Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Silva, André Barros Cardoso da; Baumgartner, Stefan V.; Krieger, Gerhard

doi:10.1109/tgrs.2019.2901126

Cited by 23 publications

(13 citation statements)

References 22 publications

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“…The PD-STAP is a reduced rank algorithm of the classical joint domain STAP, which is mainly carried out in the range-Doppler domain. A moving target should remain in one doppler cell during the integration time to optimize the computational cost (STAP processing) [5,7,8]. Otherwise, the long CPI needs to be divided into shorter CPIs for processing.…”

Section: Multichannel Signal Model and Pd-stap Overviewmentioning

confidence: 99%

“…Assume b n Ha, which is the platform distance from the ground. The multichannel signal model for the PD-STAP is as follows [8,9]:…”

Section: Multichannel Signal Model and Pd-stap Overviewmentioning

confidence: 99%

“…Moreover, contamination of the training data with strong discrete scatterers and interfering moving target signals may remarkably degrade the performance. Therefore, the DCM estimation of conventional STAP is impractical in complex and changeable environments; many reduced dimensions or partial adaptive STAP algorithms have been proposed [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, a knowledge-aided (KA) algorithm showed that the generalized inner product (GIP) test can be applied in the space-Doppler domain in order to remove the nonhomogeneous training data from the DCM estimation. However, the use of KA algorithms further increases the complexity of STAP, especially when real-time processing is required [8,10].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

et al. 2020

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The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP) in practical applications. However, its detection performance is severely constrained by the training data, especially in a dense signal environment. Improper training data and contamination of moving target signals remarkably degrade the performance of disturbance suppression and result in target cancellation by self-whitening. To address these issues, a novel post-Doppler parametric adaptive matched filter (PD-PAMF) detector is proposed in the range-Doppler domain. Specifically, the detector is introduced via the post-Doppler matched filter (PD-MF) and the lower-diagonal-upper (LDU) decomposition of the disturbance covariance matrix, and the disturbance signals of the spatial sequence are modelled as an auto-regressive (AR) process for filtering. The purpose of detecting ground moving targets as well as for estimating their geographical positions and line-of-sight velocities is achieved when the disturbance is suppressed. The PD-PAMF is able to reach higher performances by using only a smaller training data size. More importantly, it is tolerant to moving target signals contained in the training data. The PD-PAMF also has a lower computational complexity. Numerical results are presented to demonstrate the effectiveness of the proposed detector.

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Section: Multichannel Signal Model and Pd-stap Overviewmentioning

confidence: 99%

“…Assume b n Ha, which is the platform distance from the ground. The multichannel signal model for the PD-STAP is as follows [8,9]:…”

Section: Multichannel Signal Model and Pd-stap Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

et al. 2020

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“…Estimating the time-space clutter covariance matrix (ST-CCM) is one of the most critical issues in adaptive space-time statistical processing (STAP) [1][2][3][4]. However, it is difficult to obtain sufficient training samples to satisfy the requirement for independent and identically distributed (IID).…”

Section: Introductionmentioning

confidence: 99%

Clutter Subspace Characteristics-Aided Space-Time Adaptive Outlier Sample Selection Method

Liao

2021

Sensors

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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 training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.

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A training sample selection method based on united generalised inner product statistics for STAP

Li¹,

Xie²,

Wang³

2021

IET Radar Sonar & Navi

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In heterogeneous environments, the snapshot under test (SUT) and the corresponding training samples are usually not independent and identically distributed, which seriously degrades the clutter suppression performance of space-time adaptive processing (STAP). To solve this problem, this paper proposes a method which can select the training samples with similar clutter characteristics to that of the SUT. The proposed method constructs a novel united generalised inner product (UGIP) statistic with the sub-aperture clutter covariance matrix (CCM) of the SUT and that of any other snapshot. The smaller the statistic is, the more similar the corresponding two snapshots are. Therefore, the snapshots with smaller UGIPs will be selected as training samples. The proposed method effectively improves the quality of the selected training samples for STAP and a better estimate of the CCM can be obtained. Simulation experiments verify the effectiveness of the proposed method with both simulated data and measured data.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Cited by 23 publications

References 22 publications

A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

A Novel Post-Doppler Parametric Adaptive Matched Filter for Airborne Multichannel Radar

Clutter Subspace Characteristics-Aided Space-Time Adaptive Outlier Sample Selection Method

A training sample selection method based on united generalised inner product statistics for STAP

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