Lower bound of error in AOA based passive source localization using single moving platform

Hejazi, Farzam; Norouzi, Yaser; Nayebi, Mohammad Mahdi

doi:10.1109/ewdts.2013.6673206

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…Although in this simulation we suppose that signal-to-noiseratio (SN R) is 20dB, much higher SNRs is accessible due to the independency of noise in antennas pairs that results in the cancellation of noise terms in (9). Assuming identical SNR at input antennas, CRLB illustrated in Figure 4 is very close in shape and values to CRLB of the same sensor network that utilizes directional antennas with 0.02 o in standard deviation of directional of arrival (DOA) estimation error [31], [32]. Such a precision in DOA estimation can be achieved using a ULA with at least 80 antennas when SN R = 20dB [33].…”

Section: Cramer-rao Lower Bound Of Error (Crlb)mentioning

confidence: 92%

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Hejazi¹,

Joneidi²,

Rahnavard³

2020

Preprint

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In this paper, a framework for localization of multiple co-channel transmitters using phase difference measurements between two antennas mounted on sensors of a sensor network is proposed. To pursue localization, we equip each sensor with two antennas and we use temporal cross-correlations between the received signals of the two antennas to extract the phase differences between each antenna pairs, named as phase interferometry measurements (PIMs), provoked by each transmitters using tensor decomposition. We calculate Cramer-Rao lower bound of error of localization using PIMs. Our simulation results show that highly accurate estimations can be achieved using PIMs. We also compare the accuracy of our proposed technique with a sensor network that exploits highly directional linear array antennas and show that our proposed technique can perform similar to a network that employs very large antenna arrays.

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Section: Cramer-rao Lower Bound Of Error (Crlb)mentioning

confidence: 92%

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Hejazi¹,

Joneidi²,

Rahnavard³

2020

Preprint

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“…Primitive DoA estimation techniques use pencil beam antennas (e.g. dish antennas) along with mechanical actuators for steering the beam and spatial search [15], [16], [17], [18], [19], [20]. More recent techniques, use beamforming techniques over array antennas to obtain narrow beams.…”

Section: Related Workmentioning

confidence: 99%

“…As we proved in the previous section, the interaction between two waves received at each antennas forms a standing-wave and its amplitude can be measured as of ( 18) for each frequency f , measuring amplitude of the standing-wave, employing a group of EDs. Consider that (18) consists of 2a 2 k cos(2β(f )x+2πf ∆t k ) terms. Thus, estimating ∆t k and α k for k = 0, .…”

Section: A Doa Detection and Resolutionmentioning

confidence: 99%

“…Considering β = 2π f c T , (18) clarifies that angle and phase difference of PIMs for any arbitrary frequency inside [f − B 2 , f + B 2 ] would be easily extracted by applying Fourier transform over E sw (x, f ) across x, if we could measure E sw (x, f ) for an infinite length. Unfortunately, in practice we can only measure E sw (x, f ) for a limited length and it enforces a strong limitation on the frequency resolution of the Fourier transform.…”

Section: B Frequency Resolutionmentioning

confidence: 99%

“…To calculate of resolution of FFT over E sw (x, f ) across x, consider that if we have a signal for length T (in time), the highest FFT resolution possible is 1 T [48]. Given SWWG length is L, referring to (18), the frequency resolution (δ(f ))…”

Section: B Frequency Resolutionmentioning

confidence: 99%

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Phase Spectrometry For High Precision mm-Wave DoA Estimation In 5G Systems

Hejazi¹,

Rahnavard²

2021

Preprint

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In this paper, we introduce a direction of arrival (DoA) estimation method based on a technique named phase spectrometry (PS) that is mainly suitable for mm-Wave and Tera-hertz applications as an alternative for DoA estimation using antenna arrays. PS is a conventional technique in optics to measure phase difference between two waves at different frequencies of the spectrum. Here we adapt PS for the same purpose in the radio frequency band. We show that we can emulate a large array exploiting only two antennas. To this end, we measure phase difference between the two antennas for different frequencies using PS. Consequently, we demonstrate that we can radically reduce the complexity of the receiver required for DoA estimation employing PS. We consider two different schemes for implementation of PS: via a long wave-guide and frequency code-book. We show that using a frequency code-book, higher processing gain can be achieved. Moreover, we introduce three PS architectures: for device to device DoA estimation, for base-station in uplink scenario and an ultra-fast DoA estimation technique mainly for radar and aerial and satellite communications. Simulation and analytical results show that, PS is capable of detecting and discriminating between multiple incoming signals with different DoAs. Moreover, our results also show that, the angular resolution of PS depends on the distance between the two antennas and the band-width of the frequency code-book. Finally, the performance of PS is compared with a uniform linear array (ULA) and it is shown that PS can perform the same, with a much less complex receiver, and without the prerequisite of spatial search for DoA estimation.

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Error Bound Estimation for Wi-Fi Localization: A Comprehensive Survey

Zhou

Wang

et al. 2019

Lecture Notes in Electrical Engineering

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Lower bound of error in AOA based passive source localization using single moving platform

Cited by 10 publications

References 8 publications

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Phase Spectrometry For High Precision mm-Wave DoA Estimation In 5G Systems

Error Bound Estimation for Wi-Fi Localization: A Comprehensive Survey

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