Signal Processing Issues Research Articles

Implementation of a high-resolution micro-grating accelerometer using a subdivision interpolation technique

This paper proposes a subdivision interpolation technique for an optical accelerometer based on diffraction grating interferometry. The diffraction light intensity curve presents a sine shape with the increase of the acceleration. To address the issues of linearization signal processing across the entire range, a subdivision interpolation circuit is employed, in conjunction with a 90° phase shift and high-precision DC bias-voltage techniques, converting an analog signal with sinusoidal characteristics from the photodetector into standard incremental digital signals that vary linearly over the full range. The novel methodology, to the best of our knowledge, ensures that its performance is least affected by the phase imbalance, offset error, and amplitude mismatch induced by fabrication and alignment errors of the grating, achieving high-resolution digital signal output. The experiment results reveal that the optical accelerometer based on grating interferometry achieved a sensitivity of 85.2 V/g, a resolution of 137.6 µg, as well as a subdivision interpolation factor of 45. This work provides a significant guide for the development of high-resolution MOEMS accelerometers in practical applications.

The Optimization Research on RF Chip Signal Processing Based on Deep Learning Algorithms

With the rapid development of wireless communication technology, radio frequency (RF) chips, as the core components of communication systems, directly influence the overall system performance through their signal processing capabilities. Traditional signal processing methods often struggle to achieve optimal results in the face of complex and variable communication environments. In recent years, deep learning technology, due to its powerful feature learning and pattern recognition capabilities, has shown tremendous potential in various fields. This paper addresses the signal processing issues of RF chips and proposes an optimization method based on deep learning algorithms. Firstly, the status and challenges of RF chip signal processing are analyzed, followed by the design of a deep learning model suitable for RF signal processing. Through data pre-processing and feature extraction, the generalization ability of the model is enhanced. Experimental results demonstrate that the proposed method outperforms traditional methods in signal noise suppression, interference cancellation, and signal distortion compensation, significantly improving the signal processing performance of RF chips. This research provides new ideas and methods for the application of deep learning in the field of RF chip signal processing and holds significant theoretical and practical importance for the future development of wireless communication systems.

Motion-guided large aperture ULA to enhance DOA estimation: An inverse synthetic aperture perspective

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution.

A time-delay neural network for ship-radiated noise recognition based on residual block and attention mechanism

Ship-radiated noise recognition remains a challenging issue in underwater acoustic signal processing for a variety of reasons. The ocean ambient noise influences the signal quality, and the scarcity of samples limits the performance of deep learning algorithms. In this study, a self-attention mechanism and residual block-based time-delay neural network (SRTDNN) is proposed to improve the recognition performance of ship-radiated noise. The noise samples are first segmented and transformed into three-dimensional Mel-scale frequency cepstral coefficients (3D-MFCC), which combine static and dynamic characteristics of targets through the concatenation of MFCC, Delta MFCC and Delta-Delta MFCC. Subsequently, 3D-MFCC are fed as input to the SRTDNN. The SRTDNN extracts time-domain features of inputs through the TDNN layers and then assigns different weights to the frame and channel dimensions of the feature maps through two attention mechanisms. The Res Connections and Res2Block are interspersed to facilitate convergence. During training, a time-domain and frequency-domain data augmentation pipeline is designed to alleviate the lack of samples. Experiments with public and real sea experiment datasets are conducted to compare the SRTDNN with acknowledged high-quality recognition methods. The results show that the SRTDNN has a significant improvement in recognition performance. Furthermore, the ablation tests demonstrate the effectiveness of the proposed method.

High‐resolution inverse synthetic aperture radar imaging of satellites in space

AbstractIn view of the increasing number of space objects, comprehensive high‐quality space surveillance becomes ever more important. Radar is a powerful tool that, in addition to detection and tracking of objects, also enables spatially high‐resolution imaging independent of daylight and most weather conditions. Together with the technique of Inverse Synthetic Aperture Radar (ISAR), very high‐resolution and distance‐independent two‐dimensional images can be obtained. However, advanced high‐performance radar imaging of space objects is a complex and demanding task, touching many technological and signal processing issues. Therefore, besides theoretical work, the Microwaves and Radar Institute of German Aerospace Center (DLR) has developed and constructed an experimental radar system called IoSiS (Imaging of Satellites in Space) for basic research on new concepts for the acquisition of advanced high‐resolution radar image products of objects in a low earth orbit. Based on pulse radar technology, which enables precise calibration and error correction, IoSiS has imaged space objects with a spatial resolution in the centimetre range, being novel in public perception and accessible literature. The goal of this paper is therefore to communicate and illustrate comprehensively the technological steps for the construction and successful operation of advanced radar‐based space surveillance. Besides the basic description of the IoSiS system design this paper outlines primarily useful theory for ISAR imaging of objects in space, together with relevant imaging parameters and main formulae. All relevant processing steps, necessary for very high‐resolution imaging of satellites in practice, are introduced and verified by simulation results. Finally, a unique measurement result demonstrates the practicability of the introduced processing steps and error correction strategies.

