Relevant bibliographies by topics / Space-time adaptive processing (STAP)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Space-time adaptive processing (STAP)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Space-time adaptive processing (STAP)"

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Lv, Wei, Zhi Jie Wang, Jian Chen Li, Ming Zhou Wang, Qiao Hu, and Bao Min Yang. "Space-Time Adaptive Processing Used for Underwater LFM and CW." Applied Mechanics and Materials 313-314 (March 2013): 1229–34. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.1229.

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In order to use the STAP in reverberation suppression, and compare the performances of STAP for underwater LFM with CW, the method of STAP used for LFM was proposed. Firstly, the principle of STAP for CW was analyzed, according to the underwater echo of CW. Then, the space-time steering vector of LFM is deduced by analysing the underwater echo of LFM. Fianlly, the performances of STAP for LFM and CW were compared by simulations. The results show that the proposed method of STAP for underwater LFM with narrower modulation bandwidth can achieve a better performance in target detection and estima
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Ślesicka, Anna, and Adam Kawalec. "Comparison of the performance of adaptive space-time processing against the background of alternative methods." Bulletin of the Military University of Technology 69, no. 2 (2020): 129–47. http://dx.doi.org/10.5604/01.3001.0014.5649.

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Description and successive stages of the STAP algorithm were characterized in this article. The ability to detect an object by using 6-element antenna array without space-time processing and using the STAP technique were compared and shown. The simulation results showed that the implemented STAP algorithm successfully coped with target detection. In addition, the possibilities of object detection using the STAP technique were compared and shown against the background of other DPCA and ADPCA algorithms. Keywords: space-time adaptive processing, STAP, DPCA, ADPCA, radar signal processing, radar
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Zhang, Xinying, Tong Wang, and Degen Wang. "Fast Variational Bayesian Inference for Space-Time Adaptive Processing." Remote Sensing 15, no. 17 (2023): 4334. http://dx.doi.org/10.3390/rs15174334.

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Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a heavy computational burden and slow convergence speed. To improve the convergence speed, the variational Bayesian inference (VBI) is introduced to STAP in this paper. Moreover, to improve computing efficiency, a fast iterative algorithm is derived. By constructing a new atoms selection rule, the dimension of the matrix inverse problem can be s
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Song, Di, Qi Feng, Shengyao Chen, Feng Xi, and Zhong Liu. "Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples." Remote Sensing 14, no. 16 (2022): 3959. http://dx.doi.org/10.3390/rs14163959.

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Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output
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ŚLESICKI, Błażej, Anna ŚLESICKA, and Adam KAWALEC. "IMPROVE THE SAFETY OF AIR TRANSPORT, ESPECIALLY IN MILITARIZED TERRAIN, BY USE OF SIDE LOOKING AIRBORNE RADAR AND SPACE TIME ADAPTIVE PROCESSING." Scientific Journal of Silesian University of Technology. Series Transport 123 (June 30, 2024): 335–46. http://dx.doi.org/10.20858/sjsutst.2024.123.17.

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The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, comma
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Li, Shiyi, Na Wang, Jindong Zhang, Chenyan Xue, and Daiyin Zhu. "Slow-Time Code Design for Space-Time Adaptive Processing in Airborne Radar." Entropy 23, no. 9 (2021): 1169. http://dx.doi.org/10.3390/e23091169.

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Space-time adaptive processing (STAP) techniques have been motivated as a key enabling technology for advanced airborne radar applications. In this paper, a slow-time code design is considered for the STAP technique in airborne radar, and the principle for improving signal-to-clutter and noise ratio (SCNR) based on slow-time coding is given. We present two algorithms for the optimization of transmitted codes under the energy constraint on a predefined area of spatial-frequency and Doppler-frequency plane. The proposed algorithms are constructed based on convex optimization (CVX) and alternatin
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Li, Kun, Jinyang Luo, Peng Li, Guisheng Liao, Zhixiang Huang, and Lixia Yang. "Improved Variational Bayes for Space-Time Adaptive Processing." Entropy 27, no. 3 (2025): 242. https://doi.org/10.3390/e27030242.

