On efficient sensor scheduling for linear dynamical systems

Vitus, Michael P.; Zhang, Wei; Abate, Alessandro; Hu, Jianghai; Tomlin, Claire J.

doi:10.1109/acc.2010.5530899

Cited by 54 publications

(100 citation statements)

References 29 publications

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“…In general, sensor scheduling is a hard combinatorial optimization problem [8]. However, the computational complexity may be circumvented by suboptimal pruning of decision trees [9], [10]. Another approach to address the complexity issue is by use convex relaxations [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Observation-Driven Scheduling for Remote Estimation of Two Gaussian Random Variables

Vasconcelos

Mitra

2020

IEEE Trans. Control Netw. Syst.

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Joint estimation and scheduling for sensor networks is considered in a system formed by two sensors, a scheduler and a remote estimator. Each sensor observes a Gaussian source, which may be correlated. The scheduler observes the output of both sensors and chooses which of the two is revealed to the remote estimator. The goal is to jointly design scheduling and estimation policies that minimize a mean-squared estimation error criterion. The person-by-person optimality of a policy pair called "max-scheduling/mean-estimation" is established, where the measurement with the largest absolute value is revealed to the estimator, which uses a corresponding conditional mean operator. This result is obtained for independent sources, and in the case of correlated sources and symmetric variances. We also consider the joint design of scheduling and linear estimation policies for two correlated Gaussian sources with an arbitrary correlation structure. In this case, the optimization problem can be cast a difference-of-convex program, and locally optimal solutions can be efficiently found using a simple numerical procedure.

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Section: Introductionmentioning

confidence: 99%

Observation-Driven Scheduling for Remote Estimation of Two Gaussian Random Variables

Vasconcelos

Mitra

2020

IEEE Trans. Control Netw. Syst.

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“…It follows that (Σ(ν c )) 11 = σ ′ 4 for all ν ∈ {ν 14 , ν 24 , ν 34 }, which implies that trace(Σ gre ) = σ ′ 4 + 2. Furthermore, the optimal sensor attack (for the priori KFSA instance) is ν = ν 12 , where ν 12 [1 1 0 0] T , since in this case we know from Lemma 3(a) and (b) that Σ(ν c ) 11 = 1…”

Section: Proof Of Corollarymentioning

confidence: 99%

“…The problem then becomes how to select sensors dynamically (at run-time) or statically (at designtime) to minimize certain metrics of the corresponding Kalman filter. The former scenario is known as the sensor scheduling problem, where different sets of sensors can be chosen at different time steps (e.g., [12]- [14]). The latter scenario is known as the design-time sensor selection problem, where the set of sensors is chosen a priori and is not allowed to change over time (e.g., [15]- [17]).…”

Section: Introductionmentioning

confidence: 99%

On the Complexity and Approximability of Optimal Sensor Selection for Kalman Filtering

Ye¹,

Roy

Sundaram³

2018

2018 Annual American Control Conference (ACC)

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Given a linear dynamical system affected by stochastic noise, we consider the problem of selecting an optimal set of sensors (at design-time) to minimize the trace of the steady state a priori or a posteriori error covariance of the Kalman filter, subject to certain selection budget constraints. We show the fundamental result that there is no polynomialtime constant-factor approximation algorithm for this problem. This contrasts with other classes of sensor selection problems studied in the literature, which typically pursue constant-factor approximations by leveraging greedy algorithms and submodularity (or supermodularity) of the cost function. Here, we provide a specific example showing that greedy algorithms can perform arbitrarily poorly for the problem of design-time sensor selection for Kalman filtering. We then study the problem of attacking (i.e., removing) a set of installed sensors, under predefined attack budget constraints, to maximize the trace of the steady state a priori or a posteriori error covariance of the Kalman filter. Again, we show that there is no polynomial-time constant-factor approximation algorithm for this problem, and show specifically that greedy algorithms can perform arbitrarily poorly.

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“…These works consider the scheduling problem for state estimation of LTI systems with Gaussian noise. With a few exceptions (e.g., [6], [7]), a finite time horizon is considered, in which the problem is a combinatorial optimisation one [8], and hence N P-hard, making the computation of the globally optimal solution over long time horizons computationally expensive. The works considering the infinite horizon case, focus on estimation only.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Downlink Communication for a Multi-Agent System With a Central Observation Post

Zanon

Charalambous

Wymeersch

et al. 2018

IEEE Control Syst. Lett.

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In this paper, we consider a set of agents, which may receive an observation of their state by a central observation post via a shared wireless network. The aim of this work is to design a scheduling mechanism for the central observation post to decide how to allocate the available communication resources. The problem is tackled in two phases: (i) first, the local controllers are designed so as to stabilise the subsystems for the case of perfect communication; (ii) second, the communication schedule is decided with the aim of maximising the stability of the subsystems. To this end, we formulate an optimisation problem which explicitly minimises the Lyapunov function increase due to communication limitations. We show how the proposed optimisation can be expressed in terms of Value of Information (VoI), we prove Lyapunov stability in probability and we test our approach in simulations.

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On efficient sensor scheduling for linear dynamical systems

Cited by 54 publications

References 29 publications

Observation-Driven Scheduling for Remote Estimation of Two Gaussian Random Variables

Observation-Driven Scheduling for Remote Estimation of Two Gaussian Random Variables

On the Complexity and Approximability of Optimal Sensor Selection for Kalman Filtering

Optimal Scheduling of Downlink Communication for a Multi-Agent System With a Central Observation Post

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