Article
Engineering, Electrical & Electronic
Jun Liu, Tao Jian, Weijian Liu
Summary: This study examines the problem of detecting subspace signals in the presence of subspace interference and Gaussian noise using multiple observations collected from multiple range cells, bands, and/or coherent processing intervals. One-step and two-step detectors are proposed based on persymmetry and the criterion of generalized likelihood ratio test (GLRT), exhibiting constant false alarm rate properties against the noise covariance matrix. Numerical examples show that the proposed detectors outperform their counterparts, especially with small training data, and the one-step GLRT detector generally has better detection performance than the two-step detector.
Review
Computer Science, Information Systems
Weijian Liu, Jun Liu, Chengpeng Hao, Yongchan Gao, Yong-Liang Wang
Summary: Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine the presence or absence of a target. These adaptive detectors possess constant false alarm rate properties and do not require additional processing. Compared to the filtering-then-CFAR technique, adaptive detection typically exhibits better performance. However, there are few overview articles on this topic, hence this study provides a tutorial overview specifically focusing on Gaussian background and covers various aspects.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Tao Jian, Zikeng Xie, Haipeng Wang, Guangfen Wei, Jia He
Summary: In this paper, the problem of adaptive detection for distributed targets in zero-mean Gaussian clutter with unknown persymmetric covariance matrix is considered. Two persymmetric subspace detectors are designed by utilizing the Gradient criterion in homogeneous and partially homogeneous environments. The proposed detectors are theoretically confirmed to be constant false alarm rate to the clutter covariance matrix and the power level. Moreover, the results show that the proposed detectors perform better than the existing competitors, especially in training-limited scenarios.
Article
Computer Science, Information Systems
Tao Jian, Jia He, Yu Liu, You He, Congan Xu, Zikeng Xie
Summary: In this paper, we propose a two-step detector for the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter. The detector leverages the persymmetric structure to achieve constant false alarm rate property with respect to the clutter covariance matrix. The numerical results demonstrate that the proposed detector outperforms existing unstructured subspace detectors and persymmetric subspace detectors, especially with limited training data.
SCIENCE CHINA-INFORMATION SCIENCES
(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)
Article
Engineering, Electrical & Electronic
Tao Jian, Zikeng Xie, Haipeng Wang, Guangfen Wei, Jia He
Summary: This paper addresses adaptive signal detection for range-spread targets in unknown zero-mean Gaussian clutter with persymmetric covariance matrix. A persymmetric subspace detector is devised using the generalized likelihood ratio test (GLRT), which has been theoretically proven to have a constant false alarm rate to the unknown clutter covariance matrix. Numerical results demonstrate the effectiveness of the proposed detector, especially in limited training data scenarios, compared to existing competitors.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Geochemistry & Geophysics
Jian Xue, Jun Liu, Shuwen Xu, Meiyan Pan
Summary: This article focuses on the problem of detecting marine targets using coherent radars in a correlated heavy-tailed sea clutter background. A compound-Gaussian model is used to model the heavy-tailed sea clutter, and the clutter texture is characterized by a lognormal distribution. An adaptive coherent detector is developed based on the two-step generalized likelihood ratio test, which achieves adaptation to sea clutter characteristics and ensures a constant false alarm rate.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Tao Jian, Jun Liu, Shenghua Zhou, Weijian Liu
Summary: This study examines the problem of detecting a target embedded in Gaussian noise with unknown covariance matrix in the colocated multiple-input multiple-output radar system, considering the mismatch between true and nominal steering vectors. Adaptive detectors based on subspace models are proposed without requiring training data, showing stronger robustness to steering vector mismatches compared to existing detectors. However, it is found that the Rao test does not exist for the detection problem considered.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Marine
Jian Wang, Haisen Li, Guanying Huo, Chao Li, Yuhang Wei
Summary: In the background of multi-background underwater surveying and mapping, detecting seafloor terrain is challenging due to environmental noise, sidelobe data, and tunnel emission. Constant false alarm detection, which can eliminate noise interference and provide accurate seabed topography information, is an important research field. This paper proposes an efficient weighted cell averaged constant false alarm detection method (WCA-CFAR) to increase detection probability, reduce missing probability, and improve detection speed. The method is validated through simulation data detection tests and actual lake test data, showing effective reduction in missing detection probability and improvement in detection probability.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Aerospace
Jun Liu, Weijian Liu, Xun Chen, Danilo Orlando, Alfonso Farina
Summary: This article proposes an adaptive detector for target detection in a modern radar system with primary and reference channels. The detector exhibits a constant false alarm rate property in Gaussian disturbance and demonstrates strong robustness against target steering vector mismatch.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Computer Science, Information Systems
Qi Hu, Yaobo Liu, Ruoxin Mao, Chaoqun Yang
Summary: This paper focuses on the target detection problem in marine wireless sensor networks, by incorporating the Poisson point process model to describe the distances from sensors to targets and establishing the relationship between sensors and targets from the perspective of detection probabilities. It derives a new consistent and conservative target detection probability evaluation within a CFAR framework, and further develops the global detection probability of the entire network on the target. The rationality of this modeling approach is demonstrated through simulation results.
