5 papers
Algorithm-Supervised Millimeter Wave Indoor Localization using Tiny Neural Networks
Anish Shastri, Steve Blandino, Camillo Gentile +2
The quasi-optical propagation of millimeter-wave signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most al…
Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks
Anish Shastri, Andres Garcia-Saavedra, Paolo Casari
We consider the localization of a mobile millimeter-wave client in a large indoor environment using multilayer perceptron neural networks (NNs). Instead of training and deploying a…
ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking
Marco Canil, Jacopo Pegoraro, Anish Shastri +2
Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to cameras for the pervasive contactless monitoring of people in indoor spaces. However, commercial mmWave…
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
Anish Shastri, Neharika Valecha, Enver Bashirov +5
The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, pa…
Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks
Anish Shastri, Joan Palacios, Paolo Casari
Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches…