Underdetermined direction-of-departure and direction-of-arrival estimation in bistatic multiple-input multiple-output radar

Chan, Frankie K. W.; So, Hing Cheung; Huang, Longting

doi:10.1016/j.sigpro.2014.04.019

Cited by 11 publications

(9 citation statements)

References 22 publications

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“…In [17], an alternating-projection-based maximum likelihood (ML) estimators is presented to enhance the DOD and DOA estimation performance even when the impinging targets are coherent. In [18], an ESPRIT-like estimator and a maximum likelihood estimator are proposed to determine more targets than the number of physical sensors under the condition of Swerling-II targets. Compared with Swerling-I targets, the method in [18] can identify four times targets.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

et al. 2019

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This paper provides an efficient method to determine the direction of departure (DOD) and direction of arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radars. The proposed method firstly decouples the DOD and DOA parameters by converting the original received signal model into two separate new signal models. The new signal model corresponding to DOA can be directly obtained by matched filtering operation. In order to obtain the model for DOD, vectorization operation and kronecker transformation are utilized after the matched filtering operation. Both the new signal models for DOA and DOD behave like an augmented signal model of uniform linear array (ULA). Then, a covariance-vector sparsity-aware estimator is developed to find the accurate angular parameter. Meanwhile, in order to improve the estimation accuracy, the additive noise is eliminated by exploiting the toeplitze structure inherent in the array received covariance matrix and the asymptotic distribution of the sampling errors is also derived. Furthermore, the regularization parameter setting used by the proposed estimator is derived with the aid of the Lagrangian duality theory to guarantee the sparsity of solution. Simulation results are conducted to verify the effectiveness and the superiority of the sparsity-based estimator over other methods in terms of the angular estimation accuracy.

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Section: Introductionmentioning

confidence: 99%

Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

et al. 2019

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“…In the co-located MIMO radar, since the transmit waveforms are independently received by the receive array when transmitted from 2D array, the DOA estimation boils down to 2D (azimuth and elevation) parameter estimation. Several 2D estimation methods have been introduced in the context of May 9, 2017 DRAFT MIMO radar [19], [20]. Typically, a regular array geometries such as uniform rectangular array (URA) [21] or L-shaped array [22], [23] are assumed or other properties such as the existence of orthogonally polarized components of linear arrays [24] are used to reduce the search complexity while designing the DOA estimation techniques.…”

Section: Introductionmentioning

confidence: 99%

Transmit Array Interpolation for DOA Estimation via Tensor Decomposition in 2-D MIMO Radar

Cao

Vorobyov

Hassanien

2017

IEEE Trans. Signal Process.

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In this paper, we propose a two-dimensional (2D) joint transmit array interpolation and beamspace design for planar array mono-static multiple-input-multiple-output (MIMO) radar for direction-of-arrival (DOA) estimation via tensor modeling. Our underlying idea is to map the transmit array to a desired array and suppress the transmit power outside the spatial sector of interest. In doing so, the signal-tonoise ratio is improved at the receive array. Then, we fold the received data along each dimension into a tensorial structure and apply tensor-based methods to obtain DOA estimates. In addition, we derive a close-form expression for DOA estimation bias caused by interpolation errors and argue for using a specially designed look-up table to compensate the bias. The corresponding Cramér-Rao Bound (CRB) is also derived. Simulation results are provided to show the performance of the proposed method and compare its performance to CRB.

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“…Indeed, by using the MIMO technique, beamforming can be performed in the receiver rather than in the transmitter. The advantages of MIMO radar include high resolution in angle estimation [14,15], improved target detection performance [16], and increased DOFs for clutter suppression [17]. The FDA-MIMO radar can provide greater DOFs for rangeangle control.…”

Section: Introductionmentioning

confidence: 99%

Deceptive jamming suppression with frequency diverse MIMO radar

et al. 2015

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Underdetermined direction-of-departure and direction-of-arrival estimation in bistatic multiple-input multiple-output radar

Cited by 11 publications

References 22 publications

Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

Transmit Array Interpolation for DOA Estimation via Tensor Decomposition in 2-D MIMO Radar

Deceptive jamming suppression with frequency diverse MIMO radar

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