• ETRI Journal
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• v.45 no.1
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• pp.138-149
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• 2023

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.258-264
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• 2016

In this paper, we propose a computationally efficient direction of arrival (DoA) estimation algorithm based on propagator method with non-uniform array. While the co-prime array techniques can improve the resolution of DoA, they generally lead to high computational complexity as the length of the coarray aperture. To reduce the complexity we use the propagator method that does not require singular value decomposition (SVD). Through simulations, we compare MUSIC with uniform lineary array, propagator method with uniform linear array, MUSIC with co-prime array, and the proposed scheme and observe that the performance of the proposed scheme is significantly better than MUSIC or propagator method with uniform linear array while it is slightly worse than computationally much more expensive co-prime array MUSIC scheme.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.367-373
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• 2016

We propose an improved direction of arrival (DoA) estimation algorithm based on co-prime array and propagator method. The propagator method with co-prime array does not require singular value decomposition (SVD) requiring much less computational complexity but exhibiting somewhat worse performance in comparison with MUSIC based on co-prime array. We notice that one cause of the performance degradation was in the avoidance of the usage of the diagonal elements of the signal autocorrelation matrix that contains the noise power spectral density. So we propose an algorithm with the diagonal elements of the signal autocorrelation matrix based on the fact that the noise power spectral density can be estimated using noise observation over a long period of time. We observe, through simulations, that the proposed scheme in this paper improves the performance, with 4 times more computational requirement, by signal-to-noise ratio of 1.5dB and by DoA resolution of $0.7^{\circ}$ at the detection probability of 95% compared with the previously introduced co-prime array propagator scheme, resulting in performance much closer to that of co-prime array-based MUSIC scheme.