Sensor selection for target tracking based on single dimension information gain

Wang, Huan; Ge, Jianjun; De, Zhang; Liu, Guanghong

doi:10.1049/joe.2019.0298

Cited by 2 publications

(1 citation statement)

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“…To effectively schedule sensors, some algorithms based on information theory are proposed in which information entropy was employed to evaluate the performance of sensor scheduling [ 5 , 6 ]. Instead of using information theory, Wang et al proposed the linear programming method, which models sensor scheduling as an optimization problem composed by tracking accuracy and matching a matrix of sensors [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight

Zhou

Wei

et al. 2022

Entropy

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In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.

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Section: Introductionmentioning

confidence: 99%