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Journal articles on the topic 'Constrained state estimation'

Author: Grafiati

Published: 25 May 2024

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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 (May 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 (July 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 (April 14, 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 constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly.

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4

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

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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 (November 27, 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 method. The standard UHF sigma points that violate the state constraints are projected onto the constraint boundary. Firstly, the paper gives a broad overview of H∞ filtering and unscented H∞ filtering, then addresses the issue of how to add constraints using the UHF approach, and finally, the new method is tested and evaluated by the gas-phase reversible reaction and the State of Charge (SOC) estimation examples. Simulation results show the validity and feasibility of the state-constrained UHF algorithm.

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6

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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7

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

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8

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 (August 3, 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 assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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9

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 (November 2012): 1998–2005. http://dx.doi.org/10.1109/tpwrs.2012.2194745.

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10

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-NLMS algorithm denoted as reweighted L0L1SM-NLMS (RL0L1SM-NLMS) algorithm is presented via integrating a reweighting factor into our L0L1SM-NLMS algorithm to properly adjust the zero-attracting capabilities. Our developed RL0L1SM-NLMS algorithm provides a better estimation behavior than the presented L0L1SM-NLMS algorithm for implementing an estimation on sparse channels. The estimation performance of the L0L1SM-NLMS and RL0L1SM-NLMS algorithms is obtained for estimating sparse channels. The achieved simulation results show that our L0L1SM- and RL0L1SM-NLMS algorithms are superior to the traditional LMS, NLMS, SM-NLMS, ZA-LMS, RZA-LMS, and ZA-, RZA-, ZASM-, and RZASM-NLMS algorithms in terms of the convergence speed and steady-state performance.

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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 (September 3, 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 (June 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 (August 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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de la Villa Jaen, A., and A. Gomez-Exposito. "Implicitly constrained substation model for state estimation." IEEE Transactions on Power Systems 17, no. 3 (August 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 (October 11, 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, these estimation algorithms are extended with nonlinear inequality constraint handling functionalities. The proposed framework is applied to a constrained nonlinear state of charge lithium-ion cell observer and is validated with experimental data.

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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 equality-constrained states do not satisfy the unconstrained dynamic process equation, the effect of pseudo measurement is projecting the estimate which is not optimal onto the constraint set. However, since the performance of a projected estimate is also certainly influenced by its original estimate, we show through a numerical example that the pseudo measurement method is not always a good choice, especially when the process equation mismatch is large.

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19

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 arrival cost function. The proposed estimator ensures that the optimal estimated state can be obtained in the worst case. Furthermore, the asymptotic convergence of the estimator is analyzed and some sufficient conditions for convergence are given. Finally, the validity of the proposed approach can be demonstrated by numerical simulations.

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20

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 quantization and time-delay, some sufficient criteria are obtained for the estimation error (EE) system to satisfy the error variance boundedness and the $ H_{\infty} $ performance constraint. With the help of stochastic analysis technique, a new $ H_{\infty} $ SE algorithm without resorting the augmentation method is proposed for DTVUNNs with uniform quantization. Finally, a simulation example is given to illustrate the feasibility and validity of the proposed variance-constrained robust $ H_{\infty} $ SE method.</p></abstract>

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21

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 consumption of the data packet. The data rate and the time slot are allocated to each communication link. The simulation results show that the optimal state estimation of the physical process can be obtained under a small transmission bandwidth and simple BPSK modulation, and the energy consumption of the transmitted data packet can be effectively reduced.

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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 all the communication constraints, the error state of the filtering process is mean square bounded and the steady-state variance of the estimation error for each state is not more than the individual prescribed upper bound. It is shown that the desired filtering can effectively be solved if there are positive definite solutions to a couple of algebraic Riccati-like inequalities or linear matrix inequalities. Finally, an illustrative numerical example is presented to demonstrate the effectiveness and flexibility of the proposed design approach.

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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 (December 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 (October 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 (October 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 (October 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 (March 3, 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 (May 2002): 305–14. http://dx.doi.org/10.1109/tpwrs.2002.1007897.

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31

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 (October 4, 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 further improved by incorporating state constraints in the filtering process. In this paper, the mean square and estimation projection methods are used to incorporate PL-MMSE with linear constraints, respectively. Then, the linear approximation and second-order approximation methods are applied to merge PL-MMSE with nonlinear constraints, respectively. Simulation results demonstrate that the constrained PL-MMSE algorithms result in lower mean square errors and bias norms, which demonstrates the superiority of the constrained algorithms.

