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

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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3

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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4

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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8

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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9

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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11

Fertig, Louis. "Analytical expressions for space-time adaptive processing (STAP) performance." IEEE Transactions on Aerospace and Electronic Systems 51, no. 1 (2015): 42–53. http://dx.doi.org/10.1109/taes.2014.130676.

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Guo, Yijia, Jun Geng, Xun Zhang, and Haiyu Dong. "Sea Clutter Suppression for Shipborne DRM-Based Passive Radar via Carrier Domain STAP." Remote Sensing 17, no. 12 (2025): 1985. https://doi.org/10.3390/rs17121985.

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This paper proposes a new carrier domain approach to suppress spreading first-order sea clutter in shipborne passive radar systems using Digital Radio Mondiale (DRM) signals as illuminators. The DRM signal is a broadcast signal that operates in the high-frequency (HF) band and employs orthogonal frequency-division multiplexing (OFDM) modulation. In shipborne DRM-based passive radar, sea clutter sidelobes elevate the noise level of the clutter-plus-noise covariance matrix, thereby degrading the target signal-to-interference-plus-noise ratio (SINR) in traditional space–time adaptive processing (

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Liu, Kun, Tong Wang, Jianxin Wu, and Jinming Chen. "A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors." Sensors 22, no. 1 (2021): 77. http://dx.doi.org/10.3390/s22010077.

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In the presence of unknown array errors, sparse recovery based space-time adaptive processing (SR-STAP) methods usually directly use the ideal spatial steering vectors without array errors to construct the space-time dictionary; thus, the steering vector mismatch between the dictionary and clutter data will cause a severe performance degradation of SR-STAP methods. To solve this problem, in this paper, we propose a two-stage SR-STAP method for suppressing nonhomogeneous clutter in the presence of arbitrary array errors. In the first stage, utilizing the spatial-temporal coupling property of th

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Meng, Zhen, and Feng Shen. "Robust Space-Time Adaptive Processing Method for GNSS Receivers in Coherent Signal Environments." Remote Sensing 15, no. 17 (2023): 4212. http://dx.doi.org/10.3390/rs15174212.

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In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately est

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15

ŚLESICKI, Błażej, Adam KAWALEC, and Anna ŚLESICKA. "The Study of the Possibility of Applying Parallel Programming to the Algorithms of Space-Time Adaptive Processing." Problems of Mechatronics Armament Aviation Safety Engineering 13, no. 3 (2022): 27–42. http://dx.doi.org/10.5604/01.3001.0016.0048.

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The article presents the description, assumptions and subsequent steps of the space-time adaptive processing (STAP) algorithms used as a signal processing tool in radars. The possibilities of object detection using the Sample Matrix Inversion (SMI) and Data Domain Least Squares (DDLS) algorithms were compared and showned. The article shows the impact of the use of parallel programming on the computation time of both algorithms. The main aim of this study was to propose an efficient method for the real-time implementation of the STAP algorithm in airborne radar systems. The idea of using parall

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Ślesicka, Anna, and Adam Kawalec. "An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing." Sensors 20, no. 12 (2020): 3468. http://dx.doi.org/10.3390/s20123468.

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The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In additi

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17

Wang, Wei, Lin Zou, and Xuegang Wang. "A Novel Two-Level Nested STAP Strategy for Clutter Suppression in Airborne Radar." Mathematical Problems in Engineering 2019 (June 4, 2019): 1–16. http://dx.doi.org/10.1155/2019/2540858.

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Nested arrays have been studied recently in array signal processing field because of their closed-form expressions for the sensor locations and achievable degrees of freedom (DOFs). In this paper, the concept of nesting is further extended to space-time adaptive processing (STAP). Different from the traditional uniform-STAP method that calculates the clutter plus noise covariance matrix (CNCM) and performs the STAP filter direct using the data snapshots collected from the uniform linear array (ULA) and the transmitting pulses with uniform pulse repetition interval (PRI), we present a new optim

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18

Zhang, Shuguang, Tong Wang, Cheng Liu, and Degen Wang. "A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar." Sensors 22, no. 15 (2022): 5479. http://dx.doi.org/10.3390/s22155479.

