Disambiguation of interferometric DOA estimates in vehicular passive radar

Searle, Stephen J.

doi:10.1049/iet-rsn.2017.0046

“…4. Standard deviation of CRLB of location estimation using PIMs of (14). In this scenario, 9 sensors are placed in a line aligned yaxis, the first one is placed at (1, −1)Km, the ninth one is placed at where ∆φ x0,y0 denotes PIMs of an arbitrary known PU placed at (x 0 , y 0 ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In this section we investigate CRLB for an unbiased estimator that employs PIMs as of (14). As we mentioned in the previous section, we recovered phase differences between two antennas of each sensor provoked by a PU.…”

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

confidence: 99%

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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“…In passive multi-static radar, DOA can help to reduce the false associations between the targets and measurements [9,10]. DOA estimation in passive radar has been intensively studied and many methods have been proposed [8,[11][12][13][14][15][16][17][18][19][20][21]. Basically, these methods use a rather similar procedure.…”

Section: Introductionmentioning

confidence: 99%

Direction of arrival estimation in passive radar based on deep neural network

Lyu

¹

,

Wang

²

2021

IET Signal Processing

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Most traditional direction of arrival (DOA) estimation methods in passive radar are based on the parametric model of the antenna array manifold, and lack the adaption to the array errors. The data-driven machine learning-based methods have great array error adaption capability. However, most existing machine learning-based methods cannot be applied directly to the passive radar DOA estimation, because the array covariance matrix that they use as the input is not easy to estimate with adequate accuracy in passive radar owing to the poor target signal to clutter plus noise ratio (SCNR). A deep learning-based method for DOA estimation in passive radar is proposed here. Clutter cancelation and range-Doppler cross-correlation (RDCC) is performed to increase the target SCNR. The RDCC result is taken as the input of the deep learning method, and the amplitude and phase uncertainties of the RDCC result are treated. A two-stage deep neural network (DNN) is designed. The first stage determines the spatial sub-region of the target, and the second stage gets the refined DOA estimation. Simulations show that the proposed twostage DNN well outperforms the traditional passive radar DOA estimation method and the multi-layer perceptron network. Real experiments verify the superiority of the proposed method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Phase interferometry is widely used in electronic warefere applications for wide-band (WB) interferometric direction finding [11], [12], [13]. Since phase difference between two antennas introduces ambiguous DOAs, most techniques proposed in the literature suggest using two or more baselines to disambiguate phase difference [14], [15]. Here, we suggest measuring phase differences using a sensor network that collects PIMs from different geographical locations to disambiguate the phase and directly translate phase differences to PUs locations.…”

Section: Introductionmentioning

confidence: 99%

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Hejazi¹,

Joneidi²,

Rahnavard³

2020

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

Disambiguation of interferometric DOA estimates in vehicular passive radar

Cited by 12 publications

References 28 publications

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Direction of arrival estimation in passive radar based on deep neural network

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

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