A robust refined training sample reweighting space–time adaptive processing method for airborne radar in heterogeneous environment

Xiao, Hao; Wang, Tong; Zhang, Shuguang; Wen, Cai

doi:10.1049/rsn2.12034

Cited by 9 publications

(3 citation statements)

References 32 publications

(63 reference statements)

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“…Based on the non-homogeneous detector (NHD) [18][19][20][21][22], a method which can calculate the weight vector in the whole range domain is proposed in this paper. The entire range samples in the PRI are divided into multiple range segments, each range segment calculates a new weight vector separately.…”

Section: Introductionmentioning

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.

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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.

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Section: Introductionmentioning

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.

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“…Airborne phased−array radar is widely used to detect moving targets because of its mobility and freedom from the radius of the earth's curvature, but it is always plagued by the existence of the 'nuisance' clutter coupling in the space−time domain. To ensure the detection of weak targets, space-time adaptive processing (STAP) methods [1][2][3][4] are developed to mitigate the clutter and achieve better performance compared with traditional non−adaptive methods. For classical STAP methods, the performance is substantially determined by the accuracy of the clutter plus noise covariance matrix (CCM).…”

Section: Introductionmentioning

confidence: 99%

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

et al. 2022

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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 (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads.

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“…Space-time adaptive processing (STAP) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] is an effective approach for ground clutter suppression and low-velocity target detection in airborne radars. The performance of STAP mainly depends on the estimation accuracy of the clutter plus noise covariance matrix (CCM) of the cell under test (CUT).…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

Liu

Wang

et al. 2021

Sensors

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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 the ground clutter, a set of spatial steering vectors with array errors are well estimated by fine Doppler localization. In the second stage, firstly, in order to solve the model mismatch problem caused by array errors, we directly use these spatial steering vectors obtained in the first stage to construct the space-time dictionary, and then, the constructed dictionary and multiple measurement vectors sparse Bayesian learning (MSBL) algorithm are combined for space-time adaptive processing (STAP). The proposed SR-STAP method can exhibit superior clutter suppression performance and target detection performance in the presence of arbitrary array errors. Simulation results validate the effectiveness of the proposed method.

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A robust refined training sample reweighting space–time adaptive processing method for airborne radar in heterogeneous environment

Cited by 9 publications

References 32 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

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

A Two-Stage STAP Method Based on Fine Doppler Localization and Sparse Bayesian Learning in the Presence of Arbitrary Array Errors

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