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Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This pa

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19

Guo, Yiduo, Jian Gong, and Yu Xiao. "Local Degree of Freedom of Clutter for Reduced-Dimension Space-Time Adaptive Processing with MIMO Radar." International Journal of Antennas and Propagation 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/6026251.

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Degree of freedom (DOF) of clutter in the reduced-dimension (RD) domain, which is called local DOF (LDOF), is of great importance for RD MIMO-STAP (space-time adaptive processing for multiple-input multiple-output radar) algorithms. In this paper, the LDOF equivalence of different RD MIMO-STAP algorithms are firstly proved, and then a generalized LDOF estimation rule under different conditions is developed to estimate the clutter LDOF for MIMO radar effectively. The accuracy of the proposed rule is verified, and how to design RD MIMO-STAP processors under the guidance of the proposed rule is p

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20

Ren, Bing, and Tong Wang. "Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar." Sensors 22, no. 18 (2022): 6917. http://dx.doi.org/10.3390/s22186917.

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Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The prop

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21

Li, Jiaming, Qiang Yang, Xin Zhang, Xiaowei Ji, and Dezhu Xiao. "Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar." Remote Sensing 14, no. 12 (2022): 2935. http://dx.doi.org/10.3390/rs14122935.

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In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam resha

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Gao, Zhiqi, Wei Deng, Pingping Huang, Wei Xu, and Weixian Tan. "Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction." Electronics 13, no. 11 (2024): 2187. http://dx.doi.org/10.3390/electronics13112187.

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Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the co

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Shen, Shijian, Lan Tang, Xin Nie, Yechao Bai, Xinggan Zhang, and Pin Li. "Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar." Applied Sciences 10, no. 10 (2020): 3609. http://dx.doi.org/10.3390/app10103609.

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This paper proposes two modified space time adaptive processing (STAP) methods based on piecewise sub-apertures and data constraints for non-stationary interference cancellation in synthetic aperture radar (SAR) applications. In these methods, the entire synthetic aperture time is divided into several sub-apertures so that the interference can be considered as stationary in each sub-aperture. At the same time, the consistency of the echo phase in the slow time domain is preserved by the data constraint to ensure the null depth of the antenna pattern for non-stationary interference cancellation

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Wei, Hong Kai, Ping Bo Wang, Jing Xiang Yin, and Chun Lei Zhang. "Research on the Performance of Sub-Band STAP for Wideband Interference Suppression." Advanced Materials Research 756-759 (September 2013): 1724–28. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1724.

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Sub-band space-time adaptive processing (STAP), which decomposes the wideband signal using sub-band filter bank, is the common intuitive way to suppress wideband interference. Based on studying angel-frequency distribution characteristics of wideband, sub-band interference and echo signal in space-time plane, the paper gives the physical significance of sub-band interference and echo signals space-time distribution and thus reveals theoretically the fundamental reason for the poor performance of sub-band STAP. Furthermore, the paper outlines the novel way for wideband linear frequency modulati

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Zhang, Chenxi, Huiliang Zhao, Wenchao Chen, et al. "Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder." Remote Sensing 14, no. 15 (2022): 3800. http://dx.doi.org/10.3390/rs14153800.

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Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (M

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Kou, Siwei, Xi'an Feng, Hui Huang, and Yang Bi. "A Space-Time Adaptive Processing Method Based on Sparse Reconstruction of Reverberation Interference." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 6 (2020): 1179–87. http://dx.doi.org/10.1051/jnwpu/20203861179.