Simulation analysis of underwater wireless optical communication based on Monte Carlo method and experimental research in complex hydrological environment

This paper aims to address the challenges of short optical communication distances and high Bit Error Rates (BERs) caused by seawater absorption, scattering, and underwater turbulence. This paper establishes an optical communication channel model based on the Monte Carlo method and simulates and analyzes the process of received optical power attenuation when light is transmitted underwater due to absorption and scattering. The simulation model focuses on the influence of seawater type, transmission distance, receiver position, and angle on the received optical power attenuation. The results of this simulation will provide theoretical support for the development of prototype underwater optical communication systems and experiments in complex environments. To solve the long-range weak signal reception and processing issues in underwater optical wireless communication systems, this paper proposes the design and implementation of an underwater optical wireless communication system using optimized on-off keying high-power light-emitting diode/laser diode modulation technology and avalanche photodiode (APD)/photomultiplier (PMT) receiver modules. In addition, this paper describes the design of the underwater optical communication host computer software, which provides functions such as mode selection, BER monitoring, and file transfer. To test the performance of the proposed system, this paper conducts experiments in a 35 m long pool, fixed platform, and underwater mobile platform, as well as cross-air-water interface optical communication performance test experiments in a complex hydrological environment. The results show that the system achieved a 35 m underwater medium communication distance, transmission rate of 5–20 Mbps, and cross-domain wireless optical communication function under the condition of a BER of less than 10−6. The experiments also revealed that the communication distance decreases as the turbidity of the water body increases and the PMT receiver module selected at the receiver end has a longer communication distance than the APD receiver module. Overall, this paper demonstrates the feasibility of the proposed technology for underwater optical communication systems in complex waters, providing theoretical and data support for the next engineering practice.

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks

Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical parameters based on physical models derived from MR spin magnetization evolution. This requires the acquisition of multiple MR images, resulting in very long scan times. Recently, deep learning (DL) has shown significant potential for reconstructing undersampled MR data. When applying DL to fast qMRI, physical priors can be integrated into the DL framework to improve the performance of qMRI further. This article introduces the physical models of qMRI and four ways to integrate these models into a deep neural network (NN), namely, training sample generation, contrast image prediction, loss function design, and network architecture design. The core concepts and methods for each category are presented. The associated signal processing issues regarding the generalization and reliability of physics-driven DL-based fast qMRI methods are also discussed.

FETAL ECG EXTRACTION FROM THE ABDOMINAL SIGNAL USING WAVELET BISPECTRUM TECHNIQUE

The paper is devoted to the issues of digital signal processing in noninvasive fetal electrocardiography (NI-FECG) during pregnancy. This technology has been attracting more and more attention, both from doctors of perinatal medicine and specialists in the field of electrophysiological signal processing. The former is interested in it as a new informative tool for diagnosing the condition of the fetus and various disorders of the course of pregnancy and the latter as a new field of application of modern signal processing techniques. A novel technique for filtering the FECG signal extracted during NI-FECG and detecting Q-wave R-wave S-wave (QRS) of ventricular complexes of FECG based on combined wavelet bispectrum processing strategy is proposed and studied. New algorithms were used in the prototype of the developed NI-FECG system. The proposed algorithms have shown their efficiency and high quality of FECG extraction and filtering.

Multiplexing-based control of stochastic resonance.

We show that multiplexing (Here, the term "multiplexing" means a special network topology where a one-layer network is connected to another one-layer networks through coupling between replica nodes. In the present paper, this term does not refer to the signal processing issues and telecommunications.) allows us to control noise-induced dynamics of multilayer networks in the regime of stochastic resonance. We illustrate this effect on an example of two- and multi-layer networks of bistable overdamped oscillators. In particular, we demonstrate that multiplexing suppresses the effect of stochastic resonance if the periodic forcing is present in only one layer. In contrast, multiplexing allows us to enhance the stochastic resonance if the periodic forcing and noise are present in all the interacting layers. In such a case, the impact of multiplexing has a resonant character: the most pronounced effect of stochastic resonance is achieved for an appropriate intermediate value of coupling strength between the layers. Moreover, multiplexing-induced enhancement of the stochastic resonance can become more pronounced for the increasing number of coupled layers. To visualize the revealed phenomena, we use the evolution of the dependence of the signal-to-noise ratio on the noise intensity for varying strength of coupling between the layers.

