Investigation of array robustness to sensor failure

Alexiou, Angeliki; Manikas, A.

doi:10.1016/j.jfranklin.2004.11.005

Cited by 20 publications

(7 citation statements)

References 16 publications

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“…Note that the sensors at both ends of array are more important than others. It was shown in [33] that for the ULA with 6 sensors, the position set is {0,1,2,3,4,5}, the sensors at 0 and 5 are more important than sensors at 1, 2, 3, and 4. According to the Theorem 2 in [11], the sensors at 0 and N − 1 are the most essential ones while the others are inessential.…”

Section: Failure Sensor Diagnosis and Classificationmentioning

confidence: 99%

Direction-of-Arrival Estimation Under Array Sensor Failures with ULA

Sun

Shi

et al. 2020

IEEE Access

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In this paper, to address the problem of array sensors failure, we propose a covariance matrix reconstruction method for direction-of-arrival (DOA) estimation. Firstly, we devise a diagnosis method to detect and locate the positions of failure sensors. According to the robustness of the array, the sensor failure scenarios are classified into redundant sensors failure and non-redundant sensors failure. Then, the corresponding DOA estimation method is adopted for two failure scenarios. The former can be solved using the virtual sensors in the difference coarray. As for the latter, the difference coarray has some holes, resulting in the decrease of available continuous virtual sensors or degrees of freedom (DOFs). Based on the matrix completion theory, the covariance matrix is extended to a high-dimensional Toeplitz matrix with missing data, where some elements are zero. We employ the mapping matrix, further use trace norm instead of the rank norm for convex relaxation to reconstruct the covariance matrix, thereby realizing the filling of the virtual sensor holes in difference coarray and restoring the DOFs. Compared with the sparsitybased methods, the proposed method can eliminate the effect of the discretization of the angle domain, and avoid regularization parameter selection. Finally, the root-MUSIC method is given for DOA estimation. Theoretical analysis and simulation results show that the proposed methods can alleviate the effect of array sensors failure and improve the estimation performance.

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Section: Failure Sensor Diagnosis and Classificationmentioning

confidence: 99%

Direction-of-Arrival Estimation Under Array Sensor Failures with ULA

Sun

Shi

et al. 2020

IEEE Access

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“…At present, some methods have been proposed in [ 12 , 13 ] to deal with an array with impaired sensors. The basic idea of these methods is that the missing observation data from the impaired sensors can be obtained by reconstructing the array output.…”

Section: Introductionmentioning

confidence: 99%

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

Lan,

Jiang,

et al. 2024

Sensors

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Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

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“…However, the above spatial-time DOA estimation methods based on SDCA involve high-dimensional data processing of multiple pseudo snapshots, which has high computational load. Additionally, another limitation of the above methods is that the case of sensor failure [24][25][26] always occurring in the actual direction-finding system has been ignored. e failure of some sensors or receiving channels may destroy the virtual array structure of sparse array and reduce the number of consecutive DOF and the effective array aperture, which results in the performance degradation.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

Zhao

Wang

2021

International Journal of Antennas and Propagation

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This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

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Investigation of array robustness to sensor failure

Cited by 20 publications

References 16 publications

Direction-of-Arrival Estimation Under Array Sensor Failures with ULA

Direction-of-Arrival Estimation Under Array Sensor Failures with ULA

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

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