A Semidefinite Programming Approach for Obstacle Prediction and Localization
Konferenz: WSA 2021 - 25th International ITG Workshop on Smart Antennas
10.11.2021 - 12.11.2021 in French Riviera, France
Tagungsband: ITG-Fb. 300: WSA 2021
Seiten: 6Sprache: EnglischTyp: PDF
Vural, Metin; Yuan, Chun; Jung, Peter (Technical University of Berlin, Germany)
Kleppmann, Nicola (KT Elektronik, Klaucke und Partner GmbH, Berlin, Germany)
Localization of things is becoming increasingly important in many of today’s applications, especially in applications using wireless sensor networks (WSNs) for communication. Here, the most readily available radio information is the received signal strength (RSS). However, accurate localization of nodes based on the RSS in non-line-of-sight (NLOS) scenarios and in the presence of obstacles is challenging. Known approaches often suffer from model mismatch and strong multipath in NLOS. Inevitable noise and shadowing caused by obstacles significantly affect the accuracy of distance estimation, often making localization a very difficult task. Here, we present a robust semi-definite programming (SDP) method using RSS-values in noisy NLOS environments, which significantly improves positioning accuracy and allows for obstacle prediction.