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Journal articles on the topic 'Constrained state estimation'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Gomez-Quiles, Catalina, Hugo A. Gil, Antonio de la Villa Jaen, and Antonio Gomez-Exposito. "Equality-constrained bilinear state estimation." IEEE Transactions on Power Systems 28, no. 2 (2013): 902–10. http://dx.doi.org/10.1109/tpwrs.2012.2215058.

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2

Quintana, V. H., B. W. Scott, and A. Y. Chikhani. "Constrained Power System State Estimation." IFAC Proceedings Volumes 20, no. 5 (1987): 7–12. http://dx.doi.org/10.1016/s1474-6670(17)55409-9.

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3

Hu, Yudong, Changsheng Gao, and Wuxing Jing. "Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization." Aerospace 9, no. 4 (2022): 217. http://dx.doi.org/10.3390/aerospace9040217.

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Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear

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4

Nie, S., J. Zhu, and Y. Luo. "Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments." Hydrology and Earth System Sciences 15, no. 8 (2011): 2437–57. http://dx.doi.org/10.5194/hess-15-2437-2011.

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Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilat

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5

Liu, Yuanyuan, Yaqiong Fu, Huipin Lin, Jingbiao Liu, Mingyu Gao, and Zhiwei He. "A New Constrained State Estimation Method Based on Unscented H∞ Filtering." Applied Sciences 10, no. 23 (2020): 8484. http://dx.doi.org/10.3390/app10238484.

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The unscented Kalman filter (UKF) is widely used in many fields. When the unscented Kalman filter is combined with the H∞ filter (HF), the obtained unscented H∞ filtering (UHF) is very suitable for state estimation of nonlinear non-Gaussian systems. However, the application of state estimation is often limited by physical laws and mathematical models on some occasions. The standard unscented H∞ filtering always performs poorly under this situation. To solve this problem, this paper improves the UHF algorithm based on state constraints and studies the UHF algorithm based on the projection metho

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6

Mare, José B., and José A. De Doná. "Symmetry between constrained reference tracking and constrained state estimation." Automatica 45, no. 1 (2009): 207–11. http://dx.doi.org/10.1016/j.automatica.2008.06.020.

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7

Prakash, J., Sachin C. Patwardhan, and Sirish L. Shah. "Constrained State Estimation Using Particle Filters." IFAC Proceedings Volumes 41, no. 2 (2008): 6472–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01091.

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8

Dasgupta, Kalyan, and K. S. Swarup. "Tie-line constrained distributed state estimation." International Journal of Electrical Power & Energy Systems 33, no. 3 (2011): 569–76. http://dx.doi.org/10.1016/j.ijepes.2010.12.010.

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9

Wang, Yanyan, and Yingsong Li. "Sparse Multipath Channel Estimation Using Norm Combination Constrained Set-Membership NLMS Algorithms." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8140702.

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A norm combination penalized set-membership NLMS algorithm with l0 and l1 independently constrained, which is denoted as l0 and l1 independently constrained set-membership (SM) NLMS (L0L1SM-NLMS) algorithm, is presented for sparse adaptive multipath channel estimations. The L0L1SM-NLMS algorithm with fast convergence and small estimation error is implemented by independently exerting penalties on the channel coefficients via controlling the large group and small group channel coefficients which are implemented by l0 and l1 norm constraints, respectively. Additionally, a further improved L0L1SM

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10

Korres, George N., and Theodore A. Alexopoulos. "A Constrained Ordering for Solving the Equality Constrained State Estimation." IEEE Transactions on Power Systems 27, no. 4 (2012): 1998–2005. http://dx.doi.org/10.1109/tpwrs.2012.2194745.

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11

Eras-Herrera, Wendy Y., Alexandre R. Mesquita, and Bruno O. S. Teixeira. "Equality-constrained state estimation for hybrid systems." IET Control Theory & Applications 13, no. 13 (2019): 2018–28. http://dx.doi.org/10.1049/iet-cta.2018.6374.

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12

Zhou, Gongjian, Keyi Li, and Thiagalingam Kirubarajan. "Constrained state estimation using noisy destination information." Signal Processing 166 (January 2020): 107226. http://dx.doi.org/10.1016/j.sigpro.2019.07.019.

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13

de la Villa Jaen, A., and Exposito, A. Gomez. "Implicitly Constrained Substation Model for State Estimation." IEEE Power Engineering Review 22, no. 6 (2002): 57. http://dx.doi.org/10.1109/mper.2002.4312285.

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14

Valverde, Gustavo, Saikat Chakrabarti, Elias Kyriakides, and Vladimir Terzija. "A Constrained Formulation for Hybrid State Estimation." IEEE Transactions on Power Systems 26, no. 3 (2011): 1102–9. http://dx.doi.org/10.1109/tpwrs.2010.2079960.

