Source Direction Finding and Direct Localization Exploiting UAV Array With Unknown Gain-Phase Errors

Li, Jianfeng; Zhang, Qiting; Deng, Weiming; Tang, Yawei; Zhang, Xiaofei; Wu, Qihui

doi:10.1109/jiot.2022.3181450

Cited by 19 publications

(4 citation statements)

References 33 publications

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“…Direction of arrival (DOA) estimation has increasingly become a hot topic in the field of array signal processing since it is continually encountered in many applications, such as radar, wireless communication and automatic driving [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In the past half century, numerous subspace-based algorithms [ 10 , 11 ] and sparse reconstruction approaches [ 12 , 13 ] have been proposed to estimate the incident direction of multiple sources.…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array

Zhang

Zhou

et al. 2023

Sensors

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To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.

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Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array

Zhang

Zhou

et al. 2023

Sensors

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“…Parameter estimation has drawn widespread concern and become a research hotspot in the field of array signal processing over the past few decades, especially for directionof-arrival (DOA) estimation [1,2]. To the best of our knowledge, DOA estimation mainly uses sensor array to sample, analyze, and process spatial signals, achieving azimuth and elevation angles estimation for interested targets.…”

Section: Introductionmentioning

confidence: 99%

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Wang

Lan

et al. 2023

Sensors

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In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution’s sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.

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“…The instrumental sensor method (ISM), or so-called auxiliary element method, is introduced in [20]; it demands auxiliary elements more than the number of emitters. In [21], we propose a joint amplitude-phase error calibration and DPD method that relies on singlearray movement to construct overall cost function and quadratic optimization, realizing the decoupling of amplitude-phase errors and positions. Nonetheless, synchronization errors and attenuation coefficients are not considered in these methods.…”

Section: Introductionmentioning

confidence: 99%

A Direct Position Determination Method under Unknown Multi-Perturbation with Moving Distributed Arrays

Zhang

Tang

et al. 2023

Electronics

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Distributed array manifold perturbation, which includes synchronization errors, amplitude-phase errors, and path attenuation, has seriously degraded the accuracy of existing direct position determination (DPD) methods. In this paper, a DPD method under unknown multi-perturbation with moving distributed coprime arrays is advocated for. Firstly, by means of array position interchange, the integrated signals received from distributed arrays can be fused, which contributes to multi-position fusion. Subsequently, by resorting to the orthogonality between the noise subspace and steering vector received via distributed arrays, a quadratic optimization problem is constructed. Finally, we realize multi-parameter decoupling and achieve localization regardless of unknown perturbations. The superiority of the advocated method is substantiated from simulation examples.

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Source Direction Finding and Direct Localization Exploiting UAV Array With Unknown Gain-Phase Errors

Cited by 19 publications

References 33 publications

Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array

Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

A Direct Position Determination Method under Unknown Multi-Perturbation with Moving Distributed Arrays

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