Targets, sensors and infinite-horizon tracking optimality

Féron, Éric; Olivier, Christian

doi:10.1109/cdc.1990.204032

Cited by 12 publications

(12 citation statements)

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“…But if the length of time horizon becomes large or infinite then finding an optimal non-myopic schedule is difficult, because the number of sensor sequences grows prohibitively large as the time horizon grows. Therefore, some researchers have considered the problem of periodic sensor schedules on an infinite time horizon [14]- [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

Liu

Fardad

Maşazade

et al. 2014

IEEE Trans. Signal Process.

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We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of discrete-time dynamical systems. We assume that multiple sensors have been deployed and that the sensors are subject to resource constraints, which limits the number of times each can be activated over one period of the periodic schedule. We seek an algorithm that strikes a balance between estimation accuracy and total sensor activations over one period. We make a correspondence between active sensors and the nonzero columns of estimator gain. We formulate an optimization problem in which we minimize the trace of the error covariance with respect to the estimator gain while simultaneously penalizing the number of nonzero columns of the estimator gain. This optimization problem is combinatorial in nature, and we employ the alternating direction method of multipliers (ADMM) to find its locally optimal solutions. Numerical results and comparisons with other sensor scheduling algorithms in the literature are provided to illustrate the effectiveness of our proposed method. Index TermsDynamical systems, alternating direction method of multipliers, state estimation, sensor networks, sensor scheduling, sparsity.

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Section: Introductionmentioning

confidence: 99%

Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

Liu

Fardad

Maşazade

et al. 2014

IEEE Trans. Signal Process.

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“…This was originally suggested in [13], and an algorithm to find the optimal periodic measurements based on generating sequences at random was developed. Here, the periodic formulation is posed as finding the periodic measurement sequence that minimizes J 1 .…”

Section: B Periodic Sensor Scheduling Problemmentioning

confidence: 99%

“…The scheduling algorithm described in the sequel produces an optimal periodic infinite horizon measurement schedule that can be determined a priori. That the optimal infinite horizon measurement schedule is periodic was first proposed in [13]. The optimal schedule in [13] is determined by generating sequences at random.…”

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confidence: 99%

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Scalable Sensing, Estimation, and Control Architecture for Large Spacecraft Formations

McLoughlin

Campbell

2007

Journal of Guidance, Control, and Dynamics

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A decentralized estimation architecture for large formations of spacecraft is introduced that, when coupled with a local controller, parameterizes the degree to which a node is a leader or a follower, eliminating the rigid classification of nodes as strictly leaders and followers. Measurements are provided by a single range/bearing sensor similar to the autonomous formation flying sensor. A reference point calculation is introduced that automatically compensates for noise in the estimation and sensing subsystems. A scheduling algorithm is developed that maximizes the information collected across the formation. The scheduling problem is posed as an infinite horizon optimization of either the formation information or covariance matrices, or the reference point information or covariance matrices. The resulting architecture is compared via simulation to a system in which each spacecraft has full knowledge of the fleet. Results show that performance degradation (root mean square position error and fuel usage) is very small compared to the ideal, full knowledge solution, and that the system performs better than the traditional leader-follower architecture.Nomenclature C i = measurement function Jacobian at spacecraft i G i = free virtual center weighting matrix at spacecraft i g i = free scalar virtual center weight at spacecraft i J 1 = infinite horizon measurement scheduling cost K = measurement period (dimensionless) N = number of spacecraft Q = system process noise covariance relative to spacecraft i v i = collection of measurement noise at spacecraft i w i = system process noise disturbance relative to spacecraft i x ci = system virtual center state relative to spacecraft î x ci = virtual center state estimate at spacecraft i x i = system state relative to spacecraft î x i = system state estimate at spacecraft i Y ci = information matrix of the virtual center state estimate at spacecraft i Y i = information matrix of the system state estimate at spacecraft i Y p = nominal steady-state periodic information matrix z i = collection of system measurements at spacecraft i j = measurement duty to spacecraft j K = periodic measurement sequence 1 = infinite horizon measurement schedule

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Multiple Manoeuvering Target Tracking: A Memory-constrained Minimum Risk Approach.

Dessoude¹,

Olivier²

1991

Engineering Systems With Intelligence

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Targets, sensors and infinite-horizon tracking optimality

Cited by 12 publications

References 0 publications

Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

Scalable Sensing, Estimation, and Control Architecture for Large Spacecraft Formations

Multiple Manoeuvering Target Tracking: A Memory-constrained Minimum Risk Approach.

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