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Jun Liu - IEEE Xplore Author Profile

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In this article, we address the problem of clutter covariance matrix estimation for radar adaptive detection. Traditional estimation methods are usually based on specific models. However, performance will experience degradation in the presence of model mismatch, which occurs commonly in reality. Therefore, we resort to the data-driven deep learning method and construct a network based on the convo...Show More
In this paper, we address the problem of polarimetric detection of a target with energy spillover in Gaussian environment. We adopt full polarized channels and use diverse criteria to design detectors, resulting in five new adaptive detectors. Performance evaluation reveals that the derived detectors gain higher probabilities of detection (PD) and better localization abilities, compared to the det...Show More
Target detection is a critical aspect in the practical application of hyperspectral images. Recently, sparse representation has garnered significant attention in this field. The underlying assumption of the sparse representation model is that each pixel can be linearly and sparsely represented by a combination of the prior target and background spectra. However, detection algorithms based on spars...Show More
This paper studies the joint estimation of range and velocity of a target in multi-target environments by using automotive frequency modulated continuous wave radar. First, considering the single target case, the maximum likelihood criterion is employed to convert the estimation problem into an optimization problem. In order to find the solution of this optimization problem, Newton-backtracking li...Show More
The vortex wave featuring specific wavefronts and orthogonal orbital angular momentum (OAM) modes has demonstrated broad prospects in radar forward-looking imaging applications. However, the Bessel function modulation effect arising from the physical property of vortex waves and the limited OAM modes affect the imaging performance. To solve this issue, we present an innovative vortex wavefront mod...Show More
In this letter, the problem of detecting a multiple subspace-based target in the presence of deterministic interference is considered. To solve the problem, we utilize the Kullback-Leibler information criterion and model order selection rules to design detection schemes. The alternative hypothesis related to the most likely signal subspace is selected from multiple alternative hypotheses, and is t...Show More
In this paper, a method for double-knowledge (DK)-aided Rao and Wald tests under training-limited condition is designed, where both information of the prior distribution and persymmetric structure are utilized. A two-step way is used to realize the D K -aided tests, including the first step of deriving the DK-aided Rao and DK-aided Wald statistics under the assumption of the known covariance matri...Show More
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 structure of clutter covariance matrix, but with varying and unknown power mismatches. To address this problem, we iteratively estimate all the unknowns, including the coordinate matrix of the target, the clutter covariance ma...Show More
In this article, we investigate the problem of distributed target adaptive detection in the presence of deterministic subspace interference and Gaussian noise, wherein the target signal and interference are assumed to lie in independent subspaces, and a set of independent and identically distributed training samples is used to learn the noise covariance matrix. In the context of the above assumpti...Show More
We consider a mixed analog-to-digital converter (ADC) based architecture consisting of high-precision and one-bit ADCs with the antenna-varying threshold for direction of arrival (DOA) estimation using a uniform linear array (ULA), which utilizes fixed but different thresholds for one-bit ADCs across different receive antennas. The Cramér-Rao bound (CRB) with the antenna-varying threshold is obtai...Show More
This article investigates the problem of adaptive detection of point targets against compound-Gaussian (CG) clutter with inverse gamma (IG) texture. Under the subspace signal model, we first propose adaptive detectors without signal mismatch based on the one-step generalized likelihood ratio test (1S-GLRT), maximum a posteriori Rao (MAP-Rao), MAP-Wald, MAP-Gradient, and MAP-Durbin criteria. These ...Show More
In the case of distributed target detection in unknown Gaussian noise, training data are often limited, and signal mismatch is a common issue. To tackle these problems, we utilize the Bayesian theory by taking the noise covariance matrix as an inverse Wishart distribution. Our approach involves incorporating a fictitious determinist jamming signal in the signal-absence hypothesis to create a selec...Show More
Radar sensors are vital for autonomous driving due to their consistent and dependable performance, even in challenging weather conditions. Semantic segmentation of moving objects in sparse radar point clouds is an emerging task that contributes to improving the safety of autonomous driving. However, the methods still need to be explored since radar points in driving scenes are distributed sparsely...Show More
This article deals with the problem of tracking targets with X-band marine radars in the complicated sea clutter background. By jointly exploiting the target’s kinematic and appearance information, we propose a novel dynamic model-based Doppler-adaptive correlation filter (DDACF). The proposed tracker mainly consists of two modules. First, a DACF is developed based on the kernel correlation filter...Show More
Point Cloud Registration (PCR) has been viewed as an essential part of photogrammetry, remote sensing, and autonomous robot mapping. Existing methods are either sensitive to rotation transformations, or rely on feature learning networks with poor generalization. We propose a novel outdoor point cloud registration algorithm, including preprocessing, yaw angle estimation, coarse registration, and fi...Show More
The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) has shown significant potential in high-resolution radar imaging. However, the Bessel function modulation (BFM) effect caused by the vortex wave’s physical nature influences the imaging performance, especially under the limited OAM modes. To address this issue, we first establish a computationally efficient 2-D vortex rad...Show More
Hyperspectral anomaly detection, which is aimed at distinguishing anomaly pixels from the surroundings in spatial features and spectral characteristics, has attracted considerable attention due to its various applications. In this article, we propose a novel hyperspectral anomaly detection algorithm based on adaptive low-rank transform, in which the input hyperspectral image (HSI) is divided into ...Show More
With the development of hyperspectral imaging technology, the hyperspectral anomaly has attracted considerable attention due to its significant role in many applications. Hyperspectral images (HSIs) with two spatial dimensions and one spectral dimension are intrinsically three-order tensors. However, most of the existing anomaly detectors were designed after converting the 3-D HSI data into a matr...Show More
In this article, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. To reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix...Show More
We consider a mixed analog-to-digital converter (ADC) based architecture for the direction of arrival (DOA) estimation under sparse linear arrays (SLAs) with arbitrary structure. Given the fixed number of ADCs, the arrangement of high-precision and one-bit ADCs in SLA is analyzed using Cramér-Rao bound (CRB). To obtain the optimal mixed-precision arrangement, we simplify the problem into a 0–1 int...Show More
In this letter, we investigate the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under Bernoulli–Gaussian model. In addition to the generalized likelihood ratio test (GLRT) proposed in (Hariri and Babaie–Zadeh et al., 2017), the corresponding Rao test and Wald test are derived in this letter. By observing that obtaining their analytical performance is chall...Show More
This article deals with the detection problem of a moving point-like target in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with a lognormal-distributed texture and an unknown covariance matrix. To improve the detection performance for radar targets in sample-starved environments where the number of secondary data is limited, the persymmetric structure is expl...Show More
Nowadays, vehicles with a high level of automation are being driven everywhere. With the apparent success of autonomous driving technology, we keep working to achieve fully autonomous vehicles on roads. Efficient and accurate vehicle detection is one of the essential tasks in the environment perception of an autonomous vehicle. Therefore, numerous algorithms for vehicle detection have been develop...Show More
We consider a mixed analog-to-digital converter (ADC) based architecture for direction of arrival (DOA) estimation using a uniform linear array (ULA). We derive the Cramér-Rao bound (CRB) of the DOA under the optimal time-varying threshold, and find that the asymptotic CRB is related to the arrangement of high-precision and one-bit ADCs for a fixed number of ADCs. Then, a new concept called "mixed...Show More
It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interference is frequently present, which complicates the detecting issue. We provide a two-step method, termed interference cancellation before detection (ICBD), to address the issue of signal detection in the unknown Gaussian no...Show More