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To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and
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Li, Jiyang, Xiaohu Duan, Jia Li, and Peng Bai. "Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against mDT-STAP System." Electronics 12, no. 1 (2022): 152. http://dx.doi.org/10.3390/electronics12010152.

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The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulati
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Pető, Tamás, and Rudolf Seller. "Space-Time Adaptive Cancellation in Passive Radar Systems." International Journal of Antennas and Propagation 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/2467673.

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A critical issue in the realization of passive radar systems is the effective suppression of the zero Doppler interference (ZDI). The performance of the clutter cancellation relies much on the used algorithms. Several state-of-the-art approaches consist in the independent use of spatial and temporal algorithms for ZDI suppression. In this paper, a novel interference cancellation algorithm is proposed, which jointly exploits the available information from both space and time domains. We call this novel method Space-Time Adaptive Cancellation (STAC), and it differs from previous schemes included
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Cui, Weichen, Tong Wang, Degen Wang, and Kun Liu. "An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior." Remote Sensing 14, no. 15 (2022): 3520. http://dx.doi.org/10.3390/rs14153520.

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Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better
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Dissertations / Theses on the topic "Space-time adaptive processing (STAP)"

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Burwell, Alex. "An Expert System Approach to Bistatic Space-Time Adaptive Processing." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1618405886692882.

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Wortham, Cody. "Space-Time Processing for Ground Surveillance Radar." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14468.

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As the size of an adaptive antenna array grows, the system is able to resist interference signals of increasing bandwidth. This is a result of the transmit pattern gain increasing, which raises the target's return power, and a greater number of degrees of freedom. However, once the interference signal decorrelates completely from one channel to the next, increasing array size will cease to improve detection capability. The use of tapped delay-line processing to improve correlation between channels has been studied for smaller arrays with single element antennas, but previous analyses have not
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Moore, Thomas Dean. "Analytic Study of Space-Time and Space-Frequency Adaptive Processing for Radio Frequency Interference Suppression." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1037380239.

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Bryant, Christine Ann. "Multiple-Input Single-Output Synthetic Aperture Radar and Space-Time Adaptive Processing." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276875612.

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Zhou, Zheng N. "Space-time adaptive processing with multi-staged Wiener filter and principal component signal dependent algorithms." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/276.

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Space-time Adaptive Processing (STAP) is a two-dimensional filtering technique for antenna array with multiple spatial channels. The name "space-time" describes the coupling of these spatial channels with pulse-Doppler waveforms. Applications for STAP includes ground moving target indicator (GMTI) for airborne radar systems. Today, there are strong interests to develop STAP algorithms for operations in “sample starved” environments, where intense environmental interference can reduce STAP capacity to detect and track ground targets. Careful applications of STAP can effectively overcome thes
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Anadol, Erman. "A Knowledge Based Approach In Gmti For The Estimation Of The Clutter Covariance Matrix In Space Time Adaptive Processing." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615014/index.pdf.

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Ground Moving Target Indication (GMTI) operation relies on clutter suppression techniques for the detection of slow moving ground targets in the presence of strong radar returns from the ground. Space Time Adaptive Processing (STAP) techniques provide a means to achieve this goal by adaptively forming the clutter suppression filter, whose parameters are obtained using an estimated covariance matrix of the clutter data. Therefore, the performance of the GMTI operation is directly aected by the performance of the estimation process mentioned above. Knowledge based techniques are applicable in ap
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Breloy, Arnaud. "Algorithmes d’estimation et de détection en contexte hétérogène rang faible." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLN021/document.

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Une des finalités du traitement d’antenne est la détection et la localisation de cibles en milieu bruité. Dans la plupart des cas pratiques, comme par exemple le RADAR ou le SONAR actif, il faut estimer dans un premier temps les propriétés statistiques du bruit, et plus précisément sa matrice de covariance ; on dispose à cette fin de données secondaires supposées identiquement distribuées. Dans ce contexte, les hypothèses suivantes sont généralement formulées : bruit gaussien, données secondaires ne contenant que du bruit, et bien sûr matériels fonctionnant parfaitement. Il est toutefois connu
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Oudin, Marc. "Etude d'algorithmes de traitement d'antenne sur signaux large bande et signaux radar bande étroite à antenne tournante." Paris 6, 2008. http://www.theses.fr/2008PA066209.

