arXiv subjects

Shengyao Chen

Publications and source records attributed to Shengyao Chen.

At least 19 recordsLinked to original sources

arXiv2026

Fundamental Limits of Adaptive Beamforming Under Finite Training

Finite training reduces the output signal-to-interference-plus-noise ratio (SINR) of an adaptive beamformer, and a natural question is how much of this loss is unavoidable. This paper determines this question, providing a beamforming counterpart of the Cram\'er--Rao bound in spectral estimation. An exact identity expresses the SINR loss as a bounded function of the error in the clairvoyant minimum-variance distortionless-response (MVDR) weight. It yields a local asymptotic minimax lower bound over all measurable data-dependent beamforming rules, including biased and irregular rules. The first-order coefficient is $\tr(\Mb\Jb_{\rm eff}^{-1})$, where $\Jb_{\rm eff}$ describes the information in the training data and $\Mb$ measures the sensitivity of the output SINR. Matching constructions determine this coefficient in two complex-Gaussian models. For an $N$-sensor uniform linear array with $K$ distinct point interferers and $2K+1\le N$, a data-driven split one-step beamformer attains the coefficient $C_\theta\le K$ at every interior scene of a fixed compact regular parameter set. For unrestricted covariance matrices, sample matrix inversion (SMI) attains the coefficient $N-1$ through the classical Reed--Mallett--Brennan law. The difference quantifies the first-order value of finite-source structure. Geometric formulas and numerical results describe the dependence on interference power and array geometry, and the finite-sample departure near a weak-source boundary.

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arXiv2026

FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On

Video virtual try-on aims to transfer a target garment onto a moving person across video frames. Current methods rely on human parsing masks or pose keypoints that frequently fail under large motions and occlusions, causing boundary artifacts and temporal inconsistency. A further limitation is that most approaches rely solely on attention mechanisms for temporal modeling, providing no explicit motion supervision. We propose FlowVVTON, a mask-free framework that eliminates parsing mask dependency entirely. Optical flow is used solely as a training-time supervision signal: a flow-warped latent loss, applied across all layers of the generation model, enforces multi-scale temporal consistency by aligning adjacent-frame features under explicit physical motion constraints. A two-stage training strategy establishes mask-free spatial alignment before introducing flow-guided temporal supervision. Experiments on TikTokDress show that FlowVVTON outperforms baselines by substantial margins, particularly in temporal consistency (5.7$\times$ VFID-R improvement over SwiftTry), while requiring no segmentation masks, pose keypoints, or region annotations at any stage.

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arXiv2026

Fixed-Threshold One-Bit Toeplitz Covariance Estimation under Sparse-Ruler Sampling

We estimate the Toeplitz covariance matrix of a centered Gaussian distribution from data that are both coarsely quantized and sparsely sampled. Only the coordinates of a sparse ruler are recorded, and each recorded value is kept as a single bit: the sign of its comparison with a fixed threshold. Such data arise in low-precision sensing front ends and sparse sensor arrays. Because the threshold is nonzero, every bit has a common mean. Each bit is also reused across many of the products that build the covariance, so one bit's error enters many of them. Centering removes the shared error. We prove a Gaussian variance contraction theorem for products of a centered, bounded nonlinearity of a Gaussian vector, the non-smooth one-bit sign included; it sets each lag's variance by how well the ruler covers that lag. The resulting estimator needs neither the signal scale nor the bit mean in advance, since the nonzero threshold makes both identifiable from the marginal bits. A matching minimax lower bound shows the resulting coverage rate is optimal up to constants over a neighborhood of white noise; the bound holds even for the unquantized real-valued samples, so one-bit quantization costs only a constant factor.

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arXiv2026

VTONGuard: Automatic Detection and Authentication of AI-Generated Virtual Try-On Content

With the rapid advancement of generative AI, virtual try-on (VTON) systems are becoming increasingly common in e-commerce and digital entertainment. However, the growing realism of AI-generated try-on content raises pressing concerns about authenticity and responsible use. To address this, we present VTONGuard, a large-scale benchmark dataset containing over 775,000 real and synthetic try-on images. The dataset covers diverse real-world conditions, including variations in pose, background, and garment styles, and provides both authentic and manipulated examples. Based on this benchmark, we conduct a systematic evaluation of multiple detection paradigms under unified training and testing protocols. Our results reveal each method's strengths and weaknesses and highlight the persistent challenge of cross-paradigm generalization. To further advance detection, we design a multi-task framework that integrates auxiliary segmentation to enhance boundary-aware feature learning, achieving the best overall performance on VTONGuard. We expect this benchmark to enable fair comparisons, facilitate the development of more robust detection models, and promote the safe and responsible deployment of VTON technologies in practice.

