Robust non‐homogeneity detection algorithm based on prolate spheroidal wave functions for space‐time adaptive processing

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

“…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.

“…In the past years, numbers of non-homogeneity detection (NHD) algorithms [7][8][9][10] have been proposed, such as generalised inner product (GIP) algorithm [7][8][9] and loaded GIP algorithm [10]. Idealistically, these GIP algorithms can exclude those samples which are heterogeneous with most of the initial training samples.…”

Section: Introductionmentioning

confidence: 99%

Robust training samples selection algorithm based on spectral similarity for space–time adaptive processing in heterogeneous interference environments

IET Radar, Sonar & Navigation

et al. 2015

In space-time adaptive processing (STAP), the required interference covariance matrix is commonly estimated from homogeneous samples. However, the real interference environments are often heterogeneous, which is induced by the fact that the training samples do not share the same interference property with the cell under test (CUT), and thus the interference covariance matrix estimated from the training samples is mismatched with the real one. To improve the performance of STAP in heterogeneous interference environments, this paper proposes a robust training samples selection algorithm which is based on the spectral similarity of the interference. The spectrums of samples are estimated firstly, which are then utilised to calculate the similarity between the interference of the CUT and those of the training samples. Then, the samples whose interference spectrums are similar to that of the CUT are selected as the final training samples, such that the performance of the interference covariance matrix estimated from these samples can be improved significantly. Besides, to avoid the target self-nulling effect, a simple technique is adopted to exclude the samples which are contaminated by target signal. The proposed method is applied to real radar data, and experimental results demonstrate the effectiveness of the proposed method.