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Assessing CREMAs' Capacity to Govern Landscape Resources in the Western Wildlife Corridor of Northern Ghana. [PDF]

Environ Manage
Bayala ERC, Zida M, Asubonteng KO, Ros-Tonen MAF, Reed J, Siangulube FS, Ickowitz A, Djoudi H, Sunderland T.   +8 more
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A deterministic least-squares approach to space-time adaptive processing (STAP)

IEEE Transactions on Antennas and Propagation, 2001
A direct data domain (D/sup 3/) least-squares space-time adaptive processing (STAP) approach is presented for adaptively enhancing signals in a nonhomogeneous environment. The nonhomogeneous environment may consist of nonstationary clutter and could include blinking jammers.
Sarkar, Tapan Kumar, Wang, Hong, Park, Sheeyun, Adve, Raviraj, Koh, Jinwan, Kim, Kyungjung, Zhang, Yuhong, Wicks, Michael C., Brown, Russell D.   +8 more
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Analytical expressions for space-time adaptive processing (STAP) performance

IEEE Transactions on Aerospace and Electronic Systems, 2015
Space-time adaptive processing (STAP) is a family of algorithms frequently employed in surface moving target indication radar systems to enable detection of moving objects in the presence of fixed (i.e., nonmoving) clutter. In this paper, two new closed-form expressions that quantify the loss associated with the STAP notch centered on clutter are ...
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Framework and Taxonomy for Radar Space-Time Adaptive Processing (STAP) Methods

IEEE Transactions on Aerospace and Electronic Systems, 2007
The goal of radar space-time adaptive processing (STAP) is to detect slow moving targets from a moving platform, typically airborne or spaceborne. STAP generally requires the estimation and the inversion of an interference-plus-noise (I+N) covariance matrix.
Sebastien Greve, Philippe Ries, Fabian Lapierre, Jacques Verly   +3 more
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Space-time adaptive processing (STAP) with limited sample support

Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509), 2004
A particularly active area of research in space-time adaptive processing (STAP) involves scenarios in which the sample support available for training the adaptive processor is limited. Several of these scenarios are of significant current interest.
null Ping Li, H. Schuman, J.H. Micheis, B. Himed   +3 more
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GPU-based space-time adaptive processing (STAP) for radar

2013 IEEE High Performance Extreme Computing Conference (HPEC), 2013
Space-time adaptive processing (STAP) utilizes a two-dimensional adaptive filter to detect targets within a radar data set with speeds similar to the background clutter. While adaptively optimal solutions exist, they are prohibitively computationally intensive.
Thomas M. Benson, Ryan K. Hersey, Edwin Culpepper   +2 more
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Benefits of space–time adaptive processing (STAP) in bistatic airborne radar

IEE Proceedings - Radar, Sonar and Navigation, 2003
The paper investigates the merits of using STAP for clutter suppression in bistatic airborne radar. Particular emphasis is placed on the clutter angle-Doppler inter-relationships in bistatic geometries, and it is demonstrated that there can be ground regions where small changes in arrival angle imply large changes in the Doppler shift.
G.M. Herbert, P.G. Richardson
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A new approach to wideband space-time adaptive processing (W-STAP)

Processing Workshop Proceedings, 2004 Sensor Array and Multichannel Signal, 2005
This paper presents a new methodology for airborne wideband space-time adaptive processing (W-STAP) radar systems. In W-STAP, the wideband target signal is first decomposed into a series of narrowband signals. This is referred to as a sub-banding process.
null BrahamHimed, null Kyungiung Kim, null YuhongZhang, A. Hajjari   +3 more
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Performance modeling and optimization framework for space-time adaptive processing (STAP)

18th International Parallel and Distributed Processing Symposium, 2004. Proceedings., 2004
Summary form only given. Fully-adaptive space-time adaptive processing (STAP) is an optimal but impractical radar processing algorithm due to the overwhelming number of operations required. Hence, many different heuristic approaches are sought to approximate the optimal method with fewer number of operations.
null Kyusoon Lee, A.W. Bojanczyk
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