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arXiv2024

Transmit Beampattern Synthesis for Active RIS-Aided MIMO Radar via Waveform and Beamforming Optimization

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.

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arXiv2024

Reconfigurable Intelligent Surface-Enabled Array Radar for Interference Mitigation

Conventional active array radars often jointly design the transmit and receive beamforming for effectively suppressing interferences. To further promote the interference suppression performance, this paper introduces a reconfigurable intelligent surface (RIS) to assist the radar receiver because the RIS has the ability to bring plentiful additional degrees-of-freedom. To maximize the output signal-to-interference-plus-noise ratio (SINR) of receive array, we formulate the codesign of transmit beamforming and RIS-assisted receive beamforming into a nonconvex constrained fractional programming problem, and then propose an alternating minimization-based algorithm to jointly optimize the transmitor beamfmer, receive beamformer and RIS reflection coefficients. Concretely, we translate the RIS reflection coefficients design into a series of unimodular quadratic programming (UQP) subproblems by employing the Dinkelbach transform, and offer the closed-form optimal solutions of transmit and receive beamformers according to the minimum variance distortionless response principle. To tackle the UQP subproblems efficiently, we propose a second-order Riemannian Newton method (RNM) with improved Riemannian Newton direction, which avoids the line search and has better convergence speed than typical first-order Riemannian manifold optimization methods. Moreover, we derive the convergence of the proposed codesign algorithm by deducing the explicit convergence condition of RNM. We also analyze the computational complexity. Numerical results demonstrate that the proposed RIS-assisted array radar has superior performance of interference suppression to the RIS-free one, and the SINR improvement is proportional to the number of RIS elements.

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arXiv2023

TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs

Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays. The substantial quantization distortion complicates the extraction of signal and noise subspaces from the quantized data. To address this issue, this paper introduces a novel approach that leverages the Transformer model to aid the subspace estimation. In this model, multiple snapshots are processed in parallel, enabling the capture of global correlations that span them. The learned subspace empowers us to construct the MUSIC spectrum and perform gridless DOA estimation using a neural network-based peak finder. Additionally, the acquired subspace encodes the vital information of model order, allowing us to determine the exact number of sources. These integrated components form a unified algorithmic framework referred to as TransMUSIC. Numerical results demonstrate the superiority of the TransMUSIC algorithm, even when dealing with one-bit quantized data. The results highlight the potential of Transformer-based techniques in DOA estimation.

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arXiv2023

LiQuiD-MIMO Radar: Distributed MIMO Radar with Low-Bit Quantization

Distributed MIMO radar is known to achieve superior sensing performance by employing widely separated antennas. However, it is challenging to implement a low-complexity distributed MIMO radar due to the complex operations at both the receivers and the fusion center. This work proposed a low-bit quantized distributed MIMO (LiQuiD-MIMO) radar to significantly reduce the burden of signal acquisition and data transmission. In the LiQuiD-MIMO radar, the widely-separated receivers are restricted to operating with low-resolution ADCs and deliver the low-bit quantized data to the fusion center. At the fusion center, the induced quantization distortion is explicitly compensated via digital processing. By exploiting the inherent structure of our problem, a quantized version of the robust principal component analysis (RPCA) problem is formulated to simultaneously recover the low-rank target information matrices as well as the sparse data transmission errors. The least squares-based method is then employed to estimate the targets' positions and velocities from the recovered target information matrices. Numerical experiments demonstrate that the proposed LiQuiD-MIMO radar, configured with the developed algorithm, can achieve accurate target parameter estimation.

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arXiv2023

Time Encoding Sampling of Bandpass Signals

This paper investigates the problem of sampling and reconstructing bandpass signals using time encoding machine(TEM). It is shown that the sampling in principle is equivalent to periodic non-uniform sampling (PNS). Then the TEM parameters can be set according to the signal bandwidth and amplitude instead of upper-edge frequency and amplitude as in the case of bandlimited/lowpass signals. For a bandpass signal of a single information band, it can be perfectly reconstructed if the TEM parameters are such that the difference between any consecutive values of the time sequence in each channel is bounded by the inverse of the signal bandwidth. A reconstruction method incorporating the interpolation functions of PNS is proposed. Numerical experiments validate the feasibility and effectiveness of the proposed TEM scheme.

