OFDM indoor positioning based on TDOAs: Performance analysis and experimental results

Global navigation satellite systems (GNSSs) can deliver very good position estimates under optimum conditions. However, in urban and indoor scenarios positioning with GNSS is often impossible. On the other hand, cellular wireless communication systems, e.g., the new, orthogonal frequency division multiplexing (OFDM) based third generation partnership project's long-term evolution (3GPP-LTE), provide excellent coverage in urban and most indoor environments. Thus, this paper researches timing based positioning algorithms for OFDM using time difference of arrival (TDOA) measurements and the 3GPP-LTE signals. Therefore, it introduces synchronization, TDOA estimation, and signal-to-noise ratio (SNR) estimation algorithms. To solve the navigation equation for TDOA, this paper considers the static Gauss-Newton algorithm, positioning Kalman filter and particle filter. Further, this paper derives new Cramér-Rao lower bounds (CRLBs) to analyze the obtained algorithms. First, new CRLBs are derived for TDOA estimation and pairwise synchronized or fully synchronized transmitters. Afterwards, static and novel dynamic recursive Bayesian CRLBs are derived for position estimation. The CRLBs are compared to real estimated TDOAs and positions. Improvements of the positioning algorithms are still possible compared to the CRLBs. Nevertheless, this paper demonstrates that indoor positioning with TDOAs from OFDM based on 3GPP-LTE is possible.