constrained particle filter in both state and parameter estimation in Monte Carlo simulations; (ii) in real-world experiment, the proposed approach achieves the smallest root mean squared error for canopy cover estimation among the three algorithms by using day forwardchaining validation method.

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Section: Introductionmentioning

confidence: 51%

“…It follows from Liu et al (2019) that the posterior distribution of X 0:k+1 with constraints information D 1:k+1 can be derived according to the Bayesian recursion once measurement Y 1:k+1 is available, given by…”

Section: Improved Particle Filtermentioning

confidence: 99%

State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

Zhang

Liu

et al. 2021

Computers and Electronics in Agriculture

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“…Substituting from (23) into (24) while using model properties (4), then after simple mathematical manipulations one gets the covariance matrices of the prediction error as given by (8) for i ∈ {1, 2, . .…”

Section: A: the Prediction Stepmentioning

confidence: 99%

“…Its main idea was to fuse the constraints and the auxiliary dynamics to achieve a constrained dynamical model on which the linear minimum mean square error estimator of the LEC system was applied [22]. For inequality-constrained nonlinear systems, algorithms based on the particle filter [23], the EnKF [24], and the interior point method [25] were developed. Other examples of constrained state estimators can be found in [26]- [29].…”

Section: Introduction a Literature Reviewmentioning

confidence: 99%

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

Hassan

2020

IEEE Access

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In this paper, a decomposed state estimator is developed for stochastic constrained nonlinear discrete-time dynamical systems with uncertain parameters. The proposed estimator can deal with general nonlinear uncertain stochastic systems without any pre-defined specifications on the system structure and/or the measurement model. Moreover, it can handle estimation problems for nonlinear stochastic systems subject to a set of imposed linear and/or nonlinear equality and/or inequality constraints. The mathematical structure of the proposed estimator is developed using multiple projection approach and its performance is analyzed and compared with the global (un-decomposed) structure. The main features of the proposed estimator are: it reduces the processing time, it can handle estimation problems with bad initial conditions of the estimator, it can be implemented on modern parallel processing facilities to further reduce the processing time, and last but not least, it minimizes the rounding off errors and hence improves the numerical stability of the algorithm. Simulations results on different real applications are presented to show the effectiveness of the developed approach, and its improved performance when compared with other techniques reported in the literature. INDEX TERMS Constrained stochastic uncertain nonlinear systems, decomposed state estimation, extended Kalman filter, large scale systems, performance analysis.

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“…The particle filter (PF), or sequential Monte Carlo method, is an efficient solution to nonlinear filtering problems which has been applied in various fields including virtual reality [4], target tracking [5], robotics [6], econometrics [7], computer vision [8,9], etc. The basic idea of the PF is to calculate the posterior PDF using a finite set of particles and corresponding weights.…”

Section: Introductionmentioning

confidence: 99%

Iterative Truncated Unscented Particle Filter

Wang

et al. 2020

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The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

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Particle Filtering With Soft State Constraints for Target Tracking

Cited by 22 publications

References 38 publications

State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

A Generalized Decomposed State Estimation Approach for Uncertain Constrained Stochastic Nonlinear Systems

Iterative Truncated Unscented Particle Filter

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