Abstract
In recent years, direct position determination (DPD) with multiple arrays for non-circular (NC) signals is a hot topic to research. Conventional DPD techniques with spectral peak search methods have high computational complexity and are sensitive to the locations of the observation stations. Besides, there will be loss when the signal propagates in the air, which leads to different received signal-to-noise ratios (SNRs) for each observation station. To attack the problems mentioned above, this paper derives direct position determination of non-circular sources for multiple arrays via weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) data fusion (NC-Euler-WESPRIT) method. Firstly, elliptic covariance information of NC signals and Euler transformation are used to extend the received signal. Secondly, ESPRIT is applied to avoid the high-dimensional spectral function search problem of each observation station. Then, we combine the information of all observation stations to construct a spectral function without complex multiplication to reduce the computational complexity. Finally, the data of each observation station is weighted to compensate for the projection error. The consequence of simulation indicates that the proposed NC-Euler-WESPRIT algorithm not only improves the estimation performance, but also greatly reduces the computational complexity compared with subspace data fusion (SDF) technology and NC-ESPRIT algorithm.
Highlights
Original two-step location technology needs to estimate the intermediate parameters from the original data, such as time of arrival (TOA) [9], time difference of arrival (TDOA) [10], angle of arrival (AOA) [11]
As signal-to-noise ratios (SNRs) of received signals at each observation station are different, this paper introduces the principle of power allocation [30] and derives weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) direct position determination (DPD) algorithm for NC sources (NC-Euler-WESPRIT)
This paper introduces the idea of Euler-ESPRIT and constructs a spectral function without complex multiplication
Summary
As an important research area about signal processing, passive location has attracted extensive attention recently. Current DPD algorithms of NC signals need to fuse the received data of each observation station for high-dimensional spectral function search, which undoubtedly has a great computational complexity. As the existing algorithms do not consider the effect of this difference, they are more sensitive to the locations of the observation stations, resulting in insufficient estimation accuracy. As SNRs of received signals at each observation station are different, this paper introduces the principle of power allocation [30] and derives weighted Euler ESPRIT DPD algorithm for NC sources (NC-Euler-WESPRIT). The consequence of simulation shows that the proposed NC-EulerWESPRIT algorithm improves the estimation performance, and greatly reduces the computational complexity compared with SDF technology and NC-ESPRIT algorithm. (1) The proposed NC-Euler-WESPRIT DPD algorithm takes full advantage of elliptic covariance information of NC signals to expand the virtual array aperture. Sci. 2022, 12, 2503 array; ⊗ denotes Kronecker product; ∂(a)/∂(b) stands for the derivative of a with respect to b; (·)+ and (·)−1 denote general Moore-Penrose inverse and the inverse of the matrix
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