Article
Engineering, Aerospace
Ling Hong, Fengzhou Dai
Summary: This article addresses the problem of wideband radar target detection in a heterogeneous environment. The wideband radar return of the target is characterized as a subband model with range migration, and the heterogeneous clutter is described with a hierarchical Bayesian model. Knowledge-aided detectors, including the maximum posterior ratio test, Rao test, and Wald test, are proposed based on the target and clutter models. The performance of the detectors is evaluated using simulations with both simulated clutter generated by a probability model and synthesized clutter from a real synthetic aperture radar complex image, showing their effectiveness for wideband radar target detection in heterogeneous clutter.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Environmental Sciences
Qiang Wang, Bin Xue, Xiaowei Hu, Guangen Wu, Weihu Zhao
Summary: A novel robust space-time joint sparse processing method with airborne active array is proposed to address the issue of clutter suppression under insufficient IID training samples and exacerbated clutter heterogeneity, achieving superior clutter suppression performance without relying on IID training samples.
Article
Engineering, Electrical & Electronic
Dongyang Li, Haixia Zhang, Dongfeng Yuan, Minggao Zhang
Summary: This work proposes a learning-based hierarchical edge caching scheme to predict content popularity using deep learning and content similarity and minimize average downloading latency. It effectively addresses the latency issues caused by the explosive growth of mobile data traffic.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2023)
Article
Geochemistry & Geophysics
Penghui Huang, Zihao Zou, Xiang-Gen Xia, Xingzhao Liu, Guisheng Liao
Summary: This article proposes a novel dimension-reduced STAP algorithm based on spatial-temporal 2-D sliding window processing for effective clutter suppression. It achieves better performance with lower computational complexity compared to conventional STAP algorithms. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Automation & Control Systems
Rafi Ahmed, B. M. Golam Kibria
Summary: A novel method for detecting ground-moving targets using a distinguishing target and clutter feature is introduced. The method extracts clutter proximity feature based on the Euclidean distance in the angle-Doppler domain, and classifies target and clutter pixels for detection without removing clutters.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Geochemistry & Geophysics
Xixi Chen, Yongqiang Cheng, Hao Wu, Hongqiang Wang
Summary: In this paper, a novel clutter suppression method via affine transformation on manifolds is proposed to address the performance degradation of adaptive clutter suppression in heterogeneous environments. Experimental results on both simulated and real data validate the superiority of the proposed method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xianghai Li, Zhiwei Yang, Xiao Tan, Guisheng Liao, Yuxiang Shu
Summary: This article proposes a homogeneous sample selection method based on a novel concept of generalized spatial spectrum density function (GSSDF) to improve the robustness of clutter suppression. Experimental results demonstrate that the proposed method achieves better clutter suppression performance than other contrast methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Chemistry, Analytical
Hai Li, Yutong Chen, Kaihong Feng, Ming Jin
Summary: A low-altitude windshear wind speed estimation method based on knowledge-aided sparse iterative covariance-based estimation STAP (KASPICE-STAP) is proposed in this paper to address the issue of estimating wind speed in low-altitude windshear conditions for airborne weather radar without independent identically distributed (IID) training samples. Firstly, a clutter dictionary composed of clutter space-time steering vectors is constructed using prior knowledge. Then, the SPICE algorithm is employed to iteratively obtain the clutter covariance matrix. Finally, the STAP processor is designed to eliminate ground clutter echo signals and estimate the wind speed. Simulation results demonstrate the accuracy of the proposed method without IID training samples.
Article
Computer Science, Information Systems
Qiang Wang, Yani Zhang, Zhihui Li, Weihu Zhao
Summary: In this paper, an improved direct data domain method based on sparse Bayesian learning is proposed to suppress clutter in a nonhomogeneous clutter environment. The method only uses a single snapshot data of a cell under test (CUT) and has fast computational speed. Simulation results show that the proposal can significantly decrease the computational burden while maintaining superior heterogeneous clutter suppression performance.
