Adaptive time delay estimation algorithm for indoor near‐field electromagnetic ranging

Abstract

SUMMARYThe near-field electromagnetic ranging technology exploits the near-field behavior of radio signals to measure distance in indoor real-time location system. In this paper, we propose a new adaptive timedelay estimation algorithm based on maximum correntropy criterion (MCC) and received signal strength indication (RSSI), abbreviated as RSSIMCC. The proposed RSSIMCC algorithm estimates the time delay between the electric and magnetic components of low-frequency signals in the near field instead of detecting the phase difference. An inaccurate time-delay estimation value obtained according to the RSSI is used as the initial time delay of adaptive time-delay estimator, which is not far from the true time delay and can improve the convergence speed. Through the analysis of simulation and experiment results, we conclude that the proposed RSSIMCC algorithm can estimate the time delay with small steady-state error in nearfield electromagnetic ranging application. Then the time delay estimated by RSSIMCC algorithm can be exploited based on the near-field behavior of radio signals for ranging in an indoor environment. Copyright

An Original Correction Method for Indoor Ultra Wide Band Ranging-Based Localisation System

“…We make the same observation, the distance measured by SDS-TWR is very close to the actual distance, which confirms here too the good implementation of the protocol in our environment. 1,482 -0,017 0,03 -0,07 0,02390231 2 2,061 0,061 0,11 0,01 0,02122458 2,5 2,546 0,046 0,08 0,01 0,01724975 3 3,067 0,067 0,11 0,03 0,02023639 5 5,107 0,107 0,15 0,07 0,01559689…”

“…A new experiment was performed on 11 positions regularly spaced 0.5 meter between nodes A and B (from 0.5 m to 5.5 m). For each point, we set a delay of 1,2,3,4,5,6,7,8,9,10,16 and 21 ms. Thirty measurements were performed for each delay value, yielding 240 samples per position. A new ranging session begins every 200ms.…”

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

“…Based on this observation, the authors propose the generalized cross correlation method (Hertz and Azaria, 1985;Kang et al, 2012;Zhang et al, 2015), the phase spectrum estimation method (Harada, 2014;Choudhuri et al, 2016), the adaptive estimation method (Carrier and Got, 2014;Wang et al, 2017) and so on to implement the TDOA estimation, which significantly improves the antinoise property, estimation accuracy and resolution of the algorithm. However, these algorithms are all based on the second-order statistics and the assumption that the noises follow the Gaussian distribution.…”

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

“…When noisy signal follows the Gaussian distribution, the above method can estimate the time delay by detecting the peak position of the cross-correlation function of the signals x 1 (n) and x 2 (n). Based on this observation, the authors propose the generalized cross correlation method (Hertz and Azaria, 1985;Kang et al, 2012;Zhang et al, 2015), the phase spectrum estimation method (Harada, 2014;Choudhuri et al, 2016), the adaptive estimation method (Carrier and Got, 2014;Wang et al, 2017) and so on to implement the TDOA estimation, which significantly improves the antinoise property, estimation accuracy and resolution of the algorithm. However, these algorithms are all based on the second-order statistics and the assumption that the noises follow the Gaussian distribution.…”

“…Since the classic article on TDOA written by Knapp and Carter was published in 1976, this problem has perpetually been a research focus in the field of international signal processing (Knapp et al, 1976). The common method of TDOA includes the generalized cross correlation method (Knapp et al, 1976;Souden et al, 2010;Jin et al, 2013), the phase spectrum estimation method (Youn et al, 1982;Qiu et al, 2012), the generalized bi-spectral estimation method (Hinich et al, 1992;Hou et al, 2013), the adaptive estimation method (Gedalyahu et al, 2010;Salvati and Canazza, 2013), the energy method based on Hilbert-Huang transform and so on. These methods have been widely used in many fields.…”