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Cette thèse est consacrée à l'étude d'algorithmes de filtrage spatial ou spatio-temporel dans deux contextes d'application différents. Dans une première partie de la thèse, nous nous intéressons à des algorithmes de traitement d'antenne sur signaux large bande. Puis, dans une seconde partie, nous considérons le problème du filtrage de signaux radar bande étroite en configuration radar terrestre à antenne tournante. Dans la première partie de ce document, notre contribution a porté principalement sur les points suivants : dans le cadre de l'étude de robustesse de la formation de faisceaux bande
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Yang, Ho. "Partially adaptive space-time processing." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13028.

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Seliktar, Yaron. "Space-time adaptive monopulse processing." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13075.

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Books on the topic "Space-time adaptive processing (STAP)"

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Klemm, Richard. Principles of space-time adaptive processing. Institution of Electrical Engineers, 2002.

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Richard, Klemm, ed. Applications of space-time adaptive processing. Institution of Electrical Engineers, 2004.

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Engineers, Institution of Electrical, ed. Principles of space-time adaptive processing. 3rd ed. Institution of Electrical Engineers, 2006.

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Klemm, Richard. Space-time adaptive processing: Principles and applications. Institution of Electrical Engineers, 1998.

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Lebak, James M. Toward a portable parallel library for space-time adaptive methods. Cornell Theory Center, Cornell University, 1996.

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Rooyen, Pieter Van. Space-time processing for CDMA mobile communications. Kluwer Academic Publishers, 2000.

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Klemm, Richard. Principles of Space-Time Adaptive Processing. Institution of Engineering & Technology, 2011.

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Klemm, Richard. Principles of Space-Time Adaptive Processing. Institution of Engineering and Technology, 2006. http://dx.doi.org/10.1049/pbra021e.

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Klemm, Richard. Applications of Space-Time Adaptive Processing. Institution of Electrical Engineers, 2004.

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Klemm, Richard. Principles of Space-Time Adaptive Processing. 3rd ed. Institution for Engineering and Technology, 2006.

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Book chapters on the topic "Space-time adaptive processing (STAP)"

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Uncini, Aurelio. "Discrete Space-Time Filtering." In Fundamentals of Adaptive Signal Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02807-1_9.

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Affes, Sofiène, and Paul Mermelstein. "Adaptive Space-Time Processing for Wireless CDMA." In Adaptive Signal Processing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11028-7_10.

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Tüzünalp, Önder. "Aeon-Adaptive Time-Space Array Processing." In Adaptive Methods in Underwater Acoustics. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5361-1_63.

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Peng, Yang, Zhixin Deng, Guangwei Fan, and Peilin Chen. "Time Domain Filtering Based Compensation Technique for Space-Time Adaptive Processing." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29193-7_53.

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Gu, Irene Y. H., Vasile Gui, and Zhifei Xu. "Video Segmentation Using Joint Space-Time-Range Adaptive Mean Shift." In Advances in Multimedia Information Processing - PCM 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922162_85.

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Lee, Kyusoon, and Adam W. Bojańczyk. "ALPS: A Software Framework for Parallel Space-Time Adaptive Processing." In Applied Parallel Computing. State of the Art in Scientific Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11558958_50.

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Wang, Jiamou. "Study on Gaussian Priory Model for Space Time Adaptive Processing." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35470-0_12.

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Wicks, Michael C., Muralidhar Rangaswamy, Raviraj S. Adve, and Todd B. Hale. "Space-Time Adaptive Processing for Airborne Radar: A Knowledge-Based Perspective." In Knowledge-Based Radar Detection, Tracking, and Classification. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470283158.ch5.

