We consider the channel acquisition problem for a wideband terahertz (THz) communication system, where an extremely large-scale array is deployed to mitigate severe path attenuation. In channel modeling, we account for both the spherical wavefront and beam-splitting phenomena of the wide-band near-field channel. We propose a frequency-independent orthogonal dictionary that generalizes the standard discrete Fourier transform (DFT) matrix by introducing an additional parameter to capture near-field effects. This dictionary enables an efficient two-dimensional (2D) block-sparse representation of the wideband near-field channel. By leveraging this structured sparsity, the wideband near-field channel estimation problem can be effectively solved within a customized compressive sensing framework. Numerical results demonstrate the significant advantages of our proposed 2D block-sparsity-aware method over conventional polar-domain-based approaches for near-field wideband channel estimation.

Extremely large antenna arrays (ELAAs) are widely adopted in mmWave/THz communications to compensate for the severe path loss, wherein the channel estimation remains a significant challenge since the Rayleigh distance of ELAAs stretches to tens or even hundreds of meters and the near-field channel model should be considered. Existing polar-domain based methods and block-sparse based methods are originally devised for Uniform Linear Arrays (ULAs) near-field channel estimation. The polar-domain based method can be applied to Uniform Planar Arrays (UPAs), but it behaves plain since it ignores the specific sparsity structure of the UPA near-field channels. Meanwhile, the block-sparse based method cannot be extended to the UPA scenarios directly. To address these issues, we first reformulate the original UPA near-field channel as an outer product of two ULA near-field channels and we construct a modified two-dimensional DFT (2D-DFT) dictionary for it. With the proposed dictionary, we further prove that the UPA near-field channel admits a 2D block-sparse structure. Leveraging this specific sparse structure, we solve the channel estimation problem with the 2D Pattern-Coupled Sparse Bayesian Learning (2D-PCSBL) algorithm. Simulation results show that the proposed approach outperforms conventional existing methods while maintaining a comparable computational complexity.

In the paper, we consider the line spectral estimation problem in an unlimited sensing framework (USF), where a modulo analog-to-digital converter (ADC) is employed to fold the input signal back into a bounded interval before quantization. Such an operation is mathematically equivalent to taking the modulo of the input signal with respect to the interval. To overcome the noise sensitivity of higher-order difference-based methods, we explore the properties of the first-order difference of modulo samples, and develop two line spectral estimation algorithms based on the first-order difference, which are robust against noise. Specifically, we show that, with a high probability, the first-order difference of the original samples is equivalent to that of the modulo samples. By utilizing this property, line spectral estimation is solved via a robust sparse signal recovery approach. The second algorithms is built on our finding that, with a sufficiently high sampling rate, the first-order difference of the original samples can be decomposed as a sum of the first-order difference of the modulo samples and a sequence whose elements are confined to three possible values. This decomposition enables us to formulate the line spectral estimation problem as a mixed integer linear program that can be efficiently solved. Simulation results show that both proposed methods are robust against noise and achieve a significant performance improvement over the higher-order difference-based method. methods.

Trajectory reconstruction is essential for localizing and tracking maritime vehicles, but non-Gaussian process and measurement noises, as well as time-varying measurement loss, are present in the marine environment. To enhance TR accuracy in such challenging scenarios, this article proposes a state sequence estimation method based on the Rauch-Tung-Striebel smoother, which fully leverages the distributional characteristics of outliers. This approach models the likelihood density of non- Gaussian noise using two multivariate Laplace distributions. Unknown noise covariances are then modeled via an inverse Wishart distribution, while unknown measurement losses and their occurrence probabilities are addressed using Bernoulli and Beta distributions, respectively. On this basis, Variational Bayes inference is utilized to decouple fused anomalous processes, and infer states within a hierarchical model, yielding the proposed robust smoother. Experiments with an Unmanned Underwater Vehicle and numerical simulations demonstrate the effectiveness and robustness of this proposed method for TR tasks.

