Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network

Received signal strength indicator (RSSI) based indoor localization technology has its irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, will play a key role location-based application scenarios including smart home systems, manufacturing automation, health care, robotics. Compared with wireless coverage using conventional monopole antenna, leaky coaxial cables (LCX) can generate uniform stable over long-narrow linear-cell or irregular environment such as railway station underground shopping-mall, especially some factories zone areas from large number mental machines. This paper presents method multiple an multipath-rich environment. Different methods on time arrival (TOA) difference (TDOA), we consider improving accuracy by machine learning RSSI LCX. We present probabilistic neural network (PNN) approach utilizing The proposal aimed at two-dimensional (2-D) trajectory. In addition, also compared performance RSSI-based PNN (RSSI-PNN) TDOA same results show RSSI-PNN promising more than 90% errors are within 1 m. method, better middle area LCXs