In this paper we address the problem of distributed estimation of spatial fields using mobile sensor networks with communication constraints. These constraints consist of a maximum communication bandwidth which limits...
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In this paper we address the problem of distributed estimation of spatial fields using mobile sensor networks with communication constraints. These constraints consist of a maximum communication bandwidth which limits the amount of data that can be exchanged between any two nodes of the network at each time instant. An algorithm to select the most significant data to be transferred between neighboring sensor nodes is developed starting from derived analytical error bounds. Moreover, the motion of the network nodes is controlled using a coverage control algorithm with the objective of minimizing the estimation uncertainty of each of the nodes. The resulting communication constrained distributed estimation algorithm is deployed on a team of ground mobile robots in the Robotarium, and its performance is evaluated both in terms of estimation accuracy of a simulated spatial field, and of the amount of data transferred. Copyright (C) 2020 The Authors.
Moving Horizon estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one ...
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Moving Horizon estimation (MHE) is an important optimization-based approach for state estimation and parameter updates, because of its capabilities in dealing with nonlinearity and state constraints. In addition, one of the applications is to provide the full state information for Model Predictive Controller (MPC) to control the process in either setpoint tracking or economic control purposes. However, the computational burden of MHE could deteriorate the control performance if the feedback delay caused by computation is too long, leading to potential safety issues or process damage. In this paper, we propose a fast moving horizon estimation algorithm to overcome the long computational time of MHE for real-time control applications, especially for fast dynamics or large-scale systems. We exploit the nonlinear programming (NLP) sensitivity and make use of efcient NLP solvers, IPOPT and k_aug , to reduce the on-line computational costs. This new approach is demonstrated on a CSTR process, where results are compared to ideal MHE and advanced-step MHE (asMHE).
In this work we develop a non-parametric method to estimate the input-passivity index of an unknown linear and time-invariant (LTI) system from iterative experiments based on the power method from numerical linear alg...
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In this work we develop a non-parametric method to estimate the input-passivity index of an unknown linear and time-invariant (LTI) system from iterative experiments based on the power method from numerical linear algebra. Inspired by the power method for estimating the H∞-norm (or L 2 -gain) from data, we propose an algorithm that time-reverses input-output data in order to emulate measurements of a virtual system whose L 2 -gain matches the passivity index of the original system under study. While the proposed method requires exciting the original system twice, we also introduce an improved sampling scheme where only one experiment per iteration is needed.
estimation using a suboptimal method can lead to imprecise models, with cascading effects in complex models, such as climate change or pollution. The goal of this study is to compare the solutions supplied by differen...
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estimation using a suboptimal method can lead to imprecise models, with cascading effects in complex models, such as climate change or pollution. The goal of this study is to compare the solutions supplied by different algorithms used to model ozone pollution. Using Box and Tiao (1975) study, we have predicted ozone concentration in Los Angeles with an ARIMA and an autoregressive process. We have solved the ARIMA process with three algorithms (i.e., maximum likelihood, like Box and Tiao, conditional least square and unconditional least square) and the autoregressive process with four algorithms (i.e., Yule-Walker, iterative Yule-Walker, maximum likelihood, and unconditional least square). Our study shows that Box and Tiao chose the appropriate algorithm according to the AIC but not according to the mean square error. Furthermore, Yule-Walker, which is the default algorithm in many software, has the least reliable results, suggesting that the method of solving complex models could alter the findings. Finally, the model selection depends on the technical details and on the applicability of the model, as the ARIMA model is suitable from the AIC perspective but an autoregressive model could be preferred from the mean square error viewpoint. Our study shows that time series analysis should consider not only the model shape but also the model estimation, to ensure valid results.
The phase of the signals backscattered by the ultrahigh frequency radio-frequency identification (UHF-RFID) tags is generally more insensitive to multipath propagation than the received signal strength indicator (RSSI...
