A study of Reduced-Rank stap

Zhang, Liang; Bao, Zheng; Liao, Guisheng

doi:10.1007/s11767-000-0001-4

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Reducing the dimension or rank is often used to solve the problems mentioned above, by decreasing the system's DOF and selecting parts of the training sample data to estimate the CNCM of the cell to be detected [11][12][13][14]. For example, in STAP dimension reduction methods with fixed structure, the auxiliary channel processing (ACP) method [15] has been proposed by means of setting the two-dimensional beam near the clutter ridge as the auxiliary beam.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples

Liu

Liao

et al. 2022

Remote Sensing

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Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods.

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Section: Introductionmentioning

confidence: 99%

Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples

Liu

Liao

et al. 2022

Remote Sensing

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“…Conversely, sub-optimal algorithms that require fewer samples have been devised. For instance, reduced-dimension [16,17] (e.g., loaded sample matrix inversion (LSMI) [6], 3DT [17]) and reduced-rank algorithms [18,19] (e.g., methods exploiting structural information) were proposed to diminish the demand for samples. These algorithms rely on specific assumptions about the data's statistical properties and suffer from performance loss [9].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

et al. 2021

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Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support.

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