Randomized Sensor Selection for Nonlinear Systems With Application to Target Localization

Bopardikar, Shaunak D.; Ennasr, Osama; Tan, Xiaobo

doi:10.1109/lra.2019.2928208

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…As such, the sensor selection problem is posed as an integer programming (IP) problem which objective is maximizing the logarithmic determinant of the Gramian. The third approach, developed in [43], introduces a randomized algorithm for dealing with the sensor placement problem and accordingly, theoretical bounds for eigenvalue and condition number of observability Gramian are proposed. The last and most recent approach is established in [44] where the authors make use of the numerous observer designs for some classes of nonlinear systems posed as semidefinite programs (SDP).…”

Section: Motivation and Paper Contributionsmentioning

confidence: 99%

Where Should Traffic Sensors Be Placed on Highways?

Nugroho¹,

Vishnoi²,

Taha³

et al. 2021

Preprint

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This paper investigates the practical engineering problem of traffic sensors placement on stretched highways with ramps. Since it is virtually impossible to install bulky traffic sensors on each highway segment, it is crucial to find placements that result in optimized network-wide, traffic observability. Consequently, this results in accurate traffic density estimates on segments where sensors are not installed. The substantial contribution of this paper is the utilization of control-theoretic observability analysis-jointly with integer programming-to determine traffic sensor locations based on the nonlinear dynamics and parameters of traffic networks. In particular, the celebrated asymmetric cell transmission model is used to guide the placement strategy jointly with observability analysis of nonlinear dynamic systems through Gramians. Thorough numerical case studies are presented to corroborate the proposed theoretical methods and various computational research questions are posed and addressed. The presented approach can also be extended to other models of traffic dynamics.

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Section: Motivation and Paper Contributionsmentioning

confidence: 99%

Where Should Traffic Sensors Be Placed on Highways?

Nugroho¹,

Vishnoi²,

Taha³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

“…Prior works [15], [16], and [17] employed randomized sampling strategies to establish bounds on observability Gramian metrics. In [16] and [17], matrix-valued concentration inequalities, like the Ahlswede-Winter inequality [18], are used to study dynamical systems with no process or measurement noise. This paper investigates the more practical estimation problem, where the sensor network and the process it is attempting to estimate are corrupted by Gaussian noise.…”

Section: A Literature Reviewmentioning

confidence: 99%

Probabilistic Performance Bounds for Randomized Sensor Selection in Kalman Filtering

Calle¹,

Bopardikar²

2021

Preprint

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We consider the problem of randomly choosing the sensors of a linear time-invariant dynamical system subject to process and measurement noise. We sample the sensors independently and from the same distribution. We measure the performance of a Kalman filter by its estimation error covariance. Using tools from random matrix theory, we derive probabilistic bounds on the estimation error covariance in the semi-definite sense. We indirectly improve the performance of our Kalman filter for the maximum eigenvalue metric and show that under certain conditions the optimal sampling distribution that minimizes the maximum eigenvalue of the upper bound is the solution to an appropriately defined convex optimization problem. Our numerical results show the efficacy of the optimal sampling scheme in improving Kalman filter performance relative to the trivial uniform sampling distribution and a greedy sampling with replacement algorithm.

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“…For target tracking, sensors can be chosen based on their capabilities of location [38], [39] or to best provide coverage of an entire area [40]. Selection methods have also been introduced based on task allocation algorithms [41], sparsity [8], [42], cross entropy [43], and even randomized algorithms [44], [45].…”

Section: B Feature Selectionmentioning

confidence: 99%

Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception

Reily,

Zhang

2020

Preprint

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Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurate recognition, and relevant observations need to be selected in order to allow unnecessary robots to continue on to observe other targets. This research problem has not been well studied in the literature yet. In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features. As our optimization formulation is hard to solve due to the introduced non-smooth norms, we implement a new iterative optimization algorithm, which is guaranteed to converge to the optimal solution. We evaluate our approach on multi-view benchmark datasets, a case-study in simulation, and on a physical multi-robot system. Experimental results demonstrate that our approach enables accurate object recognition and effective view selection as defined by mutual information.

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Randomized Sensor Selection for Nonlinear Systems With Application to Target Localization

Cited by 15 publications

References 27 publications

Where Should Traffic Sensors Be Placed on Highways?

Where Should Traffic Sensors Be Placed on Highways?

Probabilistic Performance Bounds for Randomized Sensor Selection in Kalman Filtering

Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception

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