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The phase of the signals backscattered by the ultrahigh frequency radio-frequency identification (UHF-RFID) tags is generally more insensitive to multipath propagation than the received signal strength indicator (RSSI). However, signal phase measurements are inherently ambiguous and could be further affected by the unknown phase offsets added by the transponders. As a result, the localization of an agent by using only signal phase measurements looks infeasible. In this article, it is shown instead that the design of a dynamic position estimator (e.g., a Kalman filter) based only on the signal phase measurement is actually possible. To this end, the necessary conditions to ensure the theoretical local nonlinear observability are first demonstrated. However, a system that is locally observable guarantees the convergence of the localization algorithm only if the actual initial agent position is approximately known a priori. Therefore, the second part of the analysis covers the global observability, which ensures convergence starting from any initial condition in the state space. It is important to emphasize that complete observability holds only in theory. In fact, measurement uncertainty may greatly affect position estimation convergence. The validity of the analysis and the practicality of this localization approach are further confirmed by numerical simulations based on an unscented Kalman filter (UKF).
This paper proposes a new nonlinear attitude observer based on high-grade rate gyros and single body-fixed vector measurements of a constant inertial vector, in contrast with typical solutions that require two of thes...
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This paper proposes a new nonlinear attitude observer based on high-grade rate gyros and single body-fixed vector measurements of a constant inertial vector, in contrast with typical solutions that require two of these vectors. The structure is cascaded, where in the first block a vector that is related to the angular velocity of the Earth is estimated and in the second block the attitude itself is obtained. The attitude is directly estimated on the special orthogonal group and the estimation error is shown to converge to zero, with a region of convergence that is best described as semi-global, with local exponential convergence. Simulation results illustrate the achievable performance of the proposed solution and the robustness to sensor noise. (C) 2019 Elsevier B.V. All rights reserved.
Comprehensive measures for the estimation performance evaluation (EPE) has become increasingly prominent. This paper proposed a new radar chart evaluation method to measure the estimation performance. Firstly, the new...
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Comprehensive measures for the estimation performance evaluation (EPE) has become increasingly prominent. This paper proposed a new radar chart evaluation method to measure the estimation performance. Firstly, the new radar chart index, which is composed of several popular incomprehensive measures, are presented, and the method of the weight of the each index is calculated based on vector ranking method. Secondly, the new comprehensive measures for the EPE is designed according to the fan area and the fan arc length. Finally, two cases study are provided to verify the effectiveness of this method.
The positioning problem addressed in this article amounts to finding the planar coordinates of a device from a collection of ranging measurements taken from other devices located at known positions. The solution based...
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The positioning problem addressed in this article amounts to finding the planar coordinates of a device from a collection of ranging measurements taken from other devices located at known positions. The solution based on weighted least square (WLS) is popular, but its accuracy depends from a number of factors only partially known. In this article, we explore the dependency of the uncertainty from the geometric configuration of the anchors. We show a refinement technique for the estimate produced by the WLS that compensates for the effects of geometry on the WLS and reduces the target uncertainty to a value very close to the Cramer-Rao Lower Bound. The resulting algorithm is called geometric WLS (G-WLS) and its application is particularly important in the most critical conditions for WLS (i.e., when the target is far apart from the anchors). The effectiveness of the G-WLS is proven theoretically and is demonstrated on a large number of experiments and simulations.
This paper addresses the problem of estimating the frequencies, amplitudes and phases of then sinusoidal components of a possibly biased multi-sinusoidal signal. The proposed adaptive observer allows the direct adapta...
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This paper addresses the problem of estimating the frequencies, amplitudes and phases of then sinusoidal components of a possibly biased multi-sinusoidal signal. The proposed adaptive observer allows the direct adaptation of the frequency estimates with a relatively low dynamic order 3n + 1 (3n for an unbiased signal). The stability analysis proves the global exponential convergence of the estimation error and the robustness to additive norm-bounded measurement perturbations. (C) 2018 Elsevier Ltd. All rights reserved.
This paper considers the observer problem for a class of nonlinear systems which are not observable, but can have a finite number of (non-converging) indistinguishable trajectories. This means that in general it does ...
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This paper considers the observer problem for a class of nonlinear systems which are not observable, but can have a finite number of (non-converging) indistinguishable trajectories. This means that in general it does not exist a usual, i.e. single-valued, observer. Instead, a multivalued one is proposed here, allowing to estimate the full set of indistinguishable trajectories. By extending the system's dynamics, it is also possible to estimate all possible unknown inputs acting on the system. The approach is illustrated with various examples, including experiments with a magnetic levitation system. (c) 2020 Elsevier Ltd. All rights reserved.
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