Short-range indoor localization is one of the key necessities in automation industries and healthcare setups. With its increasing demand, the need for more precise positioning systems is rapidly increasing. Millimeter-wave (mm-wave) technology is emerging to enable highly precise localization performance. However, due to the limited availability of low-cost mm-wave sensors, it is challenging to accelerate research on real data. Furthermore, noise due to the hardware components of a sensor incurs perturbation in the received signal, which corrupts the estimation of range and the angle of arrival (AoA). Due to the huge success of data-driven algorithms in solving regression problems, we propose a data-driven approach, which employs two deep learning (DL)-based regression models, i.e., dense neural network and convolutional neural network, and compare their performance with two machine learning-based regression models, linear regression and support vector regression, to reduce errors in the estimate of AoA and range obtained via an mm-wave sensor. Our main goal is to optimize the localization measurements acquired from a low-cost mm-wave sensor for short-range applications. This will accelerate the development of proof of concept and foster research on cost-effective mm-wave-based indoor positioning systems. All experiments were conducted using over-the-air data collected with an mm-wave sensor, and the validity of the experiments was verified in unseen environments. The results obtained from our experimental evaluations, both for in-sample and out-of-sample testing, indicate improvements in the estimation of AoA and range with our proposed DL models. The improvements achieved were greater than 15% for AoA estimation and over 85% for range estimation compared to the baseline methods.
|Number of pages||15|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Early online date||5 Sep 2023|
|Publication status||Published - 14 Sep 2023|