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15

Singh, H., F. L. Alvarado, and W. H. E. Liu. "Constrained LAV state estimation using penalty functions." IEEE Transactions on Power Systems 12, no. 1 (1997): 383–88. http://dx.doi.org/10.1109/59.575725.

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16

de la Villa Jaen, A., and A. Gomez-Exposito. "Implicitly constrained substation model for state estimation." IEEE Transactions on Power Systems 17, no. 3 (2002): 850–56. http://dx.doi.org/10.1109/tpwrs.2002.800979.

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17

Brembeck, Jonathan. "A Physical Model-Based Observer Framework for Nonlinear Constrained State Estimation Applied to Battery State Estimation." Sensors 19, no. 20 (2019): 4402. http://dx.doi.org/10.3390/s19204402.

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Future electrified autonomous vehicles demand higly accurate knowledge of their system states to guarantee a high-fidelity and reliable control. This constitutes a challenging task—firstly, due to rising complexity and operational safeness, and secondly, due to the need for embedded service oriented architecture which demands a continuous development of new functionalities. Based on this, a novel model based Kalman filter framework is outlined in this publication, which enables the automatic incorporation of multiphysical Modelica models into discrete-time estimation algorithms. Additionally,

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18

Zhu, Bingjie, Yingting Luo, and Yunmin Zhu. "New Viewpoints about Pseudo Measurements Method in Equality-Constrained State Estimation." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/946952.

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We discuss the pseudo measurement method which is one of the main approaches to equality-constrained state estimation for a dynamic system. We demonstrate by the fundamental theory of Kalman filtering that reviewing the equality constraint as a pseudo measurement seems questionable. The main reason is that the additional pseudo measurement is actually a constant here which cannot help to estimate the state. More specifically, when the states in an unconstrained dynamic system model have already satisfied the equality constraint, the extra constraint is obviously not necessary. When the true eq

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19

Li, Kuan, and Xiaoquan Xu. "A Resource Scheduling Strategy for WSN with Communication Constrained." MATEC Web of Conferences 175 (2018): 03032. http://dx.doi.org/10.1051/matecconf/20181750100103032.

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Due to the loss of wireless communication link, the remote estimator in the wireless sensor network can only receive part of the observation information or can not be completely received, which reduces the accuracy of the state estimation. Focusing on the above problems, a strategy based on network resource scheduling is proposed to improve the impact of link loss on state estimation. The strategy considers the quantization process of sensor observations and the limited transmission bandwidth. The objective of optimization is to minimize the estimated error covariance and the expected energy c

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20

Fan, Jianming, and Binqiang Xue. "Moving Horizon Estimation for Uncertain Networked Control Systems with Packet Loss." Mathematical Problems in Engineering 2020 (July 4, 2020): 1–10. http://dx.doi.org/10.1155/2020/9875891.

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This paper studies a moving horizon estimation approach to solve the constrained state estimation problem for uncertain networked systems with random packet loss. The system model error range is known, and the packet loss phenomena are modeled by a binary switching random sequence. Taking the model error, the packet loss, the system constraints, and the network transmission noise into account, a time-varying weight matrix is obtained by solving a least-square problem. Then, a robust moving horizon estimator is designed to estimate the system state by minimizing an optimization problem with an

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21

Sun, Baoyan, Jun Hu, and Yan Gao. "Variance-constrained robust $ H_{\infty} $ state estimation for discrete time-varying uncertain neural networks with uniform quantization." AIMS Mathematics 7, no. 8 (2022): 14227–48. http://dx.doi.org/10.3934/math.2022784.

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<abstract><p>In this paper, we consider the robust $ H_{\infty} $ state estimation (SE) problem for a class of discrete time-varying uncertain neural networks (DTVUNNs) with uniform quantization and time-delay under variance constraints. In order to reflect the actual situation for the dynamic system, the constant time-delay is considered. In addition, the measurement output is first quantized by a uniform quantizer and then transmitted through a communication channel. The main purpose is to design a time-varying finite-horizon state estimator such that, for both the uniform quanti

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22

Wang, Baofeng, Ge Guo, and Xiue Gao. "Variance-Constrained Robust Estimation for Discrete-Time Systems with Communication Constraints." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/980753.

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This paper is concerned with a new filtering problem in networked control systems (NCSs) subject to limited communication capacity, which includes measurement quantization, random transmission delay, and packets loss. The measurements are first quantized via a logarithmic quantizer and then transmitted through a digital communication network with random delay and packet loss. The three communication constraints phenomena which can be seen as a class of uncertainties are formulated by a stochastic parameter uncertainty system. The purpose of the paper is to design a linear filter such that, for

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23

Ungarala, Sridhar, Eric Dolence, and Keyu Li. "CONSTRAINED EXTENDED KALMAN FILTER FOR NONLINEAR STATE ESTIMATION." IFAC Proceedings Volumes 40, no. 5 (2007): 63–68. http://dx.doi.org/10.3182/20070606-3-mx-2915.00058.

