Calibrating Nested Sensor Arrays With Model Errors


We think about the matter of direction of arrival (DOA) estimation primarily based on a nonuniform linear nested array, that is understood to produce degrees of freedom (DOFs) using only sensors. Both subspace-primarily based and sparsity-based algorithms require bound modeling assumptions, e.g., specifically known array geometry, including sensor gain and part. In practice, however, the particular sensor gain and phase are often perturbed from their nominal values, which disrupts the prevailing DOA estimation algorithms. In this paper, we have a tendency to investigate the self-calibration problem for perturbed nested arrays, proposing corresponding sturdy algorithms to estimate each the model errors and therefore the DOAs. The partial Toeplitz structure of the covariance matrix is used to estimate the gain errors, and also the sparse total least squares (STLS) is employed to accommodate the section error issue. As well, we tend to offer the Cramér-Rao certain (CRB) to analyze the robustness of the estimation performance of the proposed approaches. Furthermore, we have a tendency to extend the calibration methods to general nonuniform linear arrays. Numerical examples are provided to verify the effectiveness of the proposed strategies.

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