Abstract
Currently, the covariance matrix (CM) or pseudo CM of the received signal is processed to improve the degrees of freedom (DOFs) and estimation accuracy. This is where the second-order cumulant (SOC) in the two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm based on sparse arrays comes into play. This paper proposes a 2-D DOA estimation approach for non-circular (NC) signals based on fourth-order cumulant (FOC), which fully exploits its inherent benefits in virtual array expansion and denoising white Gaussian noise in comparison with SOC. The proposed algorithm, on the other hand, discards the original 2-D spectral peak search theory and uses the properties of the NC signal and parallel sparse array (PSA) to place the 2-D angle information in Toeplitz matrices for estimation, greatly reducing the computational complexity of processing the FOC without compromising estimation accuracy. Simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy while utilizing fewer physical sensors and a smaller array aperture.
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JW and QS contributed to the conception of the study; JW performed the data analyses and wrote the manuscript; YY and WC contributed significantly to analysis and manuscript preparation.
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Wang, J., Yang, Y., Chen, W. et al. 2-D DOA Estimation Algorithm for Non-circular Signal Based on Fourth-Order Cumulant. Circuits Syst Signal Process 42, 2480–2493 (2023). https://doi.org/10.1007/s00034-022-02218-w
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DOI: https://doi.org/10.1007/s00034-022-02218-w