Compressive sensing based location estimation using channel impulse response measurements

Due to the popularity of location-based services in environment with weak GPS signals, indoor location estimation problem have attracted more and more attention in recent years. Among several distance-related measurements, channel impulse response (CIR) reflects multi-path situation between the transmitter and receiver pair and is suitable to describe the characteristic of different positions. Note that CIR, which can be obtained from the inverse Fourier transform of channel frequency response in broadband wireless networks, is supported in most of the commercial standards. In this paper, a novel compressive sensing based location estimation using CIR measurements (CS-CIR) is proposed as well as fingerprinting algorithm. CIR information is collected from each reference point (RP) to access point (AP) and stored in the database. During the on-line stage, the mobile user measures CIR from the AP and compares measured CIR with those CIR values in the database. Note that user position is close to one of the RPs and user position vector is represented as a sparse vector. By applying compressive sensing theory, user position can be recovered by solving l₁-minimization problem. Simulation result validate that the CS-CIR outperforms the K-nearest neighbour method using CIR measurements and conventional received signal strength based methods.