Time of arrival estimation and interference mitigation based on Bayesian compressive sensing

Interference from unknown devices makes time-of-arrival (ToA) estimation using conventional signal processing methods unreliable. In this paper, we propose new ToA estimation techniques based on Bayesian compressive sensing (BCS) to improve the accuracy of the ToA estimation under the interference scenario. Our proposed BCS based ToA estimation schemes maximize the posterior probability of the channel impulse response (CIR) with given frequency domain received signals. Simulation results show that proposed BCS based ToA estimations exhibit significantly improved ToA detection accuracy and mean-squared error performance in interference scenarios. We also demonstrate a practical example of the ToA estimation using real measured indoor channels.

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