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Aiming at the problem of how to obtain reverberation samples and estimate their covariance matrix in the space-time adaptive processing(STAP) of sonar system, a new space-time adaptive processing method is proposed based on sparse reconstruction of reverberation in this paper. Firstly, according to the space-time distribution characteristics of reverberation received by moving platform sonar, a space-time steering dictionary for sparse reconstruction of reverberation is designed along the relation curve between Doppler frequency shift and incident cone angle cosine of the reverberation unit. T

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Tang, Bin, Xiaoxia Zheng, Mingxin Liu, and Mengxu Fang. "STAP Optimization of Airborne Phased Array Radar in Nonuniform Environment Based on EFA Algorithm." Mathematical Problems in Engineering 2020 (August 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/3943041.

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EFA (extended factored approach) algorithm is the main method of space-time adaptive processing technology (STAP) for airborne phased array radar, but it is faced with many problems, such as large number of samples and large amount of calculation. Therefore, this paper uses a method of spatial data dimensionality reduction processing based on cyclic iterative calculation to optimize its STAP. The final experimental results show that, after spatial data dimensionality reduction processing optimization, the STAP performance of EFA algorithm is further expanded in the range of sample number adapt

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Liu, Cheng, Tong Wang, Kun Liu, and Xinying Zhang. "A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects." Remote Sensing 14, no. 16 (2022): 3906. http://dx.doi.org/10.3390/rs14163906.

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Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an eff

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Tsakalides, P., and C. L. Nikias. "Robust space–time adaptive processing (STAP) in non-Gaussian clutter environments." IEE Proceedings - Radar, Sonar and Navigation 146, no. 2 (1999): 84. http://dx.doi.org/10.1049/ip-rsn:19990233.

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Herbert, G. M., and P. G. Richardson. "Benefits of space–time adaptive processing (STAP) in bistatic airborne radar." IEE Proceedings - Radar, Sonar and Navigation 150, no. 1 (2003): 13. http://dx.doi.org/10.1049/ip-rsn:20030078.

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Tapan Kumar Sarkar, Hong Wang, Sheeyun Park, et al. "A deterministic least-squares approach to space-time adaptive processing (STAP)." IEEE Transactions on Antennas and Propagation 49, no. 1 (2001): 91–103. http://dx.doi.org/10.1109/8.910535.

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Greve, Sebastien, Philippe Ries, Fabian Lapierre, and Jacques Verly. "Framework and Taxonomy for Radar Space-Time Adaptive Processing (STAP) Methods." IEEE Transactions on Aerospace and Electronic Systems 43, no. 3 (2007): 1084–99. http://dx.doi.org/10.1109/taes.2007.4383596.

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Yang, Zhaocheng, Rui Fa, Yuliang Qin, Xiang Li, and Hongqiang Wang. "Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/171808.

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We propose a novel direct data domain (D3) sparsity-based space-time adaptive processing (STAP) algorithm utilizing subaperture smoothing techniques for airborne radar applications. Different from either normal sparsity-based STAP or D3 sparsity-based STAP, the proposed algorithm firstly uses only the snapshot in the cell under test (CUT) to generate multiple subsnapshots by exploiting the space-time structure of the steering vector and the uncorrelated nature of the components of the interference covariance matrix. Since the interference spectrum is sparse in the whole angle-Doppler plane, by

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He, Fei, and Dong Chu Jiang. "Short-Range Clutter Suppression for Non-Side Looking Airborne Radar by Elevation Constraint Adaptive Processing." Applied Mechanics and Materials 543-547 (March 2014): 2414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2414.

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Space-time adaptive processing (STAP) can obtain excellent clutter suppression performance based on the accurate estimate of clutter plus noise covariance matrix which is obtained by enough identical independent distributed samples. However, the short-range clutter of non-side looking airborne radar (non-SLAR) occurs range dependence. As a result, the clutter plus noise covariance matrix can not be estimated accurately and thus the clutter suppressing performance by STAP declines greatly. In this paper, an elevation constraint adaptive processing method is proposed. This method obtains the ele

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Zhongping, HUANG, LEI Zhiyong, HE Zishu, LI Huiyong, ZHANG Liang, and SHEN Haoliang. "Polarization space time adaptive processing based on maximum likelihood polarization vector estimation." Journal of Physics: Conference Series 2235, no. 1 (2022): 012105. http://dx.doi.org/10.1088/1742-6596/2235/1/012105.

