A Novel Covariance Matrix Estimation via Cyclic Characteristic for STAP

Hu, Jinfeng; Li, Jianping; Li, Huiyong; Li, Keze; Liang, Jing

doi:10.1109/lgrs.2019.2957023

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…The IF of more than 70 dB is a very good result in the context of the IF parameter results boasted by researchers in recent publications [21][22][23][24]. Moreover, it provides evidence of proper clutter cancellation.…”

Section: Improvement Factormentioning

confidence: 55%

“…Among the statistical methods can be distinguished methods involving the deliberate selection of training bins [19][20][21]. In article [20], it was described as a method of training sample selection (training sample selection), which consists in accepting only those training cells for which the covariance matrices are similar in a certain way to the covariance matrix of the bin being tested for the presence of a target.…”

Section: Related Workmentioning

confidence: 99%

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A New Statistical Method for Determining the Clutter Covariance Matrix in Spatial–Temporal Adaptive Processing of a Radar Signal

Kawalec

Ślesicka

ŚLESICKI

2023

Sensors

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In this article, a new statistical method for estimating the clutter covariance matrix in space–time adaptive radar signal processing (STAP) is presented and studied. The new method was designed for multiple-input–multiple-output (MIMO) radar with time division multiplexing (TDM). An extensive analysis of statistical and non-statistical methods for estimating the clutter covariance matrix in STAP is presented in this paper. In addition, the STAP algorithm for the standard statistical SMI clutter covariance matrix estimation method, which is based on QR distribution, has been presented. The new method is based on LU distribution with partial pivoting. Simulation results confirm the validity of the presented model and theoretical assumptions. In addition, more accurate object detection results were demonstrated for specific computational examples than for other statistical methods. Considering the current analysis of the literature, it is noted that attention has now been focused worldwide on the study of non-statistical methods for estimating clutter covariance matrices in heterogeneous environments. Hence, it should be emphasized that the posted study fills a gap in current research on STAP.

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Section: Improvement Factormentioning

confidence: 55%

Section: Related Workmentioning

confidence: 99%

A New Statistical Method for Determining the Clutter Covariance Matrix in Spatial–Temporal Adaptive Processing of a Radar Signal

Kawalec

Ślesicka

ŚLESICKI

2023

Sensors

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“…To improve the detection ability in heterogeneous environments, Mevin et al proposed a knowledge-aided STAP technique in which the prior information was incorporated within the conventional training strategies [14]. A similar idea was used in other works [15][16][17][18]. The reduced-rank and reduced-dimension STAP, such as subspace projection and extended factored algorithms, have been widely considered since the training requirements were reduced to twice the degrees of freedom or the clutter rank [19][20][21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

“…Now, expressions ( 19) and ( 24), derived from the optimization problem presented in Equation (17), are obtained to estimate the profile vector α and the calibration vector h. However, it is difficult to obtain the analytical solutions of α and h by directly solving these two expressions. This is because Equations ( 19) and (24) are not closed-form expressions for α and h, that is, the variables R S , r Sx and ψ in Equation ( 19) are functions of h, and the variables R G , r Ge and ς in Equation ( 24) are functions of α.…”

Section: The Motivation Of the Proposed Joint Optimization Algorithmmentioning

confidence: 99%

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Deng

Pei

et al. 2023

Applied Sciences

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In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.

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“…Moreover, there are also lots of methods to improve the CCM, such as the color loading method [12] , etc. There are also methods to construct a new clutter covariance matrix by using the cyclic property of the CCM itself [13] , which takes advantage of its own property to reduce the interference of external environment.…”

Section: Introductionmentioning

confidence: 99%

Research on STAP matrix fusion algorithm based on LMS

Sang

Suo

liu

2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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The space-time adaptive processing(STAP) technique adaptively suppresses clutter jointly in the space domain and time domain, but in practice, due to the lack of sample quantity, the performance of this technique suffers a serious loss. In contrast, the knowledge-aided space-time adaptive processing(KA-STAP) algorithm can improve the estimation accuracy of the clutter covariance matrix by using a priori knowledge. In this paper, by using the cycle characteristic of the clutter covariance matrix as the priori knowledge, the spatial circular matrix, the temporal circular matrix and the spatial-temporal circular matrix are constructed. And use the Least-Mean-Square(LMS) criterion to calculate the coefficients and then integrate the above matrices. Compared with the traditional STAP method, the KA-STAP using the cyclic property as a priori knowledge has about 1 dB of output signal-to-clutter-noise(SCNR) improvement.

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A Novel Covariance Matrix Estimation via Cyclic Characteristic for STAP

Cited by 10 publications

References 16 publications

A New Statistical Method for Determining the Clutter Covariance Matrix in Spatial–Temporal Adaptive Processing of a Radar Signal

A New Statistical Method for Determining the Clutter Covariance Matrix in Spatial–Temporal Adaptive Processing of a Radar Signal

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

Research on STAP matrix fusion algorithm based on LMS

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