Target Detection Within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

Hua, Xiaoqiang; Ono, Yoshio; Peng, Linyu; Cheng, Yongqiang; Wang, Hongqiang

doi:10.1109/tsp.2021.3095725

Cited by 91 publications

(55 citation statements)

References 53 publications

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“…In our future work, on the one hand, we aim to study Wasserstein geometry on other matrix manifolds, such as the Stiefel manifold [25], Grassman manifold [26] and some complex matrix manifolds [27]. On the other hand, we would like to generalize geometry-based methods to solve more problems in image, signal processing [28] and data science.…”

Section: Discussionmentioning

confidence: 99%

Geometric Characteristics of the Wasserstein Metric on SPD(n) and Its Applications on Data Processing

Luo

Zhang

Cao

et al. 2021

Entropy

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The Wasserstein distance, especially among symmetric positive-definite matrices, has broad and deep influences on the development of artificial intelligence (AI) and other branches of computer science. In this paper, by involving the Wasserstein metric on SPD(n), we obtain computationally feasible expressions for some geometric quantities, including geodesics, exponential maps, the Riemannian connection, Jacobi fields and curvatures, particularly the scalar curvature. Furthermore, we discuss the behavior of geodesics and prove that the manifold is globally geodesic convex. Finally, we design algorithms for point cloud denoising and edge detecting of a polluted image based on the Wasserstein curvature on SPD(n). The experimental results show the efficiency and robustness of our curvature-based methods.

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Section: Discussionmentioning

confidence: 99%

Geometric Characteristics of the Wasserstein Metric on SPD(n) and Its Applications on Data Processing

Luo

Zhang

Cao

et al. 2021

Entropy

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“…These algorithms are considered in Euclidean space and do not make use of the data structure, leading to subpar performance. Recently, the relevance of matrix information geometry methods for radar signal processing has been of focus and demonstrated [23,24]. These methods exploit the non-linear geometry of matrix manifolds and show advantages in signal processing.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

et al. 2021

Self Cite

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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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“…In microwave radar, radar signal processing is usually full of challenges, under non-ideal conditions including lower signal-to-clutter-and-noise ratio (SCNR) [28] and interference [29]. Similarly, the low SCNR is also a major problem in the respiration signal detection via UWB radar.…”

Section: Introductionmentioning

confidence: 99%

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

Shi

Pan

et al. 2021

Remote Sensing

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Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

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Target Detection Within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

Cited by 91 publications

References 53 publications

Geometric Characteristics of the Wasserstein Metric on SPD(n) and Its Applications on Data Processing

Geometric Characteristics of the Wasserstein Metric on SPD(n) and Its Applications on Data Processing

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

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