Joint diagonalization based 2D-DOD and 2D-DOA estimation for bistatic MIMO radar

Xia, Tieqi

doi:10.1016/j.sigpro.2015.04.014

Cited by 33 publications

(33 citation statements)

References 13 publications

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“…In this part, we evaluate the effectiveness of the proposed method in terms of several simulations. The proposed algorithm is compared with Xia's algorithm [17], asymptotic performance analysis (Proposed asy.) in Equations (30) and (31), and the derived stochastic CRB in Equation (40).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Remark 2. Compared to the Xia's algorithm [17], the proposed algorithm exploits the redundancy existing in the noncircular signals, which improves the array virtual aperture. Additionally, the maximum numbers of detectable signals by the proposed algorithm is based on the new data vetor in Equation 7as well as the matrices K l1 and K l2 , l = 1, 2 in Equations (14) and (15) for 2D DODs, K l1 and K l2 , l = 3, 4 in Equations (16) and Equation 17for 2D DOAs, which are shown in Table 2 compared to Xia's algorithm.…”

Section: Remarkmentioning

confidence: 99%

“…Furthermore, joint diagonalization-based ESPRIT method was presented in reference [15], where a closed-form expression for both DOA and DOD was obtained and automatically paired. For MIMO radar systems based on planar arrays, a method for joint estimation of 2D-DOD and 2D-DOA was presented in references [16][17][18] by transforming the four dimensional (4D) angle estimates into four 1D estimates without any pairing procedures. In addition, the method in reference [17] can also work well when common 1D angles are presented by joint diagonalization, but the method in reference [16] cannot.…”

Section: Introductionmentioning

confidence: 99%

“…For MIMO radar systems based on planar arrays, a method for joint estimation of 2D-DOD and 2D-DOA was presented in references [16][17][18] by transforming the four dimensional (4D) angle estimates into four 1D estimates without any pairing procedures. In addition, the method in reference [17] can also work well when common 1D angles are presented by joint diagonalization, but the method in reference [16] cannot. Based on the electromagnetic vector sensors (EVSs), 2D-DODs and 2D-DOAs were estimated using an ESPRIT-based method for the bistatic MIMO radar, with an additional optimization function for pair matching [18].…”

Section: Introductionmentioning

confidence: 99%

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2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

Fang

Liu

Jiang

et al. 2020

Sensors

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In this paper, a joint diagonalization based two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) estimation method for a mixture of circular and strictly noncircular (NC) sources is proposed based on an L-shaped bistatic multiple input multiple output (MIMO) radar. By making full use of the L-shaped MIMO array structure to obtain an extended virtual array at the receive array, we first combine the received data vector and its conjugated counterpart to construct a new data vector, and then an estimating signal parameter via rotational invariance techniques (ESPRIT)-like method is adopted to estimate the DODs and DOAs by joint diagonalization of the NC-based direction matrices, which can automatically pair the four dimensional (4D) angle parameters and solve the angle ambiguity problem with common one-dimensional (1D) DODs and DOAs. In addition, the asymptotic performance of the proposed algorithm is analyzed and the closed-form stochastic Cramer–Rao bound (CRB) expression is derived. As demonstrated by simulation results, the proposed algorithm has outperformed the existing one, with a result close to the theoretical benchmark.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

Fang

Liu

Jiang

et al. 2020

Sensors

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“…Majority of these investigations focus on the estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) of uncorrelated targets [2][3][4][5][6][7] and coherent targets [8,9]. Two-dimensional (2D) DOD and 2D-DOA estimation have also been investigated for the case of uncorrelated targets [10][11][12]. 2D means azimuth and elevation angles of a target.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Polarization Angle Estimation of Mixed-Targets in Mimo Radar

Chintagunta¹,

Ponnusamy²

2019

PIER M

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This paper proposes an approach for estimating the spatial and polarization angles of mixed-targets in bistatic MIMO radar. Mixed-targets mean the combination of uncorrelated, partially correlated, and groups of coherent targets. The approach resolves rank deficiency of received signal covariance matrix and then exploits the ESPRIT-based method for estimating the angles of directionof-departure (DOD) and direction-of-arrival (DOA). This paper also presents an analytical review and necessary conditions for resolving the rank deficiency under various scenarios of the MIMO radar. Simulation results show the effectiveness of the proposed approach.

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Joint Angle-Polarization-Range Estimation for EVS-FDA-MIMO Radar

Zhong,

Tao

2023

Signals and Communication Technology

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Joint diagonalization based 2D-DOD and 2D-DOA estimation for bistatic MIMO radar

Cited by 33 publications

References 13 publications

2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar

Spatial and Polarization Angle Estimation of Mixed-Targets in Mimo Radar

Joint Angle-Polarization-Range Estimation for EVS-FDA-MIMO Radar

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