.png){kind=icon}
.png){kind=icon}
Return
Direct Localization of Multiple Noncircular Sources With a Moving Nested Array
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2929805.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a subspace data fusion (SDF)-based direct positioning determination (DPD) method for noncircular sources with a moving nested array (NA). The DPD algorithms using a uniform linear array (ULA) and coprime (CP) array are available in the existing literature. Sparse arrays have larger apertures than ULAs, which enhances degrees of freedom (DOFs). However, the existing sparse CP arrays have limited DOF and also have poor detecting ability. In this paper, the physical sensors of NA are rearranged to obtain a difference sum NA (DSNA) and second-order super NA (II-SNA). The rearranged NA sensors are seen to increase array aperture. The noncircular characteristics of the sources are also used to improve accuracy in positioning. To obtain high DOF in NA, DSNA, and II-SNA, the covariance matrices are vectorized. The coherency of the virtual array is resolved by applying the spatial smoothing technique. Finally, the SDF-based DPD is used to establish the cost function and the target is localized. The simulation results are provided for the NA, DSNA, and II-SNA and are compared with the existing CP array DPD algorithm. The results show that the proposed method shows significant enhancement in localization accuracy. The Cramer-Rao lower bound (CRLB), complexity, and performance comparisons are also described in this paper.
Keywords:
Difference and sum nested array
direct position determination
nested array
second order super nested array
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
