A Super-Resolution-Assisted Fingerprinting Method Based on Channel Impulse Response Measurement for Indoor Positioning

The channel impulse response (CIR), which characterizes the multipath channel between a transmitter and a receiver, can serve as a received position signature for indoor position fingerprinting (FP). Since it takes large system bandwidth to distinguish individual paths along which the signal waves travel in an indoor environment, a small bandwidth may yield an unsatisfactory performance of FP based on mere CIR. In this paper, we apply the multiple signal classification (MUSIC) algorithm, a super-resolution method, to unveil the path-delay signatures covered by bandwidth-limited CIRs. With the pseudospectrum evaluated with MUSIC, we resolve and identify the arrival times of the individual paths at a sub-sample precision. We further propose a super-resolution-aided fingerprinting (SFP) algorithm to estimate the receiver's position by taking the averaged positions of the reference points (RPs) of similar FP signatures with weights evaluated by the difference in pseudospectrum and received power. Experiments in an indoor environment show that SFP reduces the positioning error compared to the FP based on conventional channel state information (CSI), and that demands fewer infrastructures and less protocol complexity than CIR-based FP does to achieve similar performance.