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arXiv2022

Space-Time Adaptive Processing Using Random Matrix Theory Under Limited Training Samples

Space-time adaptive processing (STAP) is one of the most effective approaches to suppressing ground clutters in airborne radar systems. It basically takes two forms, i.e., full-dimension STAP (FD-STAP) and reduced-dimension STAP (RD-STAP). When the numbers of clutter training samples are less than two times their respective system degrees-of-freedom (DOF), the performances of both FD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. To enhance STAP performance under the limited training samples, this paper develops a STAP theory with random matrix theory (RMT). By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, and thus producing the optimal STAP weight vector. Two STAP algorithms, FD-STAP using RMT (RMT-FD-STAP) and RD-STAP using RMT (RMT-RD-STAP), are proposed. It is found that both RMT-FD-STAP and RMT-RD-STAP greatly outperform other-related STAP algorithms when the numbers of training samples are larger than their respective clutter DOFs, which are much less than the corresponding system DOFs. Theoretical analyses and simulation demonstrate the effectiveness and the performance advantages of the proposed STAP algorithms.

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arXiv2020

On the Mutual Interference between Spaceborne SARs: Modeling, Characterization, and Mitigation

Since by the International Telecommunications Union (ITU) regulatory the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of a SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR's antenna, causing radiometric artefacts in the focused image. This paper tries to model and characterize the artefacts, and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artefact, which reveals that the mutual interference can introduce a two-dimensional LFM radiometric artefact in image domain with a limited spatial extent. We show that the artefact is low-rank based on range-azimuth decoupling analysis and two-dimensional high-order Taylor expansion. Based on the low rank model, we show that two methods, i.e., principle component analysis and its robust variant, can be adopted to efficiently mitigate the artefact via processing in image domain. The former method has the advantage of fast processing speed, for example, a sub-swath of Sentinel-1 interferometric wide swath image can be processed within 70 seconds via block-wise operation, whereas the latter provides improved accuracy for sparse point-like scatterers. Experiment results demonstrate that the radiometric artefacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.

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arXiv2020

Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements

Sparse array arrangement has been widely used in vector-sensor arrays because of increased degree-of-freedoms for identifying more sources than sensors. For large-size sparse vector-sensor arrays, one-bit measurements can further reduce the receiver system complexity by using low-resolution ADCs. In this paper, we present a sparse cross-dipole array with one-bit measurements to estimate Direction of Arrivals (DOA) of electromagnetic sources. Based on the independence assumption of sources, we establish the relation between the covariance matrix of one-bit measurements and that of unquantized measurements by Bussgang Theorem. Then we develop a Spatial-Smooth MUSIC (SS-MUSIC) based method, One-Bit MUSIC (OB-MUSIC), to estimate the DOAs. By jointly utilizing the covariance matrices of two dipole arrays, we find that OB-MUSIC is robust against polarization states. We also derive the Cramer-Rao bound (CRB) of DOA estimation for the proposed scheme. Furthermore, we theoretically analyze the applicability of the independence assumption of sources, which is the fundamental of the proposed and other typical methods, and verify the assumption in typical communication applications. Numerical results show that, with the same number of sensors, one-bit sparse cross-dipole arrays have comparable performance with unquantized uniform linear arrays and thus provide a compromise between the DOA estimation performance and the system complexity.

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arXiv2020

Non-Common Band SAR Interferometry via Compressive Sensing

To avoid decorrelation, conventional synthetic aperture radar interferometry (InSAR) requires that interferometric images should have a common spectral band and the same resolution after proper preprocessing. For a high-resolution (HR) image and a low-resolution (LR) one, the interferogram quality is limited by the LR one since the non-common band (NCB) between two images is usually discarded. In this article, we try to establish an InSAR method to improve interferogram quality by means of exploiting the NCB. To this end, we first define a new interferogram, which has the same resolution as the HR image. Then we formulate the interferometric relationship between the two images into a compressive sensing (CS) model, which contains the proposed HR interferogram. With the sparsity of interferogram in appropriate domains, we model the interferogram formation as a typical sparse recovery problem. Due to the speckle effect in coherent radar imaging, the sensing matrix of our CS model is inherently random. We theoretically prove that the sensing matrix satisfies restricted isometry property, and thus the interferogram recovery performance is guaranteed. Furthermore, we provide a fast interferogram formation algorithm by exploiting computationally efficient structures of the sensing matrix. Numerical experiments show that the proposed method provides better interferogram quality in the sense of reduced phase noise and obtain extrapolated interferogram spectra with respect to CB processing.