Article
Computer Science, Artificial Intelligence
Jun Liu, Zengfu Hou, Wei Li, Ran Tao, Danilo Orlando, Hongbin Li
Summary: This article discusses anomaly detection in hyperspectral imagery using two adaptive detectors, both of which are found to be equivalent. Analytical expressions for the false alarm probability of the detectors are derived, showing a constant false alarm rate against the noise covariance matrix. Experimental results demonstrate that the proposed detector performs better than its counterparts in detecting anomalies in real hyperspectral data sets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Baiqiang Zhang, Jie Zhou, Junhao Xie, Wei Zhou
Summary: This paper proposes a novel CFAR detector for Weibull background with known shape parameter, based on a robust weighted likelihood estimator with robustness to interferences, and introduces invariant theory to prove its CFAR property. Computational analysis and simulation results confirm the effectiveness and superiority of the proposed CFAR detector.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Engineering, Aerospace
Mengru Sun, Weijian Liu, Jun Liu, Peiqin Tang, Chengpeng Hao
Summary: This article discusses the problem of adaptive detection of a multichannel subspace signal in the presence of constrained interference. The gradient test is derived and found to have the same form as the existing subspace-based generalized likelihood ratio test (SGLRT). The statistical performance of the SGLRT in the presence of orthogonal interference is also derived, showing that both orthogonal interference and signal mismatch can degrade the detection performance of the detectors.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Wei Rong-Yu, Li Jun, Zhang Da-Ming, Wang Wei-Hao
Summary: Entangled state quantum detection is a promising quantum technology that has the potential to greatly improve target detection. This paper proposes a method to achieve a constant false alarm rate in the entangled state quantum detection system, by utilizing real-time noise estimation and adaptive threshold adjustment.
ACTA PHYSICA SINICA
(2022)
Article
Computer Science, Information Systems
Liang Chen, Jun Liu, Weidong Chen, Bo Du
Summary: In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. By jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Engineering, Aerospace
Sudan Han, Yuxuan Zhang, Chengpeng Hao, Jun Liu, Alfonso Farina, Danilo Orlando
Summary: In this article, innovative classification schemes are proposed to identify radar operating scenarios in terms of clutter properties. Decision rules based on penalized log-likelihood ratio test are designed by considering the sparsity of the observed scene. The performance analysis shows the effectiveness of the proposed classifiers. Radar systems can benefit from these classification results to improve detection performance.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Automation & Control Systems
Jian Xue, Mengling Sun, Jun Liu, Shuwen Xu, Meiyan Pan
Summary: This paper addresses the problem of robust and accurate estimation of shape parameters in the maritime radar industry. Outliers caused by non-sea-surface echoes greatly affect estimation accuracy. To improve performance, two neural networks, BPFFNN-eta and MBP-FFNN-eta, are proposed. BPFFNN-eta learns the mathematical relationship between shape parameters and a ratio of two percentiles, while MBP-FFNN-eta handles dynamic changes in the outlier count.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Qing Jiang, Yuntao Wu, Weijian Liu, Daikun Zheng, Tao Jian, Pengcheng Gong
Summary: This paper addresses the problem of detecting distributed targets in unknown compound-Gaussian clutter using radar. The authors propose two detectors based on Rao and Wald tests, with fixed covariance matrix, derived using order statistics theory. The unknown covariance matrix is estimated using the approximate maximum likelihood (AML) estimation. Numerical examples using simulated and real data demonstrate the superior detection performance of the Wald test-based detector compared to existing detectors.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Computer Science, Information Systems
Zhengjie Li, Junwei Xie, Weijian Liu, Haowei Zhang
Summary: The article develops an efficient resource optimization strategy in a phased array radar network to counter active oppressive interference. By dynamically assigning tracking tasks and allocating transmit power, the performance of target tracking accuracy is improved. The proposed strategy is demonstrated to be effective through numerical simulations.
