Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage

Sensors

Vahab Akbarzadeh²  Julien-Charles Lévesque²  Christian Gagné¹ 

[1] Laboratoire de vision et systèmes numériques, Département de génie électrique et de génie informatique, Université Laval, Québec, QC G1V 0A6, Canada

关键词: sensor placement;    gradient descent optimization;    line-of-sight coverage;   

DOI  :  10.3390/s140815525

来源: mdpi

【 摘 要 】

We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.