Cyclic training sample selection and cancellation technique for airborne STAP radar under nonhomogeneous environment

Xinzhe, Li; Xie, Wu; Wang, Yongliang

doi:10.1016/j.dsp.2020.102803

Cited by 10 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To cope with the problem of non-homogeneity in the space-time adaptive processing (STAP), test statistics proposed by NHD are commonly used to identify the outliers in the training data [23][24][25][26][27]. The GIP algorithm is the most commonly used NHD for detecting outliers in a heterogeneous environment [28][29][30][31].…”

Section: Gip Based Nhdmentioning

confidence: 99%

Robust Adaptive Radar Beamforming Based on Iterative Training Sample Selection Using Kurtosis of Generalized Inner Product Statistics

Tian,

Zhang

2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

In engineering application, there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval (PRI). Therefore, if the training samples used to calculate the weight vector does not contain the jamming, then the jamming cannot be removed by adaptive spatial filtering. If the weight vector is constantly updated in the range dimension, the training data may contain target echo signals, resulting in signal cancellation effect. To cope with the situation that the training samples are contaminated by target signal, an iterative training sample selection method based on non-homogeneous detector (NHD) is proposed in this paper for updating the weight vector in entire range dimension. The principle is presented, and the validity is proven by simulation results.

show abstract

Section: Gip Based Nhdmentioning

confidence: 99%

Robust Adaptive Radar Beamforming Based on Iterative Training Sample Selection Using Kurtosis of Generalized Inner Product Statistics

Tian,

Zhang

2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

show abstract

“…Therefore, the crucial question of how to measure the distance and dissimilarity of different points on manifolds efficiently must be addressed. A natural choice for the dissimilarity metric to solve (15) is the affine invariant Riemannian metric…”

Section: Clutter Covariance Matrix Estimation On Matrix Manifoldsmentioning

confidence: 99%

“…Many algorithms concentrate on an effective non-homogeneity detector (NHD) [12,13] in which the generalized internal product (GIP) and the adaptive power residue (APR) are two important criteria. Since certain NHDs depend on the training data used, to mitigate the finite sample effect, improved methods were proposed, e.g., a new type of GIP detector based on diagonal loading (LGIP) was reported in [14] and a cyclic training sample selection and cancellation (CTSSC) algorithm was proposed in [15]. Conversely, sub-optimal algorithms that require fewer samples have been devised.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

et al. 2021

View full text Add to dashboard Cite

Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support.

show abstract

“…Clutter needs to be suppressed in complex environments when airborne phased array radar systems perform moving target detection [1]. Space-time adaptive processing (STAP) technology has attracted widespread attention as a very effective clutter suppression method [2][3][4][5][6]. In order to provide excellent suppression of clutter in the cell under test (CUT) for accurate target identification, it is essential to ensure accurate estimation of the clutter covariance matrix (CCM) of the CUT.…”

Section: Introductionmentioning

confidence: 99%

Estimation of clutter covariance matrix in stap based on knowledge-aided and geometric methods

Jing,

Du,

Tian

et al. 2023

Phys. Scr.

View full text Add to dashboard Cite

The insufficient number of available samples can cause inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), resulting in degraded clutter suppression performance. To tackle this problem, a CCM estimation approach based on knowledge aided and geometric methods is proposed in this paper. A combination of environmental as well as structural (Persymmetric or Symmetric structure) knowledge information is utilized to model the covariance matrix of each sample as a knowledge-aided Hermitian positive definite (KAHPD) covariance matrix. The estimation problem is introduced into the Riemannian manifold composed of the KA-HPD covariance matrices, and the geometric method is used for nonlinear processing. Based on the Kullback-Leibler (KL) divergence and the KL mean, the final estimated CCM is designed as a weighted combination of each KA-HPD covariance matrix. Experiment results show that the two designed structural covariance matrix estimators possess superior clutter suppression performance.

show abstract

Cyclic training sample selection and cancellation technique for airborne STAP radar under nonhomogeneous environment

Cited by 10 publications

References 10 publications

Robust Adaptive Radar Beamforming Based on Iterative Training Sample Selection Using Kurtosis of Generalized Inner Product Statistics

Robust Adaptive Radar Beamforming Based on Iterative Training Sample Selection Using Kurtosis of Generalized Inner Product Statistics

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

Estimation of clutter covariance matrix in stap based on knowledge-aided and geometric methods

Contact Info

Product

Resources

About