This paper studies wideband channel estimation for OFDM systems assisted by extremely large RIS (XL-RIS). Due to the large aperture of XL-RISs, the user equipment may operate in the near-field region, while the base station-XL-RIS link remains in the far field, leading to a cascaded channel with hybrid near-field and far-field characteristics. Moreover, wideband effects further complicate channel estimation in mmWave/THz systems. To address these challenges, we propose a frequency-independent orthogonal dictionary by augmenting the discrete Fourier transform (DFT) matrix with additional parameters, which enables an efficient representation of the wideband cascaded channel using a two-dimensional block-sparse structure. Based on this property, the considered channel estimation problem is effectively solved within a tailored compressed sensing framework. Simulation results demonstrate that the proposed method significantly outperforms conventional polar-domain channel estimation approaches in terms of estimation accuracy.

Millimeter wave/Terahertz (mmWave/THz) communication with extremely large-scale antenna arrays (ELAAs) offers a promising solution to meet the escalating demand for high data rates in next-generation communications. A large array aperture, along with the ever increasing carrier frequency over the mmWave/THz bands, leads to a large Rayleigh distance. As a result, the traditional planar-wave assumption may not hold valid for mmWave/THz systems featuring ELAAs. In this paper, we consider the problem of hybrid near/far-field channel estimation by taking spherical wave propagation into account. By analyzing the coherence properties of any two near-field steering vectors, we prove that the hybrid near/far-field channel admits a block-sparse representation on a specially designed unitary matrix. Specifically, the percentage of nonzero elements of such a block-sparse representation is in the order of 1/root N , which tends to zero as the number of antennas, N , grows. Such a block-sparse representation allows to convert channel estimation into a block-sparse signal recovery problem. Simulation results are provided to verify our theoretical results and illustrate the performance of the proposed channel estimation approach in comparison with existing state-of-the-art methods.

In this paper, we consider the problem of downlink beam training for extremely large-scale millimeter wave (mmWave)/Terahertz (THz) systems, where the far-field assumption which treats wavefronts as planar waves may not hold valid. For such hybrid far/near-field channels, beam training needs to identify the best beam alignment on a two-dimensional angle-range domain. An exhaustive search scheme sequentially scanning the entire angle-range space incurs a high training overhead. To address this issue, in this paper, we propose an efficient hybrid far/near-field beam training method. By utilizing the approximate orthogonality of near-field steering vectors of the same effective distance, we devise a multi-directional beam training sequence which can more efficiently scan the entire angle-range space. Based on the devised beam training sequence, we develop a simple estimation method at the receiver that can simultaneously identify the angle and the range associated with the dominant path. Simulation results show that the proposed method achieves better performance than the exhaustive search scheme, while with a much lower overhead cost. The proposed method also presents a clear advantage over other existing state-of-the-art hybrid far/near-field beam training methods in terms of performance and generality.

A servicing spacecraft mounted with compliant flexible devices has recently emerged as a novel and popular approach for detumbling malfunctioning satellite. Mounting numbers of works have been devoted to the detumbling dynamics and control. However, almost all studies on the dynamics do not consider the effects of the flexible panels of the satellite. In addition, since the flexible panels bring considerable influences on contact processes, the contact-induced disturbance with large amplitude easily causes the spacecraft instability, posing detrimental impacts for accurate and efficient operations. To conquer the above problems, the dynamic model of the flexible satellite is established by means of the natural coordinate and absolute nodal coordinate formulations, providing a foundation for the dynamic analysis. Besides, a novel fixed-time disturbance rejection detumbling controller is proposed based on fast terminal sliding mode control technique, wherein the fixed-time convergent observer is constructed to effectively estimate the disturbance without requiring the upper bounds of the disturbance and its derivative. This controller can significantly improve convergence performance and tracking accuracy, facilitating the spacecraft to realize accurate and efficient operations. Extensive simulations are conducted to reveal the effects of the flexible panels, and validate the effectiveness of the proposed controller.

Positioning by exploiting signals of opportunity (SOP) offers a promising alternative when GNSS signals are unreliable or unavailable. This paper investigates the use of low-Earth-orbit (LEO) satellite transmissions as SOP for positioning, formulating the problem within a nonlinear state-space framework. A cubature Kalman smoother jointly estimates the terminal position and the unknown Doppler frequency offsets of multiple LEO emitters, while an expectation maximization (EM) algorithm recursively learns the covariances of both process and measurement noise, ensuring asymptotically optimal performance. Simulation results demonstrate that the proposed method significantly outperforms recent nonlinear least-squares approaches.

