Wireless Personal Communications: An International Journal
Underwater noise modeling and direction-finding based on heteroscedastic time series
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Advances in Signal Processing
MMSE-based MDL method for robust estimation of number of sources without eigendecomposition
IEEE Transactions on Signal Processing
Conditional and unconditional Cramér-Rao bounds for near-field source localization
IEEE Transactions on Signal Processing
Multidimensional Systems and Signal Processing
Iterative HOS-SOS (IHOSS) algorithm for direction-of-arrival estimation and sensor localization
IEEE Transactions on Signal Processing
Covariance sparsity-aware DOA estimation for nonuniform noise
Digital Signal Processing
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We consider the problem of estimating directions of arrival (DOAs) of multiple sources observed on the background of nonuniform white noise with an arbitrary diagonal covariance matrix. A new deterministic maximum likelihood (ML) DOA estimator is derived. Its implementation is based on an iterative procedure which includes a stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters and requires only a few iterations to converge. New closed-form expressions for the deterministic and stochastic direction estimation Cramer-Rao bounds (CRBs) are derived for the considered nonuniform model. Our expressions can be viewed as an extension of the well-known results by Stoica and Nehorai (1989, 1990) and Weiss and Friedlander (1993) to a more general noise model than the commonly used uniform one. In addition, these expressions extend the results obtained by Matveyev et al. (see Circuits, Syst., Signal Process., vol.18, p.479-87, 1999) to the multiple source case. Comparisons with the above-mentioned earlier results help to discover several interesting properties of DOA estimation in the nonuniform noise case. To compare the estimation performance of the proposed ML technique with the results of our CRB analysis and with the performance of conventional “uniform” ML, simulation results are presented. Additionally, we test our technique using experimental seismic array data. Our simulations and experimental results both validate essential performance improvements achieved by means of the approach proposed