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Yongxu Liu 0002

dblp:67/9559-2 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 4

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Wireless sensing and localization · 56% Physical-layer communications · 44%
Theoretical computer science
2 papers
Algorithms and data structures · 50% Mathematical optimization · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing
space-time adaptive processing
0.212015
Fast inverse covariance matrix computation based on element-order recursive method for space-time adaptive processing · Sci. China Inf. Sci. 2015
Physical-layer communications › signal processing for communications › array signal processing
space-time adaptive processing
0.222013
Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing · Sci. China Inf. Sci. 2013
Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing · Sci. China Inf. Sci. 2013
Wireless sensing and localization › radar signal processing
clutter suppression
0.212013
Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing · Sci. China Inf. Sci. 2013
Wireless sensing and localization
radar signal processing
0.212013
Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing · Sci. China Inf. Sci. 2013
Mathematical optimization › statistical estimation › covariance estimation
inverse covariance estimation
0.212013
Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing · Sci. China Inf. Sci. 2013
Algorithms and data structures
numerical linear algebra
0.212013
Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing · Sci. China Inf. Sci. 2013
Physical-layer communications › signal processing for communications › statistical signal processing › estimation theory
covariance matrix estimation
0.012013
Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing · Sci. China Inf. Sci. 2013

Methods — techniques the papers use, named apart from their topics

element-order recursive method · 0.4pulse-order recursive method · 0.3
YearPublicationVenuePosition
2015 Fast inverse covariance matrix computation based on element-order recursive method for space-time adaptive processing
Xiaopeng Yang 0002, Yuze Sun 0002, Yongxu Liu 0002, Tao Zeng 0001, Teng Long 0001
Sci. China Inf. Sci.3
2013 Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing
Teng Long 0001, Yongxu Liu 0002, Xiaopeng Yang 0002, Yuze Sun 0002
Sci. China Inf. Sci.2
2013 Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing
Xiaopeng Yang 0002, Yongxu Liu 0002, Teng Long 0001
Sci. China Inf. Sci.2
2012 Geometry-aided subspace projection for mitigating range-dependence of the clutter spectrum in forward-looking airborne radar
abstract
The clutter suppression of space-time adaptive processing (STAP) will degrade severely in forward-looking airborne radar if the range-dependence of clutter spectrum is not compensated correctly. In this paper, a new method for mitigating the range-dependence of the clutter spectrum based on the geometry-aided subspace projection is proposed. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then the clutter subspace projection matrix is obtained by decomposing the corresponding covariance matrix. In the following, the mitigation of the range-dependence is accomplished by applying this clutter subspace projection matrix to transform the secondary range samples to obtain an approximate clutter subspace with the range under test. The simulation results show that the proposed method can mitigate the range-dependence significantly.
Xiaopeng Yang 0002, Yongxu Liu 0002, Teng Long 0001
IGARSS2