Detection of heterogeneous samples based on loaded generalized inner product method

Zou

EURASIP J. Adv. Signal Process.

et al. 2018

Traditional space-time adaptive processing (STAP) usually needs many independent and identically distributed (i.i.d) training datasets for estimating clutter covariance matrix (CCM). But this requirement is hardly satisfied in the heterogeneous clutter environments, which lead to an inaccurate estimation of CCM and accordingly degrade the performance of STAP significantly. To improve the performance of STAP in heterogeneous environments, a novel deterministic-aided (DA) single dataset STAP method based on sparse recovery technique (SR) is proposed in this paper. This presented algorithm exploits the property that the clutter components of side-looking airborne or spaceborne radar are distributed along the clutter ridge to estimate the CCM of the cell under test (CUT) without any secondary training data. The new method only uses a single CUT data to acquire a high-resolution angle-Doppler power spectrum using sparse recovery (SR) approach and then employs a new adaptive deterministic-aided generalized inner product (GIP) algorithm to recognize and select the clutter components in the CUT angle-Doppler power spectrum automatically. Subsequently, the CCM, which is used to construct the weights of STAP filter, can be effectively estimated by the selected clutter components to fulfill the final STAP filter processing. Simulation results verify the effectiveness of the proposed detection method.

Section: Da-gip Algorithm For Ccm Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deterministic-aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments

Zou

EURASIP J. Adv. Signal Process.

et al. 2018

“…Many training sample selection algorithms have been proposed to improve the STAP performance in heterogeneous environment. The generalized inner product (GIP) algorithm [14] utilizes GIP to eliminate the samples through different clutter statistical characteristics from the CUT. The power-selected training (PST) algorithm [15] chooses the samples with the strong clutter power to deepen the clutter notch.…”

Section: Introductionmentioning

confidence: 99%

A Geometric Barycenter-Based Clutter Suppression Method for Ship Detection in HF Mixed-Mode Surface Wave Radar

Zhang

Deng

et al. 2019

Remote Sensing

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

“…Moreover, the training samples non-homogeneity will lead to the estimation error of the clutter covariance matrix and severely degrade the performance of MIMO-STAP [11], [17], [18]. Especially, as a classic factor for forming the non-homogeneous clutter environment, when there exist interference-targets (outliers) signals in the training samples set, the specific phenomenon named target self-nulling will be generated [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Non-homogeneity detector (NHD) [18][19][20][21][22] is wellknown for its ability to improve the target detection performance of STAP in non-homogeneous environment. The generalized inner products (GIP) method [18][19][20][21][22] is a typical NHD for identifying the outliers with non-homogeneity, under the condition on the accurate estimation of the clutter covariance matrix. However, strong outliers may exist in the training samples set, which will result in severe performance degradation of the GIP NHD.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Aided Non-Homogeneity Detector for Airborne MIMO Radar STAP

Wang¹,

Zhao²,

Wang³

2017

RADIOENGINEERING

The target detection performance decreases in airborne multiple-input multiple-output (MIMO) radar space-time adaptive processing (STAP) when the training samples contaminated by interference-targets (outliers) signals are used to estimate the covariance matrix. To address this problem, a knowledge-aided (KA) generalized inner product non-homogeneity detector (GIP NHD) is proposed for MIMO-STAP. Firstly, the clutter subspace knowledge is constructed by the system parameters of MIMO radar STAP. Secondly, the clutter basis vectors are utilized to compose the clutter covariance matrix offline. Then, the GIP NHD is integrated to realize the effective training samples selection, which eliminates the effect of the outliers in training samples on target detection. Simulation results demonstrate that in non-homogeneous clutter environment, the proposed KA-GIP NHD can eliminate the outliers more effectively and improve the target detection performance of MIMO radar STAP compared with the conventional GIP NHD, which is more valuable for practical engineering application.