Abstract
Non-Gaussian impulsive noise widely exists in the real world, this paper takes the α-stable distribution as the mathematical model of non-Gaussian impulsive noise and works on the joint direction-of-arrival (DOA) and range estimation problem of near-field signals in impulsive noise environment. Since the conventional algorithms based on the classical second order correlation statistics degenerate severely in the impulsive noise environment, this paper adopts two robust correlations, the fractional lower order correlation (FLOC) and the nonlinear transform correlation (NTC), and presents two related near-field localization algorithms. In our proposed algorithms, by exploring the symmetrical characteristic of the array, we construct the robust far-field approximate correlation vector in relation with the DOA only, which allows for bearing estimation based on the sparse reconstruction. With the estimated bearing, the range can consequently be obtained by the sparse reconstruction of the output of a virtual array. The proposed algorithms have the merits of good noise suppression ability, and their effectiveness is demonstrated by the computer simulation results.
Highlights
The source localization problem in array signal processing is an important problem which has a wide range of applications
We studied the near-field localization problem based on sparse reconstruction of the constructed far-field approximate fractional lower order correlation (FLOC) vector by exploring the symmetrical characteristic of the array under impulse noise environment for the first time in [34]
To avoid estimating the characteristic exponent α of the impulsive noise before using the FLOC statistics, we define a robust correlation vector, nonlinear transform correlation (NTC) vector, which is just like the FLOC vector that is in relation with the DOA only, the bearings can be estimated based on the sparse reconstruction of the FLOC vector or NTC vector
Summary
The source localization problem in array signal processing is an important problem which has a wide range of applications. With the successful application of sparse signal reconstruction in far-field DOA estimation [17] and the excellent performance of this kind of algorithm in anti-noise ability and snapshot number, more and more scholars carry out the research on sparse reconstruction based near-field localization methods. We studied the near-field localization problem based on sparse reconstruction of the constructed far-field approximate FLOC vector by exploring the symmetrical characteristic of the array under impulse noise environment for the first time in [34]. To avoid estimating the characteristic exponent α of the impulsive noise before using the FLOC statistics, we define a robust correlation vector, NTC vector, which is just like the FLOC vector that is in relation with the DOA only, the bearings can be estimated based on the sparse reconstruction of the FLOC vector or NTC vector. A very important characteristic of the α-stable distribution is that it does not have a finite two order statistics and higher order statistics
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