Knowledge aided sparse
WebMar 28, 2016 · In this paper, novel knowledge-aided space-time adaptive processing (KA-STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the …
Knowledge aided sparse
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WebApr 19, 2015 · Novel support knowledge-aided multiple sparse Bayesian learning (SA-M-SBL) algorithm is introduced, which incorporates the prior information into a three-layer … WebSep 25, 2024 · This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries.
WebNov 1, 2024 · In order to improve the performance of conformal array clutter suppression, a knowledge aided method based on semiparametric/sparse iterative covariance-based estimation STAP, named with KASPICE-STAP, is proposed here. The KASPICE-STAP method requires the knowledge of the clutter ridge spread of the testing range cell. WebOct 19, 2014 · Abstract and Figures It has been shown both experimentally and theoretically that sparse signal recovery can be significantly improved given that part of the signal's …
WebDec 1, 2024 · To cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of … WebAug 20, 2024 · Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively …
WebTo cope with this problem, a novel robust STAP algorithm based on knowledge-aided sparse recovery (SR) is proposed, which can eliminate the influence of dense outliers on …
WebOct 27, 2024 · Jun Fang, Yanning Shen, Fuwei Li, and Hongbin Li, "Prior support knowledge-aided sparse Bayesian learning with partly erroneous support information", Technical report. (pdf) (Matlab codes) Journal Articles 2024 jdsjinji j-d-sys.comWebSep 25, 2024 · This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present … jdsjfWebMay 17, 2024 · The use of knowledge-aided covariance is considered for processing underwater acoustic array data in snapshot-deficient scenarios. The knowledge-aided formalism is a technique that combines array data with a known covariance to produce an invertible estimate. jds jeuWebJun 22, 2024 · Sparse Bayesian learning has recently become successful in many compressed sensing problems. However, their performance critically relies on the … jdsjisWebApr 14, 2024 · A motivation example of our knowledge graph completion model on sparse entities. Considering a sparse entity , the semantics of this entity is difficult to be modeled by traditional methods due to the data scarcity.While in our method, the entity is split into multiple fine-grained components (such as and ).Thus the semantics of these fine-grained … l83 main bearingsWebApr 12, 2024 · Learning Transferable Spatiotemporal Representations from Natural Script Knowledge Ziyun Zeng · Yuying Ge · Xihui Liu · Bin Chen · Ping Luo · Shu-Tao Xia · Yixiao … jdsjsdfWebDec 29, 2024 · This study details the development of a lightweight and high performance model, targeting real-time object detection. Several designed features were integrated into the proposed framework to accomplish a light weight, rapid execution, and optimal performance in object detection. Foremost, a sparse and lightweight structure was … jdsjds