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| Citation: | ZOU Kun, LAI Lei, LUO Yanbo, et al. Suppression of non-Gaussian clutter from subspace interference[J]. Journal of Radars, 2020, 9(4): 715–722. doi: 10.12000/JR19050
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Suppression of Non-Gaussian Clutter from Subspace Interference
doi: 10.12000/JR19050
More Information-
Abstract
In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance. -
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References
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- Figure 1. Interference identification and clutter suppression with η=17.5% using simulated data
- Figure 2. Interference identification and clutter suppression with η=40.0% using simulated data
- Figure 3. Interference identification and clutter suppression with η=77.5% using simulated data
- Figure 4. Interference identification and clutter suppression with η=40.0% using IPIX dataset (30 m resolution)
- Figure 5. Interference identification and clutter suppression with η=40.0% using IPIX dataset (15 m resolution)