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Abstract In airborne radar, signal to clutter plus noise ratio (SCNR) can be improved by utilizing polarization space time adaptive processing (PSTAP). The optimal output SCNR is obtained when the expected polarization-space-time (PST) vector matches the true value. The problem of polarization parameters estimation limits the potential application of PSTAP. The inaccuracy of polarization parameters estimation unevitably causes SCNR loss. In this paper, the effect of expected polarization vector on output SCNR is analyzed on the basis of PSTAP theory. In order to reduce SCNR loss introduced by

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Huang, Long, Zukun Lu, Zhibin Xiao, Chao Ren, Jie Song, and Baiyu Li. "Suppression of Jammer Multipath in GNSS Antenna Array Receiver." Remote Sensing 14, no. 2 (2022): 350. http://dx.doi.org/10.3390/rs14020350.

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Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Int

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Lu, Rongwei, Yifeng Wu, Lei Zhang, and Ziyi Chen. "Cascade Clutter Suppression Method for Airborne Frequency Diversity Array Radar Based on Elevation Oblique Subspace Projection and Azimuth-Doppler Space-Time Adaptive Processing." Remote Sensing 16, no. 17 (2024): 3198. http://dx.doi.org/10.3390/rs16173198.

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Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the rela

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Wu, Youai, Bo Jiu, Wenqiang Pu, Hao Zheng, Kang Li, and Hongwei Liu. "Clutter-Sensing-Driven Space-Time Adaptive Processing Approach for Airborne Sub-Array-Level Digital Array." Remote Sensing 16, no. 23 (2024): 4401. http://dx.doi.org/10.3390/rs16234401.

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Sub-array-level digital arrays effectively diminish the computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive applications in many airborne platforms. Nonetheless, airborne sub-array-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing-driven STAP approach for airborne sub-array-level digital arrays is proposed in this paper. Firstly, we derive a signal model of sub-array-level clutter sensing

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Yang, Zhaocheng, Rodrigo C. de Lamare, Xiang Li, and Hongqiang Wang. "Knowledge-Aided STAP Using Low Rank and Geometry Properties." International Journal of Antennas and Propagation 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/196507.

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This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the clutter subspace that is only determined by the space-time steering vectors, by employing the Gram-Schmidt orthogonalization approach to compute the clutter subspace. Simulation results illustrate the effectiveness of our proposed algorithms.

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Khan, Muhammad Bilal, Ahmed Hussain, Umar Anjum, Channa Babar Ali, and Xiaodong Yang. "Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar." Electronics 9, no. 11 (2020): 1896. http://dx.doi.org/10.3390/electronics9111896.

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In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filte

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He, Tao, Guangjun Sun, and Shujuan Ding. "Main lobe interference suppression method of Skywave OTHR based on fast-time STAP." Journal of Physics: Conference Series 2414, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2414/1/012012.

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Abstract For the sky-wave over-the-horizon radar (OTHR), how to effectively suppress the radio frequency interference (RFI)from the main-lobe direction is a problem. To solve this problem, this paper proposes a new space-fast time adaptive processing (fast-time STAP) which merges the matched filter coefficients used for pulse compression and the time steering vector of fast-time STAP. To compare with conventional methods, the method improves the match between the space-time steering vector of fast-time STAP and the real target signal, while the RFI and the noise from RFI are effectively suppre

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Zou, Bo, Weike Feng, and Hangui Zhu. "Airborne Radar STAP Method Based on Deep Unfolding and Convolutional Neural Networks." Electronics 12, no. 14 (2023): 3140. http://dx.doi.org/10.3390/electronics12143140.

