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Sequential Hypothesis Test with Online Usage-Constrained Sensor Selection [article]

Shang Li and Xiaoou Li and Xiaodong Wang and Jingchen Liu
2016 arXiv   pre-print
This work investigates the sequential hypothesis testing problem with online sensor selection and sensor usage constraints.  ...  The Bayesian problem is then studied under both finite- and infinite-horizon setups, based on which, the optimal solution to the original usage-constrained problem can be readily established.  ...  OPTIMAL SEQUENTIAL TEST WITH CONSTRAINED ONLINE SENSOR SELECTION In this section, we first recast (P1) into an unconstrained optimal stopping problem, which we then solve under both finite-horizon and  ... 
arXiv:1601.06447v1 fatcat:rsez34qma5e6nfqhfaaygpnsbi

Monitoring disturbances in smart grids using distributed sequential change detection

Shang Li, Xiaodong Wang
2013 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)  
PhD thesis: Distributed sequential statistical inference, with applications in anomaly detection and cyber security in sensor networks.  ...  Research Interests: Sequential decision-making, stochastic control, dynamic optimization, change-point detection, time series analysis, statistical signal processing, and statistical machine learning.  ...  [C4] Shang Li,Xiaoou Li, Xiaodong Wang, and Jingchen Liu, "Optimal Sequential Test with Finite Horizon and Constrained Sensor Selection", IEEE Int. Sym. on Inf.  ... 
doi:10.1109/camsap.2013.6714100 dblp:conf/camsap/LiW13 fatcat:gqqs5bnvizdbzkwibvr3vhcuwq

Decentralized sequential detection with a fusion center performing the sequential test

V.V. Veeravalli, T. Basar, H.V. Poor
1993 IEEE Transactions on Information Theory  
Each sensor sends a sequence of summary messages to the fusion center where a sequential test is carried out to determine the true hypothesis.  ...  A detailed analysis of this case is presented along with some numerical results. Index Terms-Decentralized detection, sequential analysis, dynamic programming. .  ...  In Section IV, we consider a finite-horizon version of the problem and establish the optimality of likelihood ratio tests at the sensors.  ... 
doi:10.1109/18.212274 fatcat:utg4tiemqjcnhd2iy2qjjviyoe

Efficient Multi-robot Search for a Moving Target

Geoffrey Hollinger, Sanjiv Singh, Joseph Djugash, Athanasios Kehagias
2009 The international journal of robotics research  
We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers.  ...  Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers.  ...  Acknowledgments We thank Andreas Krause, Christopher Geyer, Benjamin Grocholsky, and  ... 
doi:10.1177/0278364908099853 fatcat:q5hgfynx6ja75dmrzxugktjesm

Efficient Online Multi-robot Exploration via Distributed Sequential Greedy Assignment

Micah Corah, Nathan Michael
2017 Robotics: Science and Systems XIII  
DSGA retains the same suboptimality bounds as SGA with the addition of a term describing suboptimality introduced due to redundant sensor information.  ...  This work addresses the problem of efficient online exploration and mapping using multi-robot teams via a distributed algorithm for planning for multi-robot explorationdistributed sequential greedy assignment  ...  We begin by describing the system and environment models and then introduce the planning problem as a finite-horizon optimization. A.  ... 
doi:10.15607/rss.2017.xiii.070 dblp:conf/rss/CorahM17 fatcat:3z2emos5vza5vowqytyscrmhau

LQG Control and Sensing Co-Design [article]

Vasileios Tzoumas, Luca Carlone, George J. Pappas, Ali Jadbabaie
2020 arXiv   pre-print
We focus on the realistic case where the sensing design is selected among a finite set of available sensors, where each sensor is associated with a different cost (e.g., power consumption).  ...  We consider two dual problem instances: sensing-constrained LQG control, where one maximizes control performance subject to a sensor cost budget, and minimum-sensing LQG control, where one minimizes sensor  ...  We note that, in all tested instance, the proposed approach s-LQG matches the optimal selection optimal, and both approaches are relatively close to allSensors, which selects all the available sensors.  ... 
arXiv:1802.08376v7 fatcat:hziw3y2unbbnlkkpc5gdhohkum

Sensing-Constrained LQG Control [article]

Vasileios Tzoumas, Luca Carlone, George J. Pappas, Ali Jadbabaie
2020 arXiv   pre-print
Linear-Quadratic-Gaussian (LQG) control is concerned with the design of an optimal controller and estimator for linear Gaussian systems with imperfect state information.  ...  We focus on the realistic case in which the sensing strategy has to be selected among a finite set of possible sensing modalities.  ...  In particular, we formulate the sensing-constrained (finite-horizon) LQG problem as the joint design of an optimal control and estimation policy, as well as the selection of a subset of k out of N available  ... 
arXiv:1709.08826v2 fatcat:vcfcjx4lbbbw7lwniiqmrz3rze

