Accuracy of spectral analysis methods for precision indoor positioning system

Lauterbach, Martin; Pola, Marek; Zaplatilek, Ludek; Bezoušek, Pavel

doi:10.1109/dt.2014.6868716

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Our spectral estimator (a modified method of calculating the covariance matrix [16], its performance [17]) is nonlinear, however in the literature (eg [7]) it was derived a simple relation for the CRB in the case of using this method to estimate the spectrum of signals by M samples at a high SNR. Although the exact relationship depends on the selected model order P (order of the covariance matrix of the signal), the general formula has a broad minimum around P = M/3…”

Section: Spectral Estimatormentioning

confidence: 99%

“…1. Figure 4 shows the behavior of errors calculated by the spectrum analysis method (SAM) -a method based on pseudospectrum analysis -in this case particularly the modified covarinace matrix method was used, [16,17] -at various signal-to-noise ratios. Again the error mean values averaged through relative replica phases for the specific replica separation ∆R are plotted.…”

Section: Correlation Estimatormentioning

confidence: 99%

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Pseudorange error analysis for precise indoor positioning system

Pola

Bezoušek

2017

Journal of Electrical Engineering

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There is a currently developed system of a transmitter indoor localization intended for fire fighters or members of rescue corps. In this system the transmitter of an ultra-wideband orthogonal frequency-division multiplexing signal position is determined by the time difference of arrival method. The position measurement accuracy highly depends on the directpath signal time of arrival estimation accuracy which is degraded by severe multipath in complicated environments such as buildings. The aim of this article is to assess errors in the direct-path signal time of arrival determination caused by multipath signal propagation and noise. Two methods of the direct-path signal time of arrival estimation are compared here: the cross correlation method and the spectral estimation method.

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Section: Spectral Estimatormentioning

confidence: 99%

Section: Correlation Estimatormentioning

confidence: 99%