| 1. |
- Zhang, Yongchao, et al.
(författare)
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Sparse source location for real aperture radar using generalized sparse covariance fitting
- 2017
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Ingår i: 2017 IEEE Radar Conference, RadarConf 2017. - 9781467388238 ; , s. 1069-1074
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Konferensbidrag (refereegranskat)abstract
- Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.
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| 2. |
- Zhang, Yongchao, et al.
(författare)
-
Range-Recursive IAA for Scanning RadarAngular Super-Resolution
- 2017
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1545-598X. ; 14:10, s. 1675-1679
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.
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