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24

Korres, G. N. "A Robust Method for Equality Constrained State Estimation." IEEE Power Engineering Review 21, no. 12 (2001): 69. http://dx.doi.org/10.1109/mper.2001.4311235.

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25

Xu, Linfeng, X. Rong Li, Yan Liang, and Zhansheng Duan. "Constrained Dynamic Systems: Generalized Modeling and State Estimation." IEEE Transactions on Aerospace and Electronic Systems 53, no. 5 (2017): 2594–609. http://dx.doi.org/10.1109/taes.2017.2705518.

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26

Geisler, K. I., and T. G. Nowak. "Boundary constrained external network power system state estimation." Proceedings of the IEEE 75, no. 8 (1987): 1130–32. http://dx.doi.org/10.1109/proc.1987.13859.

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27

Ungarala, Sridhar, Keyu Li, and Zhongzhou Chen. "Constrained Bayesian State Estimation Using a Cell Filter." Industrial & Engineering Chemistry Research 47, no. 19 (2008): 7312–22. http://dx.doi.org/10.1021/ie070249q.

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28

Rao, Christopher V., James B. Rawlings, and Jay H. Lee. "Constrained linear state estimation—a moving horizon approach." Automatica 37, no. 10 (2001): 1619–28. http://dx.doi.org/10.1016/s0005-1098(01)00115-7.

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29

Prakash, J., Sachin C. Patwardhan, and Sirish L. Shah. "Constrained Nonlinear State Estimation Using Ensemble Kalman Filters." Industrial & Engineering Chemistry Research 49, no. 5 (2010): 2242–53. http://dx.doi.org/10.1021/ie900197s.

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30

Korres, G. N. "A robust method for equality constrained state estimation." IEEE Transactions on Power Systems 17, no. 2 (2002): 305–14. http://dx.doi.org/10.1109/tpwrs.2002.1007897.

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31

Manam, R., and S. R. Rayapudi. "Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2240–50. https://doi.org/10.5281/zenodo.1118348.

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In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs) in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB) are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of sin

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32

Li, Ming, Xiafei Tang, Qichun Zhang, and Yiqun Zou. "Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints." Applied Sciences 12, no. 19 (2022): 9975. http://dx.doi.org/10.3390/app12199975.

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For the bearing-only target motion analysis (TMA), the pseudolinear Kalman filter (PLKF) solves the complex nonlinear estimation of the motion model parameters but suffers serious bias problems. The pseudolinear Kalman filter under the minimum mean square error framework (PL-MMSE) has a more accurate tracking ability and higher stability compared to the PLKF. Since the bearing signals are corrupted by non-Gaussian noise in practice, we reconstruct the PL-MMSE under Gaussian mixture noise. If some prior information, such as state constraints, is available, the performance of the PL-MMSE can be

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33

Wang, Yanbo, Fasheng Wang, Jianjun He, and Fuming Sun. "Iterative Truncated Unscented Particle Filter." Information 11, no. 4 (2020): 214. http://dx.doi.org/10.3390/info11040214.

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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 metho

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34

Zeng, Xiangrong, Maojun Zhang, Zhiwei Zhong, and Yan Liu. "Energy-Constrained Deep Neural Network Compression for Depth Estimation." Electronics 12, no. 3 (2023): 732. http://dx.doi.org/10.3390/electronics12030732.

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Many applications, such as autonomous driving, robotics, etc., require accurately estimating depth in real time. Currently, deep learning is the most popular approach to stereo depth estimation. Some of these models have to operate in highly energy-constrained environments, while they are usually computationally intensive, containing massive parameter sets ranging from thousands to millions. This makes them hard to perform on low-power devices with limited storage in practice. To overcome this shortcoming, we model the training process of a deep neural network (DNN) for depth estimation under

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35

Wang, Shoudong, and Binqiang Xue. "Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems." Mathematics 11, no. 6 (2023): 1507. http://dx.doi.org/10.3390/math11061507.

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This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adj

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36

Xiong, Weili, Mingchen Xue, and Baoguo Xu. "Constrained Dynamic Systems Estimation Based on Adaptive Particle Filter." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/589347.

