State estimation of spatially distributed processes using mobile sensing agents

Demetriou, Michael A.; Uciński, Dariusz

doi:10.1109/acc.2011.5991065

Cited by 34 publications

(27 citation statements)

References 28 publications

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“…In contrast to related approaches [4,6,11,14,16,17], the proposed adaptive observation strategy, in principle, treats state estimation and vehicle control as two separate problems. However, to obtain a working and closed-loop solution, both parts are coupled by information exchange.…”

Section: Adaptive Sensor Motion Strategymentioning

confidence: 99%

“…A finite element method with a characteristic Galerkin approach is applied to discretize and solve (6). The number of grid nodes amounts to about 1000.…”

Section: Dynamic Test Casementioning

confidence: 99%

“…Solving such complex problems is hard and time-consuming, especially regarding the real-time requirements of the application. This is why [4] and [6] utilize computationally efficient suboptimal sensor guidance schemes based on the gradient of the mutual information rate and Lyapunov stability arguments, respectively. Although multiple sensor vehicles are employed, the cooperation among them is either not explicitly addressed or cannot be considered optimal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes

Euler

Ritter

Ulbrich

et al. 2015

Lecture Notes in Computer Science

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Abstract. Optimal coordination of multiple sensors is crucial for efficient atmospheric dispersion estimation. The proposed approach adaptively provides optimized trajectories with respect to sensor cooperation and uncertainty reduction of the process estimate. To avoid the timeconsuming solution of a complex optimal control problem, estimation and vehicle control are considered separate problems linked in a sequential procedure. Based on a partial differential equation model, the Ensemble Transform Kalman Filter is applied for data assimilation and generation of observation targets offering maximum information gain. A centralized model-predictive vehicle controller simultaneously provides optimal target allocation and collision-free path planning. Extending previous work, continuous measuring is assumed, which attaches more significance to the course of the trajectories. Local attraction points are introduced to draw the sensors to regions of high uncertainty. Moreover, improved target updates increase the sampling efficiency. A simulated test case illustrates the approach in comparison to non-attracted trajectories.

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Section: Adaptive Sensor Motion Strategymentioning

confidence: 99%

“…A finite element method with a characteristic Galerkin approach is applied to discretize and solve (6). The number of grid nodes amounts to about 1000.…”

Section: Dynamic Test Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes

Euler

Ritter

Ulbrich

et al. 2015

Lecture Notes in Computer Science

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“…Solving such complex problems is hard and time-consuming, especially regarding the real-time requirements of the application. Demetriou and Ucinski (2011) try to circumvent the optimal control problem and propose a Lyapunov-based sensor guidance scheme that can be applied in real-time. The resulting sensor motion allows for collision avoidance and connectivity constraints, but cannot be considered optimal with respect to vehicle cooperation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Ritter

Euler

Ulbrich

et al. 2014

IFAC Proceedings Volumes

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Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles' motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained.

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“…However in the infinite-dimensional framework, we can mention a different approach in Demetriou (2011); Ucinski (2005) whose goal is to derive a stable distributed parameter state observer by using measurements from some mobile sensors which have also to be controlled to satisfy this goal.…”

Section: Introductionmentioning

confidence: 99%