Effective Audio Signal Arrival Time Detection Algorithm for Realization of Robust Acoustic Indoor Positioning

2020 ◽  
Vol 69 (10) ◽  
pp. 7341-7352
Author(s):  
Shuai Cao ◽  
Xiang Chen ◽  
Xu Zhang ◽  
Xun Chen
Keyword(s):  
Arrival Time ◽  
Audio Signal ◽  
Indoor Positioning ◽  
Detection Algorithm ◽  
Signal Arrival

An automatic phase picker for local and teleseismic events

1987 ◽  
Vol 77 (4) ◽  
pp. 1437-1445
Author(s):  
M. Baer ◽  
U. Kradolfer
Keyword(s):  
Arrival Time ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Data Set ◽  
Automatic Phase ◽  
Nonlinear Amplifier ◽  
First Motion ◽  
Transient Events ◽  
Teleseismic Events ◽  
Automatic Detection Algorithm

Abstract An automatic detection algorithm has been developed which is capable of time P-phase arrivals of both local and teleseismic earthquakes, but rejects noise bursts and transient events. For each signal trace, the envelope function is calculated and passed through a nonlinear amplifier. The resulting signal is then subjected to a statistical analysis to yield arrival time, first motion, and a measure of reliability to be placed on the P-arrival pick. An incorporated dynamic threshold lets the algorithm become very sensitive; thus, even weak signals are timed precisely. During an extended performance evaluation on a data set comprising 789 P phases of local events and 1857 P phases of teleseismic events picked by an analyst, the automatic picker selected 66 per cent of the local phases and 90 per cent of the teleseismic phases. The accuracy of the automatic picks was “ideal” (i.e., could not be improved by the analyst) for 60 per cent of the local events and 63 per cent of the teleseismic events.

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

scholarly journals Automated detection and association of surface waves

1994 ◽  
Vol 37 (3) ◽  
Author(s):  
R. G. North ◽  
C. R. D. Woodgold
Keyword(s):  
Surface Waves ◽  
Group Velocity ◽  
Rayleigh Waves ◽  
Arrival Time ◽  
Narrow Band ◽  
Broad Band ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Short Period ◽  
Group Velocities

An algorithm for the automatic detection and association of surface waves has been developed and tested over an 18 month interval on broad band data from the Yellowknife array (YKA). The detection algorithm uses a conventional STA/LTA scheme on data that have been narrow band filtered at 20 s periods and a test is then applied to identify dispersion. An average of 9 surface waves are detected daily using this technique. Beamforming is applied to determine the arrival azimuth; at a nonarray station this could be provided by poIarization analysis. The detected surface waves are associated daily with the events located by the short period array at Yellowknife, and later with the events listed in the USGS NEIC Monthly Summaries. Association requires matching both arrival time and azimuth of the Rayleigh waves. Regional calibration of group velocity and azimuth is required. . Large variations in both group velocity and azimuth corrections were found, as an example, signals from events in Fiji Tonga arrive with apparent group velocities of 2.9 3.5 krn/s and azimuths from 5 to + 40 degrees clockwise from true (great circle) azimuth, whereas signals from Kuriles Kamchatka have velocities of 2.4 2.9 km/s and azimuths off by 35 to 0 degrees. After applying the regional corrections, surface waves are considered associated if the arrival time matches to within 0.25 km/s in apparent group velocity and the azimuth is within 30 degrees of the median expected. Over the 18 month period studied, 32% of the automatically detected surface waves were associated with events located by the Yellowknife short period array, and 34% (1591) with NEIC events; there is about 70% overlap between the two sets of events. Had the automatic detections been reported to the USGS, YKA would have ranked second (after LZH) in terms of numbers of associated surface waves for the study period of April 1991 to September 1992.

scholarly journals DOI estimation through signal arrival time distribution: a theoretical description including proof of concept measurements

2021 ◽  
Author(s):  
Francis Loignon-Houle ◽  
Stefan Gundacker ◽  
Maxime Toussaint ◽  
Félix Camirand Lemyre ◽  
Etiennette Auffray ◽  
...  
Keyword(s):  
Time Distribution ◽  
Arrival Time ◽  
Theoretical Description ◽  
Arrival Time Distribution ◽  
Proof Of Concept ◽  
Signal Arrival
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