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arXiv2019

Gridless Parameter Estimation for One-Bit MIMO Radar with Time-Varying Thresholds

We investigate the one-bit MIMO (1b-MIMO) radar that performs one-bit sampling with a time-varying threshold in the temporal domain and employs compressive sensing in the spatial and Doppler domains. The goals are to significantly reduce the hardware cost, energy consumption, and amount of stored data. The joint angle and Doppler frequency estimations from noisy one-bit data are studied. By showing that the effect of noise on one-bit sampling is equivalent to that of sparse impulsive perturbations, we formulate the one-bit $\ell_1$-regularized atomic-norm minimization (1b-ANM-L1) problem to achieve gridless parameter estimation with high accuracy. We also develop an iterative method for solving the 1b-ANM-L1 problem via the alternating direction method of multipliers. The Cram$\acute{\text{e}}$r-Rao bound (CRB) of the 1b-MIMO radar is analyzed, and the analytical performance of one-bit sampling with two different threshold strategies is discussed. Numerical experiments are presented to show that the 1b-MIMO radar can achieve high-resolution parameter estimation with a largely reduced amount of data.

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arXiv2018

Radar Interferometry using Two Images with Different Resolutions

Radar interferometry usually exploits two complex-valued radar images with the same resolution to extract terrain elevation information. This paper considers the interferometry using two radar images with different resolutions, which we refer to as dual-resolution radar interferometry. We find that it is feasible to recover a high-resolution interferogram from a high-resolution image and a low-resolution one. We formulate the dual-resolution interferometry into a compressive sensing problem, and exploit the wavelet-domain sparsity of the interferogram to solve it. Due to the speckle effect in coherent radar imaging, the sensing matrix of our model is expected to have small mutual coherence, which guarantees the performance of our method. In comparison with the conventional radar interferometry methods, the proposed method reduces the resolution requirement of radar image acquisition. It therefore can promote wide coverage, low sampling/data rate and storage cost. Numerical experiments on Sentinel-1 data are made to validate our method.

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arXiv2018

Quadrature compressive sampling SAR imaging

This paper presents a quadrature compressive sampling (QuadCS) and associated fast imaging scheme for synthetic aperture radar (SAR). Different from other analog-to-information conversions (AIC), QuadCS AICs using independent spreading signals sample the SAR echoes due to different transmitted pulses. Then the resulting sensing matrix has lower correlation between any two columns than that by a fixed spreading signal, and better SAR image can be reconstructed. With proper setting of the spreading signals in QuadCS, the sensing matrix has the structures suitable for fast computation of matrix-vector multiplication operations, which leads to the fast image reconstruction. The performance of the proposed scheme is assessed using real SAR image. The reconstructed SAR images with only one-fourth of the Nyquist data achieve the image quality similar to that of the classical SAR images with Nyquist samples.

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arXiv2017

A General and Yet Efficient Scheme for Sub-Nyquist Radar Processing

We study the target parameter estimation for sub-Nyquist pulse-Doppler radar. Several past works have addressed this problem but either have low estimation accuracy for off-grid targets, take large computation load, or lack versatility for analog-to-information conversion (AIC) systems. To overcome these difficulties, we present a general and efficient estimation scheme. The scheme first formulates a general model in the sense that it is applicable to all AICs regardless of whether the targets are on or off the grids. The estimation of Doppler shifts and delays is performed sequentially, in which the Doppler estimation is formulated into a spatial spectrum estimation problem and the delay estimation is decomposed into a series of compressive parameter estimation problems with each corresponding to an estimated Doppler shift. By the sequential and decomposed processing, the computational complexity is substantially reduced, and by the parametric estimation techniques, the high accurate estimation is obtained. Theoretical analyses and numerical experiments show the effectiveness and the correctness of the proposed scheme.

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arXiv2017

A General Sequential Delay-Doppler Estimation Scheme for Sub-Nyquist Pulse-Doppler Radar

Sequential estimation of the delay and Doppler parameters for sub-Nyquist radars by analog-to-information conversion (AIC) systems has received wide attention recently. However, the estimation methods reported are AIC-dependent and have poor performance for off-grid targets. This paper develops a general estimation scheme in the sense that it is applicable to all AICs regardless whether the targets are on or off the grids. The proposed scheme estimates the delay and Doppler parameters sequentially, in which the delay estimation is formulated into a beamspace direction-of- arrival problem and the Doppler estimation is translated into a line spectrum estimation problem. Then the well-known spatial and temporal spectrum estimation techniques are used to provide efficient and high-resolution estimates of the delay and Doppler parameters. In addition, sufficient conditions on the AIC to guarantee the successful estimation of off-grid targets are provided, while the existing conditions are mostly related to the on-grid targets. Theoretical analyses and numerical experiments show the effectiveness and the correctness of the proposed scheme.

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