Since the traditional direction-of-arrival (DOA) estimationalgorithm is passive and non-cooperation, its performance degrades with respect to the large number of terminals and closely spaced sources in multi-nodes ne...
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Since the traditional direction-of-arrival (DOA) estimationalgorithm is passive and non-cooperation, its performance degrades with respect to the large number of terminals and closely spaced sources in multi-nodes networks. A cooperated space-time hopping algorithm to estimate the DOA of all users simultaneously is presented. The signal will be adjusted by the pre-assigned hopping weights during each symbol time. The change of the weights is equivalent to the change of array manifolds, thus enriching differences between uses' channels. This algorithm is able to estimate more number of signals and separate some closely spaced sources. Simulations demonstrate the effectiveness of the algorithm.
An off-grid sparse direction-of-arrival (DOA) estimationalgorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spa...
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An off-grid sparse direction-of-arrival (DOA) estimationalgorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the DOA estimation precision present in unknown spatial coloured noise. The authors start by developing an off-grid sparse model based on linear interpolation with reweighted coefficient, which is a trade-off between tangent and secant offset, to guarantee an optimal approximation for off-grid signals. Next, the authors formulate the DOA estimation problem as solving the off-grid sparse model and, finally, the off-grid sparse model is addressed under the general framework of sparse Bayesian learning (SBL). Additional noise in IRLI is spatially coloured for calculating its statistical properties, which is different from SBL relying on the spatial white noise assumption. Numerical results with the limited snapshots and the low signal-to-noise ratio validate the algorithm by comparing with other algorithms.
This paper presents a computationally efficient direction-Of-arrival (DOA) estimationalgorithm by Uniform Linear Array (ULA), which is effective for highly-correlated sources but also works for uncorrelated sources. ...
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ISBN:
(纸本)9781479952304
This paper presents a computationally efficient direction-Of-arrival (DOA) estimationalgorithm by Uniform Linear Array (ULA), which is effective for highly-correlated sources but also works for uncorrelated sources. The proposed algorithm is basically based on the relation between the elements of array covariance matrix, does not need eigendecomposition, iteration or angular peak-search, and leads very low computational cost. The DOA estimation accuracy of the proposed method achieves almost the same with that of the Method Of directionestimation (MODE). Detailed performance of the proposed method is evaluated through computer simulation.
The presence of sensor array errors due to mutual coupling and channel mismatch among array sensors severely degrades the performance of direction-of-arrival (DOA) estimationalgorithms. This paper proposes a novel se...
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ISBN:
(纸本)9781479958368
The presence of sensor array errors due to mutual coupling and channel mismatch among array sensors severely degrades the performance of direction-of-arrival (DOA) estimationalgorithms. This paper proposes a novel sensor array errors calibration algorithm based on iterative least squares with projection (ILSP) algorithm, which suits for arbitrary array and only needs one auxiliary signal source. This algorithm firstly estimates the true steering vector by using ILSP, then estimates the sensor array errors by solving the equation between the nominal steering vector and the true steering vector. Comparative computer simulation results are presented to illustrate that the proposed algorithm still has lower computational complexity and higher calibration accuracy on the condition of less snapshots and minor DOA intervals of different sampling time.
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