Exploring Training Options for RF Sensing Using CSI

This work analyzes human behavior recognition approaches using WiFi channel state information from the perhaps less usual point of view of training and calibration needs. With the help of selected literature examples, as well as with more detailed experimental insights on our own Doppler spectrum-based approach for physical motion/presence/cardinality detection, we first classify the diverse forms of training so far employed into three main categories (trained, trained-once, and training-free). We further discuss under which conditions it is possible to move toward lighter forms of calibration or even succeed in devising fully untrained model-based solutions. Our take home messages are mainly two. First, reduced training might not necessarily kill performance (although, of course, trade-offs will emerge). Second, reduced training must come along with a careful customization of the technical detection approach to the specificities of the behavior recognition application targeted, as it seems very hard to find a one-size-fits-all solution without relying on extensive training.

Авторы
Di Domenico S.1 , De Sanctis M. 1, 3 , Cianca E.2 , Giuliano F.4 , Bianchi G.1
Редакторы
-
Издательство
Institute of Electrical and Electronics Engineers Inc.
Номер выпуска
5
Язык
Английский
Страницы
116-123
Статус
Опубликовано
Подразделение
-
Ссылка
-
Номер
-
Том
56
Год
2018
Организации
  • 1 Univ Roma Tor Vergata, Rome, Italy
  • 2 Univ Roma Tor Vergata, Dept Elect Engn, Rome, Italy
  • 3 RUDN Univ, Peoples Friendship Univ Russia, Moscow, Russia
  • 4 Univ Palermo, Palermo, Italy
Ключевые слова
-
Дата создания
19.10.2018
Дата изменения
19.10.2018
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/7509/