LAUSR

Position fingerprinting (FP), in which a common position signature is based on the received signal strength (RSS), is one of the most efficient indoor positioning methods. Another position signature, known as the channel impulse response (CIR), is regarded as a linear temporal filter, which characterizes the multipath channel of the operating environment. We implement a channel sounder based on an orthogonal frequency-division multiplexing system to collect off-line/online CIR measurements and develop a ray-tracing (RT) channel predictor to capture the main characteristics of the channel for the off-line predicted database. We are the first to utilize RT as a channel predictor to assist indoor FP using CIR measurements. We utilize coarse localization to classify the reference points (RPs) based on the access point with the strongest RSS. We propose an RT-assisted FP (RAFP) method, in which we estimate a position by fusing the measured and predicted signatures to find the RPs with the highest correlation values between the online measurement and the off-line measured and simulated CIR databases. Experimental results show that the RAFP—positioning with a hybrid of the predicted and measured CIR—reduces the FP localization error by 25%. By incorporating simulated CIRs, the RAFP has the advantages in reducing human labor for off-line measurement collection and in using less number of CIR measurements to maintain a satisfactory performance. The results encourage a further development to reduce the cost by replacing the sounding system with the wireless network interface cards for a scalable deployment.