Maximum likelihood estimation of DOD and DOA for bistatic MIMO radar

Tang, Bo; Tang, Jun; Zhang, Yu; Zheng, Zhidong

doi:10.1016/j.sigpro.2012.11.011

Cited by 101 publications

(77 citation statements)

References 21 publications

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“…Computer simulations are carried out to evaluate the performance of the proposed algorithm by comparing with the 2-D ESPRIT [11], ESPRIT [12], Chen et al's [13] algorithms, reduced-dimension (RD)-MUSIC approach [21], as well as ML algorithm [14] for joint DOD and DOA estimation in bistatic MIMO radar. Furthermore, the results of the MUSIC approach [17] initialized by [11][12][13], are included, while the Cramér-Rao bound (CRB) is employed as the performance benchmark [12].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The parameters are the same as before except that K = 8 and the additional {θ k } and {φ k } are shown in [11][12][13] are 20 dB while that of the proposed algorithm is 10 dB. The ML algorithm [14] performs unsatisfactorily because of poor initialization while the algorithm in [21] is even worse as it cannot resolve K pairs of DODs/DOAs for SNR < 30 dB. The superiority of our proposal in terms of estimation performance over the other algorithms is demonstrated in this more challenging environment.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Furthermore, the MUSIC algorithm performs similarly with different initializations when SNR ≥ 20 dB. The average computational times of the proposed algorithm [11][12][13][14]21] for a single run are 9.80 × 10 -3 s, 1.42 × 10 -3 s, 1.26 × 10 -3 s, 1.08 × 10 -3 s, 5.54 × 10 -1 s, and 70.32 s, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The DOD and DOA estimates correspond to the peak of the maximum likelihood (ML) cost function; though optimum, a 2K-dimensional (2K-D) search is required. To alleviate the high computational burden, the search can be divided into 2K iterative 1-D searches [14] at the expense of slow convergence. Subspace algorithms such as the ESPRIT [11,12,15] and multiple signal classification (MUSIC) [16,17], which exploit the signal and noise subspaces of the sample covariance matrix, are more computationally attractive and thus widely applied.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

Chan

Huang

2015

IEEE Trans. Aerosp. Electron. Syst.

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An iterative ESPRIT-like algorithm is devised for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Our proposal can handle identical DODs and DOAs, and provides autopairing of the angle parameters. Furthermore, it is proved that the multiple signal classification methodology cannot identify (MN -1) targets, where M and N are the element numbers in the transmit and receive antennas, respectively. Simulation results are included to evaluate the performance of the proposed algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

Chan

Huang

2015

IEEE Trans. Aerosp. Electron. Syst.

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“…The direction of arrival (DOA) estimation algorithms in collocated MIMO radar have attracted a lot of attention and have been investigated by many researchers. In order to achieve the effective estimated parameters, traditional high-resolution direction finding (DF) algorithms, such as the maximum likelihood (ML) estimate, multiple signal classification (MUSIC) estimate, estimation of signal parameters by rotational invariance techniques (ESPRIT) and so on, have been widely used in the target' parameter estimation of MIMO radar [5]- [8] and have reached superior performance compared with the conventional methods. In these DF algorithms, Conventional ML, Capon and MUSIC algorithms have been used for angle estimation regardless of the array manifold in MIMO radar, but they need spatial spectrum peak searching with high computation cost.…”

Section: Introductionmentioning

confidence: 99%

Joint 2D-DOD and 2D-DOA Estimation in Bistatic MIMO Radar

Liang¹,

Yu²

2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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Abstract. In this paper, the issue of two-dimensional (2D) direction of arrival (DOA) and twodimensional (2D) direction of departure (DOD) estimation for bistatic multiple-input-multipleoutput (MIMO) radar with cross array is investigated. A novel multidimensional angle estimation methods based on unitary transformation is proposed. Firstly, a new unitary matrix is designed using the central symmetry of cross array at transmit and receive site. Then both the rotational invariance relationships in transmit and receive array can be turned into the real-value field via the unitary transformation. And 2D-DOA and 2D-DOD joint estimations can be obtained. The proposed method, without costing the aperture of array and peak searching, can realize multidimensional angle estimation with automatic pairing and provides better estimation performance than ESPRIT. Compared with the traditional unitary transformation, the steering vectors of transmit and receive array can be turned into a real-value field via the unitary matrix and transformation in our scheme, respectively, Therefore, the proposed method can realize that the eigen-value decomposition and parameter estimation can be computed absolutely in a real-value field, with a result of a lower computation complexity. Simulation results verify the correctness of theoretical analysis and the effectiveness of proposed algorithm.

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Joint 2D-DOD and 2D-DOA Estimation for Coprime EMVS–MIMO Radar

Wang

Huang

Wan

2021

Circuits Syst Signal Process

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The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor (EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS-MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS-MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm.

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Maximum likelihood estimation of DOD and DOA for bistatic MIMO radar

Cited by 101 publications

References 21 publications

Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

Joint 2D-DOD and 2D-DOA Estimation in Bistatic MIMO Radar

Joint 2D-DOD and 2D-DOA Estimation for Coprime EMVS–MIMO Radar

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