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Loshakov, Valeriy, Mykola Moskalets, Dmytro Ageyev, Abdnoure Drif, and Konstantyn Sielivanov. "Adaptive Space-Time and Polarisation-Time Signal Processing in Mobile Communication Systems of Next Generations." In Data-Centric Business and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43070-2_21.

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Lu, Duo, Ying Zhang, and Zhang-Xin Chen. "A Modified Subspace Tracking Algorithm for Space-Time Adaptive Processing in Inhomogeneous Environment." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1260-5_37.

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Conference papers on the topic "Space-time adaptive processing (STAP)"

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Jiang, Jianrong, Haoyan Xie, Hanbo Chen, and Haigui Xie. "Feature selection enhanced sparse recovery space-time adaptive processing." In 2025 6th International Conference on Electrical, Electronic Information and Communication Engineering (EEICE). IEEE, 2025. https://doi.org/10.1109/eeice65049.2025.11034292.

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Farina, A., P. Lombardo, and M. Pirri. "Non-linear STAP processing." In IEE Colloquium on Space-Time Adaptive Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980242.

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Farina, A., and L. Timmoneri. "Real-time STAP techniques." In IEE Colloquium on Space-Time Adaptive Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980241.

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Pun, Man-On, Zafer Sahinoglu, Sagar Shah, Yoshihisa Hara, and Pu Wang. "Two-Step Low-Complexity Space-Time Adaptive Processing (STAP)." In GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5683771.

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Wright, P. J., and M. Wells. "STAP for airborne target detection using a space based phased array radar." In IEE Colloquium on Space-Time Adaptive Processing. IEE, 1998. http://dx.doi.org/10.1049/ic:19980246.

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Benson, Thomas M., Ryan K. Hersey, and Edwin Culpepper. "GPU-based space-time adaptive processing (STAP) for radar." In 2013 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2013. http://dx.doi.org/10.1109/hpec.2013.6670341.

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Fertig, Louis. "Analytical approximations for Space-Time Adaptive Processing (STAP) performance." In 2014 IEEE Radar Conference (RadarCon). IEEE, 2014. http://dx.doi.org/10.1109/radar.2014.6875568.

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Sarkar, T. K. "A pragmatic approach to adaptive antennas and space-time adaptive processing (STAP)." In Proceedings of ISAPE 2000: Fifth International Symposium on Antennas, Propagation, and EM Theory. IEEE, 2000. http://dx.doi.org/10.1109/isape.2000.894853.

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Shen, Mingwei, Jia Yu, Di Wu, and Daiyin Zhu. "Space-time adaptive monopulse for RD-STAP parameters estimation." In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015. http://dx.doi.org/10.1109/chinasip.2015.7230408.

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Beau, Sophie, and Sylvie Marcos. "Range-recursive space-time adaptive processing (STAP) with a circular airborne radar antenna." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4720879.

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Reports on the topic "Space-time adaptive processing (STAP)"

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Schuman, Harvey K., and Ping Li. Space-Time Adaptive Processing (STAP) for Low Sample Support Applications. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada423770.

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Rangaswamy, Muralidhar. Space-Time Adaptive Processing (STAP) Performance in Non-Homogeneous Radar Clutter. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada398137.

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Himed, Braham. Effects of Antenna Mutual Coupling in Space-Time Adaptive Processing (STAP). Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada358691.

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Goldstein, J. S., and Irving S. Reed. Adaptive CFAR Detection and Reduced-Rank Space-Time Adaptive Processing. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada323841.

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Bojanozyk, Adam W. Space-Time Adaptive Processing on Commercial High-Performance Computers. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada382569.

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Salama, Yassir, and Roy Senn. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 6: Knowledge-Based Space-Time Adaptive Processing (KBSTAP) User's Manual and Programmer's Manual. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada388892.

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Salama, Yassar, and Roy Senn. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 1: Summary. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada388939.

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Morgan, Charles, and Lee Moyer. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 4: Knowledge-Based Tracking. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389090.

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Schuman, Harvey. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 3: Radar Filtering Rule Book. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada388865.

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Morgan, Charles, and Lee Moyer. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 5: Knowledge-Based Tracker Rule Book. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada388902.

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