Article
Geochemistry & Geophysics
Jian Xue, Jiali Yan, Meiyan Pan, Shuwen Xu
Summary: This letter addresses the problem of knowledge-aided adaptive detection of radar targets in nonhomogeneous correlated compound Gaussian sea clutter. The lognormal distribution is used as the prior distribution to account for the non-Gaussian nature of sea clutter. A convex combination estimator (CCE) is proposed to ensure the accuracy of estimating the covariance matrix structure of sea clutter by jointly utilizing prior information and current secondary data. Based on the complex parameter Gradient test and the CCE, a knowledge-aided adaptive detector is designed. Numerical experiments demonstrate the effectiveness of the proposed CCE and adaptive detector compared to their counterparts.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Ecology
Ionut Paun, Dirk Husmeier, J. Grant C. Hopcraft, Majaliwa M. Masolele, Colin J. Torney
Summary: Understanding the spatial dynamics of animal movement is crucial for maintaining ecological connectivity and conserving key habitats. This study presents a Bayesian framework based on Gaussian processes to analyze spatial characteristics of animal movement, and demonstrates its effectiveness through synthetic data and telemetry data from the Serengeti wildebeest migration.
Article
Geochemistry & Geophysics
Zhizhuo Jiang, You He, Gang Li, Xiao-Ping Zhang
Summary: A new detector for STAP applications, called VCF-STAP, is proposed in this letter to achieve robust performance of moving target detection in heterogeneous environments. By using the volume cross-correlation function (VCF) to form a distance measure between signal and target subspaces without modeling clutter distribution, the VCF-STAP outperforms existing methods and maintains the constant false alarm rate (CFAR) property.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Yuanyi Xiong, Wenchong Xie, Hu Li, Xunzhang Gao
Summary: In this article, a novel colored-loading factor optimization method based on prewhitening performance evaluation is proposed. The method effectively optimizes the colored-loading factors by processing channel pairs and applying space-time subaperture smoothing. It solves the problem of potential differences in priori covariance matrix across range cells.
IEEE SENSORS JOURNAL
(2023)
Article
Geochemistry & Geophysics
Penghui Huang, Hao Yang, Xiang-Gen Xia, Zihao Zou, Xingzhao Liu, Guisheng Liao
Summary: In this article, a novel sea clutter suppression method based on joint space-time-frequency adaptive filtering is proposed. The method employs a subaperture time-domain sliding window and a modified subspace projection technique to achieve effective clutter suppression and signal recovery for sea clutter. The final clutter suppression is achieved through a second-stage spatial filtering method.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Zhixin Liu, Shengqi Zhu, Jingwei Xu, Xiongpeng He, Guisheng Liao, Lan Lan
Summary: A novel vertical element-pulse coding (EPC) radar is proposed in this paper to address the challenge of clutter range ambiguity and range dependence in airborne forward-looking radar. The proposed approach separates and extracts clutters corresponding to different ambiguous areas using element-pulse decoding and pre-STAP filtering, and independently accomplishes clutter elimination for each separated area.
Article
Statistics & Probability
Olivier Besson, Francois Vincent, Xavier Gendre
STATISTICS & PROBABILITY LETTERS
(2020)
Article
Engineering, Electrical & Electronic
Francois Vincent, Olivier Besson
Summary: The paper explores the issue of identifying background in hyperspectral images and introduces a robust Adaptive Matched Filter as a solution, showcasing potential improvement in case of target signature mismatch.
Article
Engineering, Electrical & Electronic
Olivier Besson, Francois Vincent, Stefania Matteoli
Summary: This paper explores local detection of a target in hyperspectral imaging using an elliptically contoured background distribution and matrix-variate t distribution for parameter inference. It presents one-step and two-step generalized likelihood ratio tests, showing that the former coincides with the Gaussian assumption and maintains a constant false alarm rate.
Article
Engineering, Electrical & Electronic
Olivier Besson
Summary: This paper examines the behavior of adaptive filters or detectors when trained with t-distributed samples instead of Gaussian distributed samples, and investigates the impact on distribution of relevant statistics. It derives properties of partitioned complex F distributed matrices to obtain statistical representations in terms of independent chi-square distributed random variables which are compared with their Gaussian counterparts, with numerical simulations illustrating and quantifying the induced degradation.