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For the state estimation problem, Bayesian approach provides the most general formulation. However, most existing Bayesian estimators for dynamic systems do not take constraints into account, or rely on specific approximations. Such approximations and ignorance of constraints may reduce the accuracy of estimation. In this paper, a new methodology for the states estimation of constrained systems with nonlinear model and non-Gaussian uncertainty which are commonly encountered in practice is proposed in the framework of particles filter. The main feature of this method is that constrained problem

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37

Brembeck, Jonathan. "Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation." Sensors 19, no. 10 (2019): 2276. http://dx.doi.org/10.3390/s19102276.

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The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic

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38

Manam, R., and S. R. Rayapudi. "Sensitive Constrained Optimal PMU Allocation with Complete Observability for State Estimation Solution." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2240–50. http://dx.doi.org/10.48084/etasr.1542.

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In this paper, a sensitive constrained integer linear programming approach is formulated for the optimal allocation of Phasor Measurement Units (PMUs) in a power system network to obtain state estimation. In this approach, sensitive buses along with zero injection buses (ZIB) are considered for optimal allocation of PMUs in the network to generate state estimation solutions. Sensitive buses are evolved from the mean of bus voltages subjected to increase of load consistently up to 50%. Sensitive buses are ranked in order to place PMUs. Sensitive constrained optimal PMU allocation in case of sin

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39

Wang, Min, and Huabo Liu. "Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks." Sensors 23, no. 14 (2023): 6553. http://dx.doi.org/10.3390/s23146553.

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In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel i

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40

Surender, Reddy Salkuti. "Power system state estimation using teaching learning-based optimization algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2125–31. https://doi.org/10.12928/TELKOMNIKA.v18i4.14159.

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The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional op

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41

Bhaskara, Ramchander Rao, Manoranjan Majji, and Felipe Guzmán. "Quantized State Estimation for Linear Dynamical Systems." Sensors 24, no. 19 (2024): 6381. http://dx.doi.org/10.3390/s24196381.

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This paper investigates state estimation methods for dynamical systems when model evaluations are performed on resource-constrained embedded systems with finite precision compute elements. Minimum mean square estimation algorithms are reformulated to incorporate finite-precision numerical errors in states, inputs, and measurements. Quantized versions of least squares batch estimation, sequential Kalman, and square-root filtering algorithms are proposed for fixed-point implementations. Numerical simulations are used to demonstrate performance improvements over standard filter formulations. Stea

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42

Ludwig, Markus. "Robust Estimation of Shape-Constrained State Price Density Surfaces." Journal of Derivatives 22, no. 3 (2015): 56–72. http://dx.doi.org/10.3905/jod.2015.22.3.056.

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43

Wen, Chuanbo, and Yunze Cai. "State Estimation for Linear Constrained Systems with Unknown Input." Chinese Journal of Electronics 23, no. 3 (2014): 539–44. https://doi.org/10.23919/cje.2014.10851202.

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44

Risso, Mariano A., Neila Bhouri, Aldo J. Rubiales, and Pablo A. Lotito. "A constrained filtering algorithm for freeway traffic state estimation." Transportmetrica A: Transport Science 16, no. 2 (2018): 316–36. http://dx.doi.org/10.1080/23249935.2018.1549618.

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45

Kadu, Sachin C., Mani Bhushan, and Kallol Roy. "Optimization-Based Sigma Points Selection for Constrained State Estimation." Industrial & Engineering Chemistry Research 52, no. 5 (2013): 1916–26. http://dx.doi.org/10.1021/ie2027487.

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46

Simon, D. "A game theory approach to constrained minimax state estimation." IEEE Transactions on Signal Processing 54, no. 2 (2006): 405–12. http://dx.doi.org/10.1109/tsp.2005.861732.

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47

Abrol, Sidharth, and Thomas F. Edgar. "A fast and versatile technique for constrained state estimation." Journal of Process Control 21, no. 3 (2011): 343–50. http://dx.doi.org/10.1016/j.jprocont.2010.05.007.

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48

Liu, Yuanyuan, Jingbiao Liu, and Zhiwei He. "Constrained State Estimation via Projection based Optimized Parameters UKF." International Journal of Hybrid Information Technology 9, no. 11 (2016): 275–84. http://dx.doi.org/10.14257/ijhit.2016.9.11.24.

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49

Luo, Zhen, Huajing Fang, and Yuanhao Luo. "Constrained State Estimation for Nonlinear Systems with Unknown Input." Circuits, Systems, and Signal Processing 32, no. 5 (2013): 2199–211. http://dx.doi.org/10.1007/s00034-013-9559-6.

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50

Li, Wenling, Yingmin Jia, and Junping Du. "Variance-Constrained State Estimation for Nonlinearly Coupled Complex Networks." IEEE Transactions on Cybernetics 48, no. 2 (2018): 818–24. http://dx.doi.org/10.1109/tcyb.2017.2653242.

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