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The lack of independent and identically distributed (IID) training range cells is one of the key factors that limit the performance of conventional space-time adaptive processing (STAP) methods for airborne radar. Sparse recovery (SR)-based and convolutional neural network (CNN)-based STAP methods can obtain high-resolution estimations of the clutter space-time spectrum by using few IID training range cells, so as to realize the clutter suppression effectively. However, the performance of SR-STAP methods usually depends on the SR algorithms, having the problems of parameter setting difficulty,

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Wang, Degen, Tong Wang, Weichen Cui, and Cheng Liu. "Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty." Remote Sensing 14, no. 18 (2022): 4463. http://dx.doi.org/10.3390/rs14184463.

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Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays a key role in detecting moving targets in strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious clutter suppression performance loss when the number of training samples is insufficient due to the inhomogeneous clutter environment. In this article, an efficient sparse recovery STAP algorithm is proposed. First, inspired by the relationship between multiple sparse Bayesian learning (M-SBL) and subspace-based hybrid greed

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Wang, Zhihao, Wei Chen, Tianfu Zhang, Mengdao Xing, and Yongliang Wang. "Improved Dimension-Reduced Structures of 3D-STAP on Nonstationary Clutter Suppression for Space-Based Early Warning Radar." Remote Sensing 14, no. 16 (2022): 4011. http://dx.doi.org/10.3390/rs14164011.

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By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are appl

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Kawalec, Adam, Anna Ślesicka, and Błażej Ślesicki. "A New Statistical Method for Determining the Clutter Covariance Matrix in Spatial–Temporal Adaptive Processing of a Radar Signal." Sensors 23, no. 9 (2023): 4280. http://dx.doi.org/10.3390/s23094280.

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In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new

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Beau, Sophie, and Sylvie Marcos. "Range dependent clutter rejection using range-recursive space-time adaptive processing (STAP) algorithms." Signal Processing 90, no. 1 (2010): 57–68. http://dx.doi.org/10.1016/j.sigpro.2009.05.029.

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Xia, Dong. "Robust STAP Algorithm under the Steering Vector Uncertainty Set." Advanced Materials Research 403-408 (November 2011): 2640–44. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2640.

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For solving the problem of optimum space-time adaptive processing (STAP) under the steering vector mismatch, a robust STAP algorithm is proposed based on the concept of robust Capon beamforming. The model of the steering vector error is established, describing the uncertainty of the desired steering vector. And a new optimization criterion is formed, by which the robust weight vector is acquired. Eventually experimental results and analysis are given with simulation signals. It is verified that the presented method is insensitive to the steering vector error and has a bigger improvement factor

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Fu, Dongning, Guisheng Liao, and Jingwei Xu. "Clutter Subspace Characteristics-Aided Space-Time Adaptive Outlier Sample Selection Method." Sensors 21, no. 9 (2021): 3108. http://dx.doi.org/10.3390/s21093108.

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For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based train

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da Silva, André B. C., and Stefan V. Baumgartner. "Novel post-Doppler STAP with a priori knowledge information for traffic monitoring applications: basic idea and first results." Advances in Radio Science 15 (September 21, 2017): 77–82. http://dx.doi.org/10.5194/ars-15-77-2017.

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Abstract. This paper presents a novel a priori knowledge-based algorithm for traffic monitoring applications. The powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a known road network obtained from the freely available OpenStreetMap (OSM) database. The road information is applied after the PD STAP for recognizing and rejecting false detections, and moreover, for repositioning the vehicles detected in the vicinity of the roads. The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art

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Wang, Yongliang, Keqing Duan, and Wenchong Xie. "Cross Beam STAP for Nonstationary Clutter Suppression in Airborne Radar." International Journal of Antennas and Propagation 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/276310.

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A novel space-time adaptive processing (STAP) method for nonstationary clutter suppression is proposed. The developed method forms a multibeam along the cross line to participate in adaptive processing, which sufficiently utilizes the spatial information both in azimuth and elevation and guarantees the least system degrees of freedom (DOFs). The characteristics of this structure help to suppress the short-range clutter which is the primary component of nonstationary clutter. Therefore, this method provides favorable clutter suppression performance when clutter range dependence exists. Approach

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