Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

Wen, Fangqing; Shi, Junpeng; Wang, Xinhai; Wang, Lin

doi:10.3390/rs13152964

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…where the elements of zexp are obtained by replacing the estimated covariance matrix R with the expected one R in Equation (5). Define the 2-norm of the vectors ∆ zsum and ∆ z as ∥∆ zsum ∥ 2 and ∥∆ z∥ 2 , respectively.…”

Section: Analysis Of Data Redundancy Removalmentioning

confidence: 99%

See 1 more Smart Citation

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Liu,

Guo,

Arnatovich

et al. 2024

Remote Sensing

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In this paper, a lightweight deep neural network (DNN) for direction of arrival (DOA) estimation is proposed, of which the input vector is designed to remove data redundancy as well as remaining DOA information. By exploring the Vandermonde property of the steering vector of a uniform linear array (ULA), the size of the newly designed input vector is greatly reduced. Furthermore, the DOA estimation is designed as a regression problem instead of a classification problem; that is, the lightweight DNN designs the output vector as the estimated DOAs of sources, of which the size is much shorter than that of the spatial spectrum used as the output vector in the conventional DNN. The reductions in the sizes of input and output vectors lead to a reduction in the sizes of hidden layers, achieving lightweightness of the neural network. The analysis illustrates that when the number of sensors is 22, the number of parameters in the lightweight DNN is three orders of magnitude less than that in the conventional DNN. The simulation results demonstrate the lightweight DNN can provide high DOA estimation accuracy with the shortest testing time. It performs better than the conventional DNN. Furthermore, it is superior to traditional solutions such as the multiple signal classification (MUSIC) method and conventional beamforming (CBF) method in harsh conditions like low signal-to-noise ratios (SNRs), closely spaced sources, and few snapshots.

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Section: Analysis Of Data Redundancy Removalmentioning

confidence: 99%

“…Direction of arrival (DOA) estimation is a widely studied topic in the signal processing area, which performs a key role in wireless communications, astronomical observation, and radar applications [1][2][3][4][5]. The conventional beamforming (CBF) method is a classical solution for DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Liu,

Guo,

Arnatovich

et al. 2024

Remote Sensing

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“…Unfortunately, these nonlinear scalar geometries are usually too complex to be conformal with the platform. In addition, the calibration of the model errors will become more complicated due to the irregular geometries [24,25].…”

Section: Introductionmentioning

confidence: 99%

PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry

et al. 2022

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In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings.

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“…In other words, the above algorithms do not consider the influence of array gain-phase error [24,25]. Owning to the existence of gain-phase errors, the performance of many algorithms will be greatly reduced or even become invalid [26,27]. Unfortunately, for FDA-MIMO radar, few research work takes the impact of gain-phase error into account.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

Guo

Wang

Shi³

et al. 2022

Remote Sensing

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As a new radar system, FDA-MIMO radar has recently developed rapidly, as it has broad prospects in angle-range estimation. Unfortunately, the performance of existing algorithms for FDA-MIMO radar is greatly degrading or even failing under the condition of array gain-phase error. This paper proposes an innovative solution to the joint angle and range estimation of FDA-MIMO radar under the condition of array gain-phase error and an estimation algorithm is developed. Moreover, the corresponding Cramér-Rao bound (CRB) is derived to evaluate the algorithm. The parallel factor (PARAFAC) decomposition technique can be utilized to calculate transmitter and receiver direction matrices. Taking advantage of receiver direction matrix, the angle estimation can be obtained. The range estimation can be estimated by transmitter direction matrix and angle estimation. To eliminate the error accumulation effect of array gain-phase error, the gain error and phase error are obtained separately. In this algorithm, the impact of gain-phase error on parameter estimation is removed and so is the error accumulation effect. Therefore, the proposed algorithm can provide excellent performance of angle-range and gain-phase error estimation. Numerical experiments prove the validity and advantages of the proposed method.

show abstract

Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

Cited by 7 publications

References 21 publications

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry

Tensor-Based Target Parameter Estimation Algorithm for FDA-MIMO Radar with Array Gain-Phase Error

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