This paper investigates an optimal energy allocation problem for multisensorestimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless c...
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ISBN:
(纸本)9781467364324
This paper investigates an optimal energy allocation problem for multisensorestimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless channels using uncoded analog transmissions. The FC reconstructs the source using the best linear unbiased estimator (BLUE). The sensors have limited batteries but can harvest energy and also transfer energy to other sensors in the network. A distortion minimization problem over a finite-time horizon with causal and non-causal information is studied and the optimal energy allocation policy for transmission and sharing is derived. Several structural necessary conditions for optimality are presented for the two sensor problem with non-causal information and a horizon of two time steps. Numerical simulations are included to illustrate the theoretical results.
This paper investigates an optimal energy allocation problem for multisensorestimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless c...
详细信息
ISBN:
(纸本)9781467364300
This paper investigates an optimal energy allocation problem for multisensorestimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless channels using uncoded analog transmissions. The FC reconstructs the source using the best linear unbiased estimator (BLUE). The sensors have limited batteries but can harvest energy and also transfer energy to other sensors in the network. A distortion minimization problem over a finite-time horizon with causal and non-causal information is studied and the optimal energy allocation policy for transmission and sharing is derived. Several structural necessary conditions for optimality are presented for the two sensor problem with non-causal information and a horizon of two time steps. Numerical simulations are included to illustrate the theoretical results.
This paper focuses on sensor scheduling for state estimation, which consists of a network of noisy sensors and a discrete-time linear system with process noise. As an energy constraint, only a subset of sensors can ta...
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This paper focuses on sensor scheduling for state estimation, which consists of a network of noisy sensors and a discrete-time linear system with process noise. As an energy constraint, only a subset of sensors can take a measurement at each time step. These measurements are fused into a common state estimate using a Kalman filter and the goal is to schedule the sensors to minimize the estimation error at a terminal time. A simple approach is to greedily choose sensors at each time step to minimize the estimation error at the next time step. Recent work has shown that this greedy algorithm outperforms other well known approaches. Results have been established to show that the estimation error is a submodular function of the sensor schedule;submodular functions have a diminishing returns property that ensures the greedy algorithm yields near optimal performance. As a negative result, we show that most commonly-used estimation error metrics are not, in general, submodular functions. This disproves an established result. We then provide sufficient conditions on the system for which the estimation error is a submodular function of the sensor schedule, and thus the greedy algorithm yields performance guarantees. (C) 2015 Elsevier Ltd. All rights reserved.
We consider the problem of selecting an optimal set of sensors to estimate the states of linear dynamical systems. Specifically, the goal is to choose (at design-time) a subset of sensors (satisfying certain budget co...
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We consider the problem of selecting an optimal set of sensors to estimate the states of linear dynamical systems. Specifically, the goal is to choose (at design-time) a subset of sensors (satisfying certain budget constraints) from a given set in order to minimize the trace of the steady state a priori or a posteriori error covariance produced by a Kalman filter. We show that the a priori and a posteriori error covariance-based sensor selection problems are both NP-hard, even under the additional assumption that the system is stable. We then provide bounds on the worst-case performance of sensor selection algorithms based on the system dynamics, and show that greedy algorithms are optimal for a certain class of systems. However, as a negative result, we show that certain typical objective functions are not submodular or supermodular in general. While this makes it difficult to evaluate the performance of greedy algorithms for sensor selection (outside of certain special cases), we show via simulations that these greedy algorithms perform well in practice. (C) 2016 Elsevier Ltd. All rights reserved.
Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each time-step due to constraints on the overall system. The problem addressed here is to choose which sens...
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Consider a set of sensors estimating the state of a process in which only one of these sensors can operate at each time-step due to constraints on the overall system. The problem addressed here is to choose which sensor should operate at each time-step to minimize a weighted function of the error covariances of the state estimates. This work investigates the development of tractable algorithms to solve for the optimal and suboptimal sensor schedules. A condition on the non-optimality of an initialization of the schedule is developed. Using this condition, both an optimal and a suboptimal algorithm are devised to prune the search tree of all possible sensor schedules. The suboptimal algorithm trades off the quality of the solution and the complexity of the problem through a tuning parameter. The performance of the suboptimal algorithm is also investigated and an analytical error bound is provided. Numerical simulations are conducted to demonstrate the performance of the proposed algorithms, and the application of the algorithms in active robotic mapping is explored. (c) 2012 Elsevier Ltd. All rights reserved.
In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and our objective is to optimize the a posteriori error covariance over an ...
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In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and our objective is to optimize the a posteriori error covariance over an infinite time horizon. We focus on the case where a fixed number of sensors are selected at each time step, and we characterize the exact conditions for the existence of a schedule with uniformly bounded estimation error covariance, Using this result, we construct a scheduling algorithm that guarantees that the error covariance will be bounded if the existence conditions are satisfied. We call such an algorithm complete. Finally, we provide simulations to compare the performance of the algorithm against other known techniques. (C) 2017 Elsevier Ltd. All rights reserved.
This paper investigates the state estimation problem for multi-sensor systems under undetectable attacks. The system outputs are transmitted to the estimator via a non-secure network in the presence of adversary. A ne...
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This paper investigates the state estimation problem for multi-sensor systems under undetectable attacks. The system outputs are transmitted to the estimator via a non-secure network in the presence of adversary. A necessary and sufficient condition is established for the scenario that the attacks are undetectable by the detector of the multi-sensor system. The attack parameters can be obtained by solving an optimization problem such that the attack can not only circumvent detection but also degrade the estimation performance. By considering the estimation error dynamics with uncertainties, an estimator redesign method is derived for the case that the estimator believes there may exist undetectable attacks in the communication network. An illustrative example is presented to demonstrate the effectiveness and potential of the proposed attacker design and estimator redesign techniques. (C) 2019 Elsevier Ltd. All rights reserved.
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