Robust Semiparametric DOA Estimation in non-Gaussian Environment
Abstract
A general non-Gaussian semiparametric model is adopted to characterize the measurement vectors, i.e.\ the \textit{snapshots}, collected by a linear array. Moreover, the recently derived \textit{robust semiparametric efficient} $R$-estimator of the data covariance matrix is exploited to implement an original version of the MUSIC estimator. The efficiency of the resulting $R$-MUSIC algorithm is investigated by comparing its Mean Squared Error (MSE) in the estimation of the source spatial frequencies with the relevant Semiparametric Stochastic Cramér-Rao Bound (SSCRB).
- Publication:
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arXiv e-prints
- Pub Date:
- April 2020
- DOI:
- 10.48550/arXiv.2004.13394
- arXiv:
- arXiv:2004.13394
- Bibcode:
- 2020arXiv200413394F
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- This paper has been submitted to 2020 IEEE Radar Conference, Florence, Italy, September 21-25, 2020