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32

Zeng, Xiangrong, Maojun Zhang, Zhiwei Zhong, and Yan Liu. "Energy-Constrained Deep Neural Network Compression for Depth Estimation." Electronics 12, no. 3 (February 1, 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 a given energy constraint as a constrained optimization problem and solve it through a proposed projected adaptive cubic quasi-Newton method (termed ProjACQN). Moreover, the trained model is also deployed on a GPU and an embedded device to evaluate its performance. Experiments show that the stage four results of ProjACQN on the KITTI-2012 and KITTI-2015 datasets under a 70% energy budget achieve (1) 0.13% and 0.61%, respectively, lower three-pixel error than the state-of-the-art ProjAdam when put on a single RTX 3090Ti; (2) 4.82% and 7.58%, respectively, lower three-pixel error than the pruning method Lottery-Ticket; (3) 5.80% and 0.12%, respectively, lower three-pixel error than ProjAdam on the embedded device Nvidia Jetson AGX Xavier. These results show that our method can reduce the energy consumption of depth estimation DNNs while maintaining their accuracy.

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33

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 problems are handled well by a sample size test and two particles handling strategies. Simulation results show that the proposed method can outperform particles filter and other two existing algorithms in terms of accuracy and computational time.

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34

Brembeck, Jonathan. "Nonlinear Constrained Moving Horizon Estimation Applied to Vehicle Position Estimation." Sensors 19, no. 10 (May 16, 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 event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.

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35

Wang, Yanbo, Fasheng Wang, Jianjun He, and Fuming Sun. "Iterative Truncated Unscented Particle Filter." Information 11, no. 4 (April 16, 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 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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36

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 (December 18, 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 single line and no line contingency are considered in observability analysis to ensure protection and control of power system from abnormal conditions. Modeling of ZIB constraints is included to minimize the number of PMU network allocations. This paper presents optimal allocation of PMU at sensitive buses with zero injection modeling, considering cost criteria and redundancy to increase the accuracy of state estimation solution without losing observability of the whole system. Simulations are carried out on IEEE 14, 30 and 57 bus systems and results obtained are compared with traditional and other state estimation methods available in the literature, to demonstrate the effectiveness of the proposed method.

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37

Wang, Shoudong, and Binqiang Xue. "Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems." Mathematics 11, no. 6 (March 20, 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 adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.

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38

Wang, Min, and Huabo Liu. "Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks." Sensors 23, no. 14 (July 20, 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 is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.

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39

Ludwig, Markus. "Robust Estimation of Shape-Constrained State Price Density Surfaces." Journal of Derivatives 22, no. 3 (February 28, 2015): 56–72. http://dx.doi.org/10.3905/jod.2015.22.3.056.

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40

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 (November 27, 2018): 316–36. http://dx.doi.org/10.1080/23249935.2018.1549618.

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41

Kadu, Sachin C., Mani Bhushan, and Kallol Roy. "Optimization-Based Sigma Points Selection for Constrained State Estimation." Industrial & Engineering Chemistry Research 52, no. 5 (January 23, 2013): 1916–26. http://dx.doi.org/10.1021/ie2027487.

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

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

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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 (November 30, 2016): 275–84. http://dx.doi.org/10.14257/ijhit.2016.9.11.24.

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Luo, Zhen, Huajing Fang, and Yuanhao Luo. "Constrained State Estimation for Nonlinear Systems with Unknown Input." Circuits, Systems, and Signal Processing 32, no. 5 (February 12, 2013): 2199–211. http://dx.doi.org/10.1007/s00034-013-9559-6.

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

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Prakash, J., Biao Huang, and Sirish L. Shah. "Recursive constrained state estimation using modified extended Kalman filter." Computers & Chemical Engineering 65 (June 2014): 9–17. http://dx.doi.org/10.1016/j.compchemeng.2014.02.013.

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Kolås, S., B. A. Foss, and T. S. Schei. "Constrained nonlinear state estimation based on the UKF approach." Computers & Chemical Engineering 33, no. 8 (August 2009): 1386–401. http://dx.doi.org/10.1016/j.compchemeng.2009.01.012.

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Das, Debashreet, C. R. Tripathy, P. K. Tripathy, and M. R. Kabat. "System reliability estimation of constrained multi-state computational grids." International Journal of Information Technology 12, no. 4 (April 18, 2018): 1419–25. http://dx.doi.org/10.1007/s41870-018-0132-1.

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Yang, Yandi, and Naser El-Sheimy. "M-GCLO: Multiple Ground Constrained LiDAR Odometry." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 283–88. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-283-2024.

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Abstract:

Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.

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