1,721,254 research outputs found
Efficient Targeting of Sensor Networks for Large-Scale Systems
No full textThis paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement choice on the uncertainty reduction backwards. This backward method provides an equivalent solution to a traditional forward approach under some standard assumptions, while removing the requirement of calculating a combinatorial number of covariance updates. A key contribution of this paper is to prove that the backward approach operates never slower than the forward approach, and that it works significantly faster than the forward one for ensemble-based representations. The primary benefits are shown on a simplified weather problem using the Lorenz-95 model.This work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Dr. James A. Hansen for invaluable discussions on ensemble-based
targeting and weather models
Coordinated Targeting of Mobile Sensor Networks for Ensemble Forecast Improvement
No full textThis paper presents an efficient targeting algorithm to coordinate a team of mobile sensor platforms in order to extract information from the natural environment for the purpose of improved forecasting. This coordinated targeting is complicated by the large dimensionality of the natural dynamic systems (and thus of the decision space), as well as by the constraints in the vehicle motions. While the backward formulation developed by the present authors provides a baseline framework to efficiently address the dimensionality challenge in an unconstrained setting, the key contributions of this paper are twofold: (a) to delineate how to effectively incorporate the sensor platform constrained mobility in the targeting process and (b) to demonstrate the importance of the interteam information sharing to achieve good targeting performance. Numerical examples of simplified weather forecasting verify that the presented method renders good targeting solutions while retaining computational tractability, which is crucial for the design of sensor networks that tightly interact with, and rapidly adapt to, large-scale dynamic environments.This work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Dr. James A. Hansen for invaluable discussions on ensemble-based targeting and weather models
A multi-UAV targeting algorithm for ensemble forecast improvement
Get PDFThis work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica
Darema as the overall program manager
Continuous Motion Planning for Information Forecast
Get PDFThis work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager. The authors thank Luca Bertuccelli for
insightful discussions
Ensemble-Based Adaptive Targeting of Mobile Sensor Networks
Get PDFThis work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager
An outer-approximation approach for information-maximizing sensor selection
No full textThis paper addresses information-maximizing sensor selection that determines a set of measurement locations providing the largest entropy reduction in the estimates of the state variables. A new mixed-integer semidefinite program (MISDP) formulation is proposed for this selection under the constraints resulting from communication limitations. This formulation employs binary variables indicating if the corresponding measurement location is selected, and ensures convexity of the objective function and linearity of the constraint functions by exploiting the linear equivalent form of a bilinear term involving binary variables. An outer-approximation algorithm is then developed for the MISDP formulation that obtains the global optimal solution by solving a sequence of mixed-integer linear programs for which reliable solvers are available. Numerical experiments verify the solution optimality and the computational effectiveness of the proposed algorithm by comparing it to branch-and-bound-based approaches with nonlinear programming relaxation. An example of sensor selection to track a moving target is considered to demonstrate the applicability of the proposed method and highlight its ability to handle quadratic constraints
Predictive Planning for Heterogeneous Human-Robot Teams
Get PDFThis paper addresses the problem of task allocation over a heterogeneous team of human operators and robotic agents with the object of improving mission eciency and reducing costs. A distributed systems-level predictive approach is presented which simultaneously plans schedules for the human operators and robotic agents while accounting for agent availability, workload and coordination requirements. The approach is inspired by the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework previously developed by the authors, which is used to perform the task coordination for the team in a dynamic environment. Results show that predictive systems-level planning improves mission performance, distributes workload eciently among agents, reduces operator over-utilization and leads to coordinated agent behavior.This research was supported in part by AFOSR (FA9550-08-1-0086) and MURI (FA9550-08-1-0356)
Algorithm and sensitivity analysis of information-theoretic ensemble-based observation targeting
Get PDFThis work presents an information-theoretic methodology for adaptive observation targeting within ensemble forecast frameworks. The mobile sensor targeting problem addresses decision making of assigning multiple sensor platforms (e.g. UAVs) to paths along which they will take additional measurements in order to reduce the forecast uncertainty in the verification region at the verification time. Employing entropy as a metric of uncertainty of the estimates, the targeting decision looks for a set of measurement points that reveals the largest mutual information related to the verification variables. In the ensemble forecast framework, entropy and mutual information are computed from the covariance information, as entropy is expressed as logarithm of the determinant of a covariance matrix under the Gaussian assumption. Also, constraints associated with the motion of the sensor platforms such as flight speed limitation of a UAV, are included in this decision.
A computationally efficient backward selection algorithm forms the backbone of the proposed targeting approach. To address the computational burden resulting from the expense of determining the impact of each measurement choice on the uncertainty reduction in the verification site, the backward selection algorithm exploits the commutativity of mutual information. This enables the contribution of each measurement choice to be computed by propagating information backwards from the verification space/time to the search space/time. This approach dramatically reduces the number of times of computationally expensive covariance updates -- equivalently, perturbation ensemble updates -- needed for finding the optimal targeting solution. Numerical experiments using an idealized chaos model verifies the effectiveness of the algorithm.
Due to limitation of available ensemble size for a realistic weather model, real implementation of the proposed targeting algorithm might suffer from performance degradation. This work performs sensitivity analysis to quantify the degree of impact that small ensemble size might have on the performance of the ensemble-based targeting. Two new concepts of range-to-noise ration (RNR) and probability of correct decision (PCD) are introduced in this quantification and their formulae are derived from statistical analysis of estimation error of mutual information. Theoretical prediction of the degree of impact of small ensemble size is verified to be consistent with the numerical results.This work is funded by NSF CNS-0540331 as part of the DDDAS program with Dr. Frederica Darema as the overall program manager
Predictive positioning and quality of service ridesharing for campus mobility on demand systems
Get PDFAutonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.Ford Motor CompanyFord-MIT Allianc
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