This paper considers channel estimation for extremely large-scale intelligent reflecting surface (XL-IRS)-assisted terahertz (THz) communication systems. Specifically, an XL-IRS is deployed close to users (UEs) to enhance communication performance between the base station (BS) and UE. With its large aperture, the XL-IRS has a Rayleigh distance of tens of meters. Therefore, the users are likely located in the near-field region of the XL-IRS, while the BS is in its far-field region. Consequently, a spherical wavefront propagation model should be considered to characterize the propagation property between the XL-IRS and the UE, while the planar wavefront propagation model is utilized in the BS-IRS link. By leveraging Khatri-Rao product and Kronecker product properties, we rephrase the channel estimation problem. In addition, we construct an orthogonal dictionary, which essentially modifies the well-known Discrete Fourier Transform (DFT) matrix. We further find that the considered channel can be block-sparsely represented by this dictionary. Hence, the channel estimation can be converted into a block sparse recovery problem, which can be efficiently solved by several off-the-shelf methods. The simulation results show that our proposed method achieves better estimation performance than the conventional polar-domain-based method.

This paper considers the estimation of a mixture of sinusoids in an unlimited sensing framework. A modulo analog-todigital converter (ADC) is employed to fold back the input signal into a bounded interval before samples are token. We show that, for a band-limited signal, when the sampling rate satisfies a certain condition that is closely related to the dynamic range of the modulo ADC, the first-order difference of the original samples can be uniquely decomposed as a sum of the first-order difference of the modulo samples and a constant with only three possible values. This enables us to formulate the problem of estimating a mixture of sinusoids as a joint sparse signal recovery and unknown integer parameters estimation problem, which can be efficiently solved via a mixed-integer linear program. In addition, a multi-channel sampling architecture is employed to form a “virtual” modulo ADC with an enlarged dynamic range. This improvement helps reduce the sampling rate for estimating sinusoidal mixtures. Numerical simulations are conducted to illustrate the performance of the proposed method.

Atmospheric turbulence can often introduce phase errors into a propagating light field, thus resulting in anisoplanatic and temporally varying blur and distortion of images. Restoring such images degraded by atmospheric turbulence is extremely ill-posed, due to multiple plausible solutions for a given input image. Most methods offer a deterministic estimation of clean images and require high-computational costs. To address these challenges, this article proposes a fast turbulence mitigation network (FTMNet). It is a lightweight model for atmospheric turbulence mitigation. Differing other methods, it does not employ a strategy for producing a single deterministic reconstruction. Instead, it leverages the Monte Carlo method to enhance restoration performance and produces a different and reasonable set of reconstructed images for a given input. As a result, FTMNet effectively mitigates atmospheric turbulence effect while maintaining low-inference time and computational resource requirements. Experimental results demonstrate that FTMNet shows high-inference speed, reaching 90 fps, and outperforms the state-of-the-art peers.

We consider the distributed filtering problem for a large-scale system with a set of spatially connected sub-systems subject to randomly occurring deception or/and denial-of-service (DoS) attacks during the data transmission among subsystems. In this work we introduce a cyber-attack detection (CAD) model for each measurement by introducing an indicator variable with a beta-Bernoulli prior. The state of interest as well as this indicator are then inferred by a mean-filed variational Bayesian method in an iterative manner. Simulation results carried out on a standard IEEE 39-bus power system demonstrate the superior performance of the proposed method in the presence of deception or/and DoS attacks.

We consider the wideband spectrum sensing within a multi-path propagation environment, where a multi-antenna base station (BS) is tasked with identifying the frequency positions of multiple narrowband transmissions distributed across a broad range of frequencies. To tackle this, we propose a sub-Nyquist sampling structure that incorporates a phased array system. Specifically, each antenna is connected to two separate sampling channels, i.e., one for direct sampling and another for delayed sampling, with the latter incorporating a specified time delay factor. The cross-correlation matrices associated with the samples, which are characterized by different time lags, are calculated. These matrices are represented in tensor form, and the factor matrices are extracted through CANDECOMP/PARAFAC (CP) decomposition. By these factor matrices, the carrier frequencies and the power spectra of the far-field signals of interest are estimated. Numerical simulations are conducted to evaluate the performance of the proposed method, and the results reveal the feasibility and effectiveness of the approach, demonstrating its potential for accurate and efficient wideband spectrum sensing in complex multi-path propagation environments.

