Signal Processing

Reconstruction of distorted signals (RODS), initially proposed in the frequency domain (FD), has been proved to be with high efficiency in depressing the distortion caused by the nonlinear power amplifier (PA). However, multiple high-dimensional ...

In numerous applications, such as DNA microarrays, face recognition, and spectral unmixing, we need to acquire a non-negative K-sparse signal x from an underdetermined linear model y = A x + v, where A is a sensing matrix and v is a noise vector. ...

• Efficient hard-thresholding-type algorithm for non-negative sparse recovery.
• Sufficient conditions for stable recovery with RHT based on RIP and mutual coherence.
• RHT achieves good recovery performance in both stimulated and real-...

Pulse characterization in ultra-fast optics presents a powerful motivation to study phase retrieval problems of high order. Frequency- and time-resolved techniques for pulse characterization both construct measurements that depend on the ...

• Optical pulse characterization is motivation for high order phase retrieval.
• Wirtinger gradient techniques are effective for bivariate pulse recovery problems.
• Spectral initializers fall short as an effective initializer for pulse ...

• A variational Bayesian based cubature Kalman filter (VBCKF-R) is derived.
• An adaptive robust nonlinear filtering framework is proposed based on the variational Bayesian (VB) method and generalized maximum likelihood estimation (GM ...

An adaptive robust nonlinear filtering framework is proposed based on the variational Bayesian (VB) method and generalized maximum likelihood estimation (GM estimation) to simultaneously handle the outliers and uncertain measurement noise ...

Multi-object elliptic localization is a challenging problem, considering that the association between the indirect path measurements and the objects is not known, and the transmitter is un-coordinated without synchronization with the receivers. ...

• For the multi-object elliptic localization problem without synchronization between the transmitter and receivers, we propose an MMS method and a BLUE to obtain the estimates of the transmitter position and range offset.
• We propose a ...

When handling large-scale multimodal data, hashing-based retrieval methods have gained significant attention due to their advantages in terms of low storage consumption and quick query speed. Despite the good performance of supervised hashing ...

• Preserving the reconstruction residual of data when learning two different latent semantic spaces.
• Aligning the latent spaces of different modalities by exploiting the consistency among modalities.
• Implementing a large number of ...

Hyperspectral image (HSI) denoising is an essential preprocessing step for improving HSI applications. Recently, subspace-based nonlocal low-rank approximation (SNLR) methods have shown their superiority. However, most of these methods ignore ...

• A novel insight is proposed for HSI denoising by incorporating guidance information.
• This is the first attempt to incorporate guidance information into nonlocal methods.
• The auxiliary information is used to guide the restoration of ...

Sparse arrays possess attractive features compared to uniform linear arrays (ULAs) with increased degrees of freedom (DOFs), reduced mutual coupling, and enlarged array aperture, which improves the estimation performance of arrays. Recently, ...

• A fragmented coprime array concept is introduced to enlarge array aperture.
• Provide the location of holes in the difference coarray of FCAs.
• Two missing interlayer subarrays fills the holes in the difference coarray of FCAs.
• ...

Acoustic beamforming methods based on microphone arrays have been widely used for sound source localization in various industrial fields. The conventional methods such as Delay and Sum (DAS) beamforming are limited by poor spatial resolution ...

• The propose DAMAS-FISTA-LASSO algorithm can significantly reduce the runtime requirement of DAMAS.
• We further design DFLNet, which adopts the structure of the DAMAS-FISTA-LASSO and inherits the relevant acoustic domain knowledge.
• ...

Recent studies have revealed that joint priors, such as joint sparsity and external nonlocal self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in various image inverse problems. Few works, however, make ...

• A new LRENSS prior that jointly exploits low-rank and ENSS priors is proposed.
• We develop a block coordinate descent method to solve resulting optimization problem.
• Experimental results demonstrate that our proposed method is ...

• The TLS-APL algorithm is proposed by using the gradient descent method and the unconstrained optimization method, which solves the problem of overall performance degradation of the APL algorithm in the EIV model.
• By using some common ...

In the errors-in-variables (EIV) model, the steady state characteristics of the affine-projection-like (APL) algorithm are poor. Hence, the total least squares APL (TLS-APL) algorithm different from the bias-compensated is proposed, which can ...

• A Multiway Fusion Module improves the accuracy of semantic segmentation.
• A multi-path semantic segmentation network with guided upsampling is proposed.
• Information in different paths can enhance the segmentation effect.
• The ...

To effectively solve the problems of intra-class dissimilarity and inter-class similarity, this study proposes a deep learning semantic segmentation model that fuses multiple path features. It utilizes Multipath Fusion Module (MFM) to extract ...

• The multi-peak noise is modeled as an MPD and classified into three types.
• An adaptive filtering algorithm called MPD is proposed under multi-peak noise.
• The MPD algorithm outperforms other commonly used algorithms under multi-peak ...

In this paper, we assume the multi-peak noise in adaptive filtering follows a multimodal probability distribution (MPD), and model it as a Gaussian mixture model (GMM). Then a new gradient ascent algorithm is proposed based on the maximum ...

Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high-resolution images and videos. In this paper, we present an efficient in-situ method ...

• It formulates a multi-layer Bayesian hierarchical model to enable in-situ compression.
• It integrates feature adaptivity into the wavelet-based image processing framework.
• It upholds computational scalability and ease of adjustment.

A constrained squared sine derived adaptive (CSSDA) algorithm is proposed in this paper, which provides better steady-state behavior than existing algorithms in impulsive noise environments. The devised CSSDA works by constructing a squared sine ...

Theory and algorithms are developed for detecting changes in the distribution of statistically periodic random processes. The statistical periodicity is modeled using independent and periodically identically distributed processes, a new class of ...

• Proposed a new model for time-series data showing statistically periodic behavior.
• Obtained optimal algorithms for quickest detection of changes in these new models.
• Applied the developed algorithms to multi-modal data collected ...

Multi-view clustering is a powerful technique that leverages both consensus and complementary information from multiple perspectives to achieve impressive results. However, existing methods have not fully explored the inherent structural ...

• Our method fully explores the inherent structural information in multi-view data.
• The augmented view, i.e., concatenated view, maximizes the consistency properties.
• Our method adopts an auto-weighted strategy to assign ideal ...

Information about the earth’s surface is difficult to capture in remote sensing images because bad weather greatly curtails visibility and diminishes visual contrast in the images. For the purpose of military survey and aerial surveillance, these ...

• Adverse weather is eliminated from remote sensing images to enhance visibility.
• The recovery of a dehazed remote sensing image depends on accurate depth measurement.
• To correctly estimate depth, a guided filter method built on ...

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology – dubbed LC 2.0 – with significantly ...

• Communication-efficient method for distributed particle filtering in sensor networks.
• Multiple methodological improvements of the likelihood consensus technique.
• Detailed simulation study demonstrating the effects of the proposed ...

In this paper we address estimation and detection problems using multiple multichannel observations drawn from a matrix-variate Student distribution, also referred to as uncorrelated t distribution. In a first part we derive the Fisher ...

• We address estimation and detection problems using Student distribution multichannel observations
• We derive the Fisher information matrix.
• We derive the GLR, Rao, Wald and gradient tests.

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC)...

• A general signal model of vertical EPC array radar is established.
• Clutter properties are analyzed in the vertical spatial frequency domain.
• Range-ambiguous clutter is separated using element-pulse decoding and pre-filtering.
• ...