
Sparse Bayesian inference for on‐grid direction‐of‐arrival (DOA) estimation using difference coarray was investigated in the authors’ previous work to estimate more signal sources than the number of physical antenna elements. Sparse Bayesian inference is derived based on a linear inverse model and the DOAs of incident signals are indicated by the sparse support of the power spectrum for a predefined dictionary. Thus, the DOA estimation accuracy of Bayesian inference is limited by the accuracy of power estimation. An enhanced off‐grid DOA estimation algorithm by combining coarray Bayesian inference with power correction is proposed in this study. Simulation results show that the additional step of corrected power increases the DOA estimation accuracy significantly.
linear inverse model, incident signals, power correction, power spectrum, Engineering (General). Civil engineering (General), power estimation, signal sources, bayes methods, sparse bayesian inference, direction-of-arrival estimation, coarray bayesian inference, corrected power bayesian inference, TA1-2040, doa estimation accuracy, off-grid doa estimation algorithm, difference coarray, on-grid direction-of-arrival estimation
linear inverse model, incident signals, power correction, power spectrum, Engineering (General). Civil engineering (General), power estimation, signal sources, bayes methods, sparse bayesian inference, direction-of-arrival estimation, coarray bayesian inference, corrected power bayesian inference, TA1-2040, doa estimation accuracy, off-grid doa estimation algorithm, difference coarray, on-grid direction-of-arrival estimation
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