IEEE SYSTEMS JOURNAL
(2023)
Review
Engineering, Civil
Jules Karangwa, Jun Liu, Zixuan Zeng
Summary: Nowadays, there is a growing presence of vehicles with high levels of automation. Efficient and accurate vehicle detection is crucial for the environment perception of autonomous vehicles. This comprehensive review evaluates various methods and datasets for vehicle detection in autonomous driving, covering over 300 research works. The review aims to assist researchers interested in autonomous driving, particularly in vehicle detection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Cai Wen, Yan Huang, Le Zheng, Weijian Liu, Timothy N. Davidson
Summary: This article proposes a transmit waveform design technique for dual-function radar-communication systems. The design focuses on optimizing the integrated main-lobe-to-sidelobe ratio and waveform similarity metric while ensuring a predefined signal-to-interference-plus-noise ratio for each communication user. Practical constraints such as per-antenna power and peak-to-average-power ratio are also considered. Numerical examples show that the proposed technique provides a superior performance tradeoff between sensing and communication compared to conventional nonlinear precoding.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Weijian Liu, Jun Liu, Tao Liu, Hui Chen, Yong-Liang Wang
Summary: This paper proposes a two-step method called Interference Cancellation before Detection (ICBD) to address the problem of signal detection in unknown Gaussian noise and subspace interference. The method involves projecting the test and training data to an interference-orthogonal subspace and utilizing traditional adaptive detector design ideas. ICBD-based detectors can function with minimal training data and have additional benefits of lower computational burden and proper operation with interference present in the training data.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Liang Chen, Jun Liu, Siyu Sun, Weidong Chen, Bo Du, Rong Liu
Summary: In this article, a generalized likelihood ratio test (GLRT)-based multipixel target detector for hyperspectral imagery (HSI) is proposed. It improves the detection performance by jointly exploiting the pixels occupied by a target of interest. However, a pixel selection problem still exists in practice. To address this issue, an adaptive target pixel selection method based on spectral similarity and spatial connectivity characteristics is proposed. Additionally, a method to collect the pixels spatially closest to the target pixels as the training background pixels is proposed.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Xiaoqiang Hua, Linyu Peng, Weijian Liu, Yongqiang Cheng, Hongqiang Wang, Huafei Sun, Zhenghua Wang
Summary: This article presents a solution for detecting maritime targets in nonhomogeneous sea clutter using a class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors. The detectors utilize Hermitian positive-definite (HPD) matrices to model the sample data and estimate the clutter covariance matrix. The proposed LDA-MIG detectors show advantages over other methods in detecting maritime targets in nonhomogeneous sea clutter, as demonstrated by numerical results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Yutong Feng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki, Jun Liu
Summary: This letter investigates the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under the Bernoulli-Gaussian model. It proposes a new probability constraint estimator and shows that it is statistically equivalent to existing detection methods with a simpler structure.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Haiyou Qu, Shiyuan Li, Chang Chen, Jun Liu, Weidong Chen
Summary: This paper discusses the potential of electromagnetic vortex waves in high-resolution radar imaging and addresses the issue of Bessel function modulation effect on imaging performance. A novel imaging algorithm based on joint low-rank and sparse constrained representation is proposed, and experimental results validate its effectiveness.
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES
(2023)
Article
Geochemistry & Geophysics
Jian Xue, Hongen Li, Meiyan Pan, Jun Liu
Summary: This article addresses the detection problem of a moving point-like target in correlated non-Gaussian sea clutter. Two adaptive persymmetric coherent detectors are proposed to improve the detection performance in sample-starved environments. The detectors utilize the persymmetric structure to transform the original radar data and employ the two-step generalized likelihood ratio test for detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Zirui Chen, Alei Chen, Weijian Liu, Xiaoyan Ma
Summary: In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. The objective function is formulated in the Bayesian framework, with regularization terms used to regularize the function. The proposed detector significantly improves the probability of detection, especially in the case of multitarget and clutter edge.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
