TY - JOUR
T1 - Sensor Selection Scheme considering Uncertainty Disturbance
AU - Wentao, Shi
AU - Dong, Chen
AU - Lin, Zhou
AU - Ke, Bai
AU - Yong, Jin
N1 - Publisher Copyright:
© 2022 Shi Wentao et al.
PY - 2022
Y1 - 2022
N2 - In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix tra'nsformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.
AB - In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix tra'nsformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.
UR - http://www.scopus.com/inward/record.url?scp=85125881321&partnerID=8YFLogxK
U2 - 10.1155/2022/2488907
DO - 10.1155/2022/2488907
M3 - 文章
AN - SCOPUS:85125881321
SN - 1687-725X
VL - 2022
JO - Journal of Sensors
JF - Journal of Sensors
M1 - 2488907
ER -