Abstract
For the near-field localization of non-circular distributed signals with spacial probability density functions (PDF), a novel algorithm is proposed in this paper. The traditional algorithms dealing with the distributed source are only for the far-field sources, and they need two-dimensional (2D) search or omit the angular spread parameter. As a result, these algorithms are no longer inapplicable for near-filed localization. Hence the near-filed sources that obey a classical probability distribution are studied and the corresponding specific expressions are given, providing merits for the near-field signal localization. Additionally, non-circularity of the incident signal is taken into account in order to improve the estimation accuracy. For the steering vector of spatially distributed signals, we first give an approximate expression in a non-integral form, and it provides the possibility of separating the parameters to be estimated from the spatially discrete parameters of the signal. Next, based on the rank-reduced (RARE) algorithm, direction of arrival (DOA) and range can be obtained through two one-dimensional (1-D) searches separately, and thus the computational complexity of the proposed algorithm is reduced significantly, and improvements to estimation accuracy and identifiability are achieved, compared with other existing algorithms. Finally, the effectiveness of the algorithm is verified by simulation.
Highlights
Source localization is an important branch in the field of array signal processing, and significant achievements have been made in this field in recent decades [1,2]
The traditional direction of arrival (DOA) estimation algorithms for far-field signals are no longer suitable for near-field sources [6]; many scholars have proposed parameter estimation algorithms for near-field models in recent years, most of which are based on higher-order statistics (HOS) [7,8]
Assume near-field signals are received by a uniform linear array (ULA) with a number of array elements of N = 7 (M = 3), where the spacing element d is a quarter of the wavelength of the received signal
Summary
Source localization is an important branch in the field of array signal processing, and significant achievements have been made in this field in recent decades [1,2]. The traditional DOA estimation algorithms for far-field signals are no longer suitable for near-field sources [6]; many scholars have proposed parameter estimation algorithms for near-field models in recent years, most of which are based on higher-order statistics (HOS) [7,8]. These algorithms can mitigate Gaussian colored noise well [7], at the cost of a substantial increase in calculations. It still needs two-dimensional spectral peak searches, and the error caused by Schmidt’s orthogonalization therein will seriously affect the range estimation
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