Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for Enhancement of Location Recognition

Abstract

Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.

Research Progress of Wireless Positioning Methods Based on RSSI

“…Therefore, wireless positioning using ultrasonic, WIFI, Bluetooth, radio frequency identification, infrared and other technologies has gradually become the focus of attention [6]. As a common measurement index, RSSI can realize positioning without additional measurement means, such as laser, camera, magnetic, ultrasonic acoustic sensor or lidar [7], thus reducing positioning cost and energy consumption [8]. However, the original RSSI signal from the wireless access point is susceptible to obstacles, signal fluctuations, noise, environmental changes, non-line-of-sight communication, multipath interference, etc.…”

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

“…Real-world time series data generally exhibit non-linearities [12], making it challenging to apply conventional prediction techniques [13]. Therefore, advanced techniques were employed to address this issue, including convolutional neural network (CNN) [14], long short-term memory (LSTM) attention [15], and quantile regression [11], to accurately predict machine failures and manage uncertainties present in the data.…”

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival *

“…There are three main positioning methods based on the seismic wave: direction of arrival (DOA) [17,18], received signal strength indicator [19,20] (RSSI), and TDOA [21,22].…”