Deep Neural Network for Indoor Positioning Based on Channel Impulse Response

• Fingerprinting positioning aided by wireless technologies plays an important role in a variety of industrial applications, such as factory automation, warehouse automation, and underground mining, where guaranteeing a position prediction error smaller than a threshold value is necessary to meet certain functional requirements. In this paper, we firstly design a deep convolutional neural network that uses the channel impulse response measurement as an input parameter to predict the position of a mobile robot. Second, we propose a simulated annealing algorithm that finds a minimum number of access points with their respective optimal positions that satisfies an expected average distance error in terms of a mobile robot's predicted position. The obtained results show that the average distance error is significantly reduced, e.g., by half compared to the case without optimal positions of access points.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering (hsv//eng)

Keyword

channel impulse response

convolutional neural network

fingerprinting positioning

simulated annealing

Convolution

Convolutional neural networks

Deep neural networks

Indoor positioning systems

Machine design

Mobile robots

Access points

Average Distance

Indoor positioning

Optimal position

Underground mining

Warehouse automation

Wireless technologies

Errors

Publication and Content Type

ref (subject category)

kon (subject category)