Sensor management for multiple target tracking with heterogeneous sensor models

Jason L. Williams, John W. Fisher III, Alan S. Willsky, Ivan Kadar
2006 Signal Processing, Sensor Fusion, and Target Recognition XV  
While control of such sensors over a rolling planning horizon can be formulated as a dynamic program, the optimal solution is inevitably intractable.  ...  Modern sensors are able to rapidly change mode of operation and steer between physically separated objects.  ...  ACKNOWLEDGMENT This work was supported in part by ODDR&E MURI through ARO grant DAAD19-00-0466 and MIT Lincoln Laboratory through ACC PO#3019934.  ... 
doi:10.1117/12.666142 fatcat:2u7dszweb5gwxidql2qogbz4my

2013 Index IEEE Transactions on Automatic Control Vol. 58

2013 IEEE Transactions on Automatic Control  
., and Shroff, N. B  ...  ., +, TAC Aug. 2013 2071-2076 Finite-Horizon H Filtering With Missing Measurements and Quantization Effects.  ...  ., +, TAC July 2013 1719-1731 Finite-Horizon H Filtering With Missing Measurements and Quantization Effects.  ... 
doi:10.1109/tac.2013.2295962 fatcat:3zpqog4r4nhoxgo4vodx4sj3l4

Decentralized CUSUM Change Detection

George Moustakides
2006 2006 9th International Conference on Information Fusion  
We compare the resulting optimum test with a simple, asynchronous one shot strategy, where each sensor performs a local CUSUM test and communicates with the fusion center only once to signal its detection  ...  By introducing a recurrence relation that defines the optimum performance of the CUSUM test for given quantization, we further optimize this measure with respect to the quantization scheme.  ...  Threshold ν is selected so that the CUSUM test satisfies the false alarm constraint with equality (i.e. E ∞ [T ] = γ).  ... 
doi:10.1109/icif.2006.301578 dblp:conf/fusion/Moustakides06 fatcat:w6cyzfoyynemze4ikmn6qvm2my

Author Index

2006 2006 14th Mediterranean Conference on Control and Automation  
A Multi-level Algorithm for the Finite Horizon LQ Optimal Control Problem with Assigned Final State: Additive and Multiplicative Procedures FEA3-5 TM3-5  ...  Constrained Finite Time Control of Networked Systems with Uncertain Delays TEA2-3 Dube, M. N.  ... 
doi:10.1109/med.2006.328715 fatcat:rjjaot7sdzdsxe2ljxq56lfsdu

Explicit MIMO Model Predictive Boost Pressure Control of a Two-Stage Turbocharged Diesel Engine

Mustafa Engin Emekli, Bilin Aksun Guvenc
2017 IEEE Transactions on Control Systems Technology  
Engine dynamometer testing have been performed to define input and input rate constraints. MPC design is performed for online optimization method.  ...  This paper focuses on the design and implementation of model predictive controller (MPC) for a boost pressure control of series sequential diesel engine.  ...  Constrained finite horizon optimal control problem can be stated for output reference tracking problem as shown in (3) .  ... 
doi:10.1109/tcst.2016.2554558 fatcat:rr7cjmdjfveedopqgoblgswh2u

Resilient Monotone Sequential Maximization [article]

Vasileios Tzoumas, Ali Jadbabaie, George J. Pappas
2020 arXiv   pre-print
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple  ...  Finally, we support our theoretical analyses with simulated experiments, by considering a control-aware sensor scheduling scenario, namely, sensing-constrained robot navigation.  ...  Given a time horizon T for landing, in [2] it is proven that the UAV selects an optimal sensor schedule and generates an optimal LQG control input with cost matrices Q and R if it selects the sensors  ... 
arXiv:1803.07954v4 fatcat:iw5d6gc5ovarpkrkjbgamf6bxe

Receding horizon stochastic control algorithms for sensor management

Darin Hitchings, David A Castan
2010 Proceedings of the 2010 American Control Conference  
We explore the performance of our proposed receding horizon algorithms in simulations using heterogeneous sensors, and show that their performance is close to that of a theoretical lower bound.  ...  In this paper, we consider sensor management problems for sensors that are trying to find and classify objects.  ...  Wald [4] , [5] considered sequential hypothesis testing with costly observations.  ... 
doi:10.1109/acc.2010.5531634 fatcat:snbzkhstivax7dqdazzifca74y

Model-Predictive Dynamic Control Allocation Scheme for Reentry Vehicles

Yu Luo, Andrea Serrani, Stephen Yurkovich, Michael W. Oppenheimer, David B. Doman
2007 Journal of Guidance Control and Dynamics  
The length of the prediction horizon has been selected as N=10, with sampling time T, = 0.02 s.  ...  The main result of the section is summarized as follows: Proposition III.1: For the system given by Eq. (7), the solution of the moving-horizon sequential optimization problems with cost functions and  ... 
doi:10.2514/1.25473 fatcat:vzesz2ssc5hcfmt663ej2oiyzm
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