Article
Engineering, Electrical & Electronic
Francois Vincent, Olivier Besson, Stefania Matteoli
Summary: In this paper, the popular RX detector is extended to the more realistic replacement model case in the context of hyperspectral anomaly detection. By estimating the background power variation, the standard RX scheme is improved to obtain the closed-form Replacement RX (RRX), which outperforms the standard RX in a real data benchmark experiment.
Article
Geochemistry & Geophysics
Corinne Mailhes, Olivier Besson, Amandine Guillot, Sophie Le Gac
Summary: This article introduces a new parametric estimation method ARWARP, combining autoregressive model and frequency scale transformation, for higher resolution estimation of sea-level anomaly in ocean monitoring. The performance advantage of ARWARP in slope estimation is validated through simulations and real data, providing improved estimation accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Olivier Besson, Francois Vincent
Summary: This study investigates the detection of a signal corrupted by Gaussian noise with unknown mean and covariance matrix, in the presence of training samples with different means. The robustness of adaptive detectors under the assumption of a same mean is evaluated. Statistical representations are derived for the generalized likelihood ratio test, adaptive matched filter, and adaptive coherence estimator, considering both an additive model and a replacement model. The new representations are given in terms of simple F distributions and provide insights into the key parameters affecting the probability of false alarm and probability of detection.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Francois Vincent, Olivier Besson
Summary: This paper proposes a more realistic model for hyperspectral imaging, where a two-step generalized likelihood ratio test is formulated. The study shows that the log likelihood can be well approximated by a weighted combination of the log likelihoods of the FTMF and the AMF, with the dimension of the background subspace serving as the tuning parameter to balance between these two well-known detectors. Comparisons with standard techniques on real hyperspectral data demonstrate the good performance of the new detectors.
Article
Engineering, Aerospace
Olivier Besson
Summary: This paper addresses the problem of detecting a signal of interest in Gaussian noise with an unknown covariance matrix, in which the amplitude of the signal fluctuates along the observations and follows a Rice distribution. The authors formulate it as a composite hypothesis testing problem and derive the generalized likelihood ratio test, which ensures a constant false alarm rate. Numerical simulations demonstrate its performance for Rician and Swerling I and III targets, showing significant improvement for Rician targets with a small number of training samples.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Olivier Besson
Summary: This paper addresses the problem of detecting a Gaussian rank-one signal by learning the covariance matrix of the noise in the samples under test using training samples. The whitened training samples are proposed to be used instead of the generalized likelihood ratio test. Three simpler alternatives, namely the Rao, gradient, and Durbin tests, are investigated. Closed-form expressions of the corresponding test statistics are derived and the detectors are shown to have a constant false alarm rate. Their performance is evaluated through numerical simulations.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Francois Vincent, Olivier Besson, Benjamin Gigleux, Eric Chaumette
Summary: In this paper, a method for maximum likelihood frequency estimation of a single tone in noise is proposed to overcome the computational complexity in real-time and embedded hardware architectures. Numerical simulations demonstrate the effectiveness of the proposed scheme.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Olivier Besson
Summary: This paper considers the problem of adaptive detection of a Gaussian rank-one component buried in Gaussian noise with unknown statistics, known up to a scaling factor, which arises when detecting Swerling I targets in radar systems. The score function and Fisher information matrix are derived from the joint distribution of the test and training samples, allowing for the formulation of the Rao, Wald, and gradient tests for the composite hypotheses problem. Numerical simulations evaluate the performance of these tests under matched and mismatched cases.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Olivier Besson
Summary: This paper examines adaptive detection in Gaussian noise with unknown covariance matrix when training samples do not match the vector under test, focusing on constant false alarm rate detectors dependent on two parameter-free statistics. It analyzes the impact of covariance mismatched training samples and investigates ways to mitigate the effects.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
C. Mailhes, O. Besson, A. Guillot, S. Le Gac
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
(2020)
Article
Engineering, Electrical & Electronic
Francois Vincent, Olivier Besson
IEEE SIGNAL PROCESSING LETTERS
(2020)