A study of TDOA estimation using Matrix Pencil algorithms and IEEE 802.11ac

Gaber, Abdo; Omar, A.S.

doi:10.1109/upinlbs.2012.6409772

Cited by 21 publications

(15 citation statements)

References 9 publications

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“…where Q K1 and Q K2 are unitary matrices whose columns are conjugate symmetric and have the sparse structure given in [3].…”

Section: System Model and Enhanced Unitarymentioning

confidence: 99%

“…where the superscript H denotes the conjugate transpose, U s and V s are the submatrices of U and V corresponding to the largestL singular values (SVs) of Σ s and span the signal subspace while U n and V n are corresponding to the SVs of Σ n and span the noise subspace.L (the dimension of the signal subspace) can be estimated as in [3]. Next, we calculate the selection matrices K Re and K Im to extract z l poles just once and store them as constant matrices according to …”

Section: System Model and Enhanced Unitarymentioning

confidence: 99%

“…This work is an extension to our previous work in [3]. Different realizations of the UMP algorithm to estimate the TDOA are implemented based on the physical MIMO-OFDM system parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Different realizations of the UMP algorithm to estimate the TDOA are implemented based on the physical MIMO-OFDM system parameters. An enhancement is added to the UMP presented in [3] to estimate the signal subspace based on the space channel frequency response (S-CFR). The discrete samples of the CFR can be obtained using a multicarrier modulation technique such as OFDM or channel sweeping at different frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Recent results of high-resolution wireless indoor positioning based on IEEE 802.11ac

Gaber

Omar

2014

2014 IEEE Radio and Wireless Symposium (RWS)

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This contribution addresses the problem of highly resolving the propagation time difference of arrival (TDOA) associated with signals in multipath communication channels for wireless indoor positioning. The Unitary Matrix Pencil (UMP) algorithm is enhanced and applied in a new way to estimate the propagation time delays from the estimated space channel frequency response (S-CFR) using MIMO-OFDM systems. The performance of using wideband orthogonal multicarrier signals and multiple antennas for wireless positioning are presented. A noniterative least square (LS) estimator is also presented to estimate the mobile unit (MU) coordinates based on the TDOA observations. Experimental results using the emerging IEEE 802.11ac standard show that accuracy in the range of two cm can be achieved for two-dimensional (2-D) wireless indoor positioning.

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“…where Q K1 and Q K2 are unitary matrices whose columns are conjugate symmetric and have the sparse structure given in [3].…”

Section: System Model and Enhanced Unitarymentioning

confidence: 99%

Section: System Model and Enhanced Unitarymentioning

confidence: 99%

“…This work is an extension to our previous work in [3]. Different realizations of the UMP algorithm to estimate the TDOA are implemented based on the physical MIMO-OFDM system parameters.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recent results of high-resolution wireless indoor positioning based on IEEE 802.11ac

Gaber

Omar

2014

2014 IEEE Radio and Wireless Symposium (RWS)

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“…In the particular case of LTE, pilot signals are already being thought to improve its positioning capabilities, on top of its communication purposes [3], [4]. As for WiFi signals, studies using timing [5], [6] and power information [7] have also shown the potential of these signals for navigation purposes.…”

Section: Introductionmentioning

confidence: 99%

Cyclostationary features of downsampled 802.11g OFDM signal for cognitive positioning systems

Silva

Daniel

Nurmi

et al. 2014

2014 11th International Symposium on Wireless Communications Systems (ISWCS)

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In cognitive positioning systems, spectrum sensing methods play an important role to understand the surrounding spectrum. Due to their good performance under noisy environments, cyclostationary methods are commonly used to characterise the received signals. These methods require a higher computational cost and high sampling rates [1]. With that in mind, this paper uses real measurement data, acquired in an office environment, at different sampling rates, including rates below the Nyquist rate. The motivation is to show that the implementation burden of these methods can be reduced by using lower sampling frequencies, since the cyclic properties of the signals are still visible.

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