Multi-UAV Cooperative Localization for Marine Targets Based on Weighted Subspace Fitting in SAGIN Environment

Wang, Xianpeng; Yang, Laurence T.; Meng, Dandan; Dong, Mianxiong; Ota, Kaoru; Wang, Huafei

doi:10.1109/jiot.2021.3066504

Cited by 90 publications

(39 citation statements)

References 35 publications

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“…The locationing effect was evaluated using the root mean square error (RMSE) metric. Differently from the single parameter estimation in [24,25], for the multi-parameter estimation problem, we defined the RMSE of the DOA and the RMSE of the range as follows: 6 and 7, the RMSE of the proposed algorithm is better than that of 2D-MUSIC with a low-density grid but worse than that of 2D-MUSIC with a high-density grid. Due to the existence of grid errors, the estimation error of 2D-MUSIC with grids of [1 • , 1 m] cannot be reduced beyond a certain value through the increase of SNR.…”

Section: Simulationsmentioning

confidence: 99%

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

et al. 2021

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The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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Section: Simulationsmentioning

confidence: 99%

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

et al. 2021

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“…Although DOD Θ t,k and DOA Θ r,k can be separately estimated from (19) and (20), they require further pairing, since there are KðK − 1Þ/2 possible DOD-DOA pairs. Recall the properties in (12) and we can rewrite (8) as…”

Section: Problem Formulationmentioning

confidence: 99%

“…As a canonical issue in bistatic MIMO systems, joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation has been studied extensively in the past decades. Various methodologies have been proposed, e.g., estimation method of signal parameters via rotational invariance technique (ESPRIT) [6], multiple signal classification (MUSIC) [7], maximum likelihood [8], tensor approaches [9,10], and sparsity-aware methods [11,12]. A common assumption in [6][7][8][9][10][11] is that the sensors are well-calibrated.…”

Section: Introductionmentioning

confidence: 99%

Angle Estimation and Mutual Coupling Self‐Calibration in Bistatic MIMO System with Arbitrary Geometry

Zhang

Wan

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Ideal array responses are often desirable to a multiple-input multiple-output (MIMO) system. Unfortunately, it may not be guaranteed in practice as the mutual coupling (MC) effects always exist. Current works concerning MC in the MIMO system only account for the uniform array geometry scenario. In this paper, we generalize the issue of angle estimation and MC self-calibration in a bistatic MIMO system in the case of arbitrary sensor geometry. The MC effects corresponding to the transmit array and the receive array are modeled by two MC matrices with several distinct entities. Angle estimation is then recast to a linear constrained quadratic problem. Inspired by the MC transformation property, a multiple signal classification- (MUSIC-) like strategy is proposed, which can estimate the direction-of-departure (DOD) and direction-of-arrival (DOA) via two individual spectrum searches. Thereafter, the MC coefficients are obtained by exploiting the orthogonality between the signal subspace and the noise subspace. The proposed method is suitable for arbitrary sensor geometry. Detailed analyses with respect to computational complexity, identifiability, and Cramer-Rao bounds (CRBs) are provided. Simulation results validate the effectiveness of the proposed method.

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“…Direction of arrival (DOA) estimation using antenna array is an important issue in many systems, e.g., radar, sonar, and wireless communication [1][2][3][4][5]. Compared to conventional subspace based methods, sparse representation based DOA estimation methods have been attractive since they can provide higher resolution and require fewer samples [6], and many effective sparse representation based methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Partial Dictionary Based Off‐Grid DOA Estimation Using Combined Coprime and Nested Array

et al. 2021

Wireless Communications and Mobile Computing

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A partial dictionary based direction of arrival (DOA) estimation method which addresses the off-grid problem and exploits combined coprime and nested array (CCNA) is proposed. Compared to general coprime array, CCNA yields two sparse coprime subarrays in the coarray domain by adding a third subarray in the physical-array domain. To ensure the DOA estimation performance, the subarray with larger aperture is chosen, and the cyclic phase ambiguity caused by the sparse subarray allows partial dictionary covering arbitrary cycle to represent the whole atoms, and then, the off-grid sparse reconstruction method is developed to amend the grid mismatch. After the sparse recovery and off-grid compensation, ambiguous DOA estimations can be eliminated by substituting the estimations into the whole virtual array. Multiple simulations verify that the proposed algorithm outperforms the other state-of-the-art methods in terms of DOA estimation accuracy and angular resolution.

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Multi-UAV Cooperative Localization for Marine Targets Based on Weighted Subspace Fitting in SAGIN Environment

Cited by 90 publications

References 35 publications

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

Angle Estimation and Mutual Coupling Self‐Calibration in Bistatic MIMO System with Arbitrary Geometry

Partial Dictionary Based Off‐Grid DOA Estimation Using Combined Coprime and Nested Array

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