We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose two CPD-based methods for estimating the cascade channels associated with different subcarriers. The proposed methods exploit the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus have the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed methods have a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation methods attain mean square errors that are close to the Cramer-Rao bound (CRB) and present a clear advantage over the compressed sensing-based method. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.

In this paper, we consider state estimation in the Kalman filtering framework with unlimited sensing measurements (USMs), which are obtained from sensors equipped with a self-reset analog-to-digital (SR-ADC). SR-ADC was recently introduced to deal with the saturation issue frequently encountered in a conventional ADC. To tackle the nonlinearity of the USM, we present a unique decomposition property of the USM. Leveraging this property and a multiple model adaptive estimation strategy, we propose a novel USF-based Kalman filtering (KF-USM) algorithm. Numerical results reveal that the proposed KF-USM filter is an effective alternative to the conventional ADC-based KF to deal with high dynamic range input signals, offering more accurate state estimation in the presence of saturation.

In this paper, we consider channel estimation for millimeter wave/Terahertz (mmWave/THz) communication systems equipped with extremely large antenna arrays. As the number of antennas increases, users may locate either in the near-field region or in the far-field region, resulting in a hybrid near/far-field channel model. By analyzing the properties of coherence of two near/far-field steering vectors, we construct an orthogonal dictionary and prove that the hybrid near/farfield channel vector has a block-sparse representation on this dictionary. Based on this observation, hybrid near/far field channel estimation for mmWave/THz systems with extremely largescale antennas can be formulated as a block-sparsity compressed sensing problem, which can be solved by many block-sparse signal recovery algorithms such as the B-SBL and PC-SBL. Simulation results reveal that our proposed method can achieve a performance improvement over the existing polar-domain based solution with a substantial reduction of training overhead.

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the Rauch–Tung–Striebel (RTS) smoother. In this study, a modified RTS smoothing algorithm combined with the minimum error entropy (MEE) criterion (MEE-RTS) is developed, and by employing the Taylor series linearization method, it is also expanded to the state estimation of nonlinear systems. The proposed methods improve the robustness of the conventional RTS smoother against complex non-Gaussian noises. In addition, we examine the MEE-RTS smoother's mean error behavior, mean square error behavior, and computational complexity, and the performance of the proposed algorithms is verified by comparing it with existing RTS-type smoothers.

We investigate the problem of joint active and passive beamforming for intelligent reflecting surface (IRS)-aided multi-user multiple-input multiple-output (MIMO) communication systems where the base station (BS) adopts low-resolution DACs to reduce the power consumption and hardware complexity. The additive quantization noise model (AQNM) is employed to model the nonlinear quantization operation. Given a pre-specified minimum achievable rate for each user, an optimization which aims to minimize the transmit power is formulated. We devise an iterative algorithm based on alternating optimization and semi-definite relaxation (SDR) techniques. Numerical results verify the effectiveness of the proposed method.

Gear fault detection based on encoder signals has attracted attention in recent years, in which it is important to capture the instantaneous angular speed (IAS) jitters caused by the gear faults. The central difference method (CDM) is widely used to calculate the IAS. However, due to the quantization error of the encoder and the measurement noise in practice, it is often hard to accurately estimate the jitters of the IAS caused by the gear fault using the CDM directly. To address this issue, the scheme of encoder signal reconstruction and the synchronous average merging is proposed to improve the estimation accuracy of the IAS. First, the encoder signal is reconstructed according to the square wavenumber of the encoder corresponding to each tooth of the gear. Then, the time-synchronous averaging (TSA) process is performed on the reconstructed signal. Third, the IAS signal is estimated by the CDM. Finally, the blind deconvolution based on the cyclostationarity maximization (CYCBD) method is used to process the IAS signal to enhance the jitters caused by the gear fault. The simulation and experimental results support the proposed method.