107 citations · 322 across the 11 of their papers we have counts for
11 papers
Adaptive Subspace Signal Detection and Performance Analysis in Nonzero-Mean Clutter
Weijian Liu, Zhenyu Xu, Jun Liu +2
To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao…
Statistical Performance of Generalized Direction Detectors with Known Spatial Steering Vector
Zhenyu Xu, Weijian Liu, Changfei Wu +2
The generalized direction detection (GDD) problem involves determining the presence of a signal of interest within matrix-valued data, where the row and column spaces of the signal…
Bayesian Rao test for distributed target detection in interference and noise with limited training data
Daipeng Xiao, Weijian Liu, Jun Liu +3
This paper has studied the problem of detecting a range-spread target in interference and noise when the number of training data is limited. The interference is located within a ce…
Mask-RadarNet: Enhancing Transformer With Spatial-Temporal Semantic Context for Radar Object Detection in Autonomous Driving
Yuzhi Wu, Jun Liu, Guangfeng Jiang +2
As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years, making it an appealing complement to commonly used sensors like camer…
Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter
Daipeng Xiao, Weijian Liu, Jun Liu +3
This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical struct…
LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter
Xiaoqiang Hua, Linyu Peng, Weijian Liu +4
This paper deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where limited number of secondary data is available due to the heterogeneity…
Detector Design and Performance Analysis for Target Detection in Subspace Interference
Weijian Liu, Jun Liu, Tao Liu +2
It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interferen…
Detection of a rank-one signal with limited training data
Weijian Liu, Zhaojian Zhang, Jun Liu +2
In this paper, we reconsider the problem of detecting a matrix-valued rank-one signal in unknown Gaussian noise, which was previously addressed for the case of sufficient training…
Multichannel adaptive signal detection: Basic theory and literature review
Weijian Liu, Jun Liu, Chengpeng Hao +2
Multichannel adaptive signal detection jointly uses the test and training data to form an adaptive detector, and then make a decision on whether a target exists or not. Remarkably,…
Multichannel signal detection in interference and noise when signal mismatch happens
Weijian Liu, Jun Liu, Yongchan Gao +2
In this paper, we consider the problem of detecting a multichannel signal in interference and noise when signal mismatch happens. We first propose two selective detectors, since th…
Sparsity-Based STAP Design Based on Alternating Direction Method with Gain/Phase Errors
Zhaocheng Yang, Rodrigo C. de Lamare, Weijian Liu
We present a novel sparsity-based space-time adaptive processing (STAP) technique based on the alternating direction method to overcome the severe performance degradation caused by…