High-Accuracy DOA Estimation Algorithm at Low SNR Through Exploiting a Supervised Index

Performance of direction of arrival (DOA) estimation is one of the most important issues in array signal processing. Subspace-based algorithms provide a good compromise between the estimation accuracy and computational complexity. However, these methods are exposed to performance breakdown at low SNR scenarios. A major reason for such performance breakdown is the subspace swap phenomenon (intersubspace leakage). In this article, we elaborate on a novel modified signal subspace model through exploiting a supervised index for the estimation of DOA. With the developed model we refine the signal subspace so as to enhance the performance of the DOA estimation. In the proposed scheme, we define a fuzzy similarity matrix for the eigenvalues of the array output correlation matrix to capture the distribution of the eigenvalues. Then, we build up a transformation matrix between the fuzzy similarity matrix and the eigenspace of the correlation matrix, and construct a nonlinear transformation function to adjust the fuzzy similarity matrix. Subsequently, we define a supervised evaluation index named signal subspace reconstruction error for DOA estimation and construct a cost function of the signal subspace to develop a supervised model for the signal subspace. The signal subspace can be modified through adjusting the parameter in the nonlinear transformation function and optimizing the abovementioned cost function. Finally, the performance of DOA estimation can be enhanced with the modified signal subspace. The main characteristic of the proposed model is circularly applied feedback of the estimated DOA for refining the estimated subspace. It is a closed loop and supervised method not reported before. This article opens a specific way for improving the performance of the DOA estimation in array signal processing by a supervised index. However, the proposed method is still unsatisfying in some scopes of signal-to-noise ratio. We believe that exploiting a validity index for DOA estimation in array signal processing is still a general and interesting problem.

Low Elevation Estimation for High Mountain Terrain of Meter Wave MIMO Radar Based on Improved Dual Carrier Frequency

Low elevation measurement of meter wave radar is a difficult issue in array signal processing. Dual carrier frequency processing method can be adopted for high mountain terrain in the field of low elevation measurement of meter wave radar. The algorithm works very well. Therefore, the algorithm is introduced into meter wave multiple-input multiple-output (MIMO) radar, and the algorithm is improved to reduce the computational complexity. This letter mainly has two contributions. One is to transplant dual carrier frequency method from conventional array to MIMO array radar; another one is to improve the original dual carrier frequency method, which has a half of calculation amount compared with the original method. Simulation results show the effectiveness of the algorithm.

An Efficient Gauss-Seidel Cubature Kalman Filter

The Cubature Kalman filter (CKF) is a widely used filter with excellent performance, particularly on large non-linear systems. However, the CKF is computationally challenging to implement. The matrix inversion operation increases the computational burden if exact inversion methods, such as the Cholesky decomposition, are used. To reduce the CKF computational cost without losing accuracy in non-linear systems, in this study, we propose an efficient CKF, called the Gauss-Seidel Cubature Kalman filter (GSCKF), where the traditional Kalman gain calculating operation is replaced with the Gauss-Seidel (GS) method. The GS method is exploited to calculate the Kalman gain without complicated matrix inversion. To choose the optimal number of iterations adaptively, the Euclidean norm is considered as the evaluation criterion, which helps the GSCKF achieve the accuracy similar to CKF with less complexity. The computational complexity analysis and execution time results reveal that the GSCKF computational cost is significantly reduced. Numerical simulation results on different large non-linear systems show that the GSCKF, with only a few iterations and outstanding time efficiency, can provide a performance that is indistinguishable with respect to the performance of the CKF. The proposed algorithm will be useful in a variety of engineering areas, specifically in signal processing issues involving large non-linear systems.

Ethical and Ecological Issues in Signal Processing [From the Editor

Ethical and Ecological Issues in Signal Processing [From the Editor

Non-Uniform Sparse Fourier Transform and Its Applications

The fast approximation algorithm of non-uniform discrete Fourier transform (NUDFT) is an important issue in signal processing. In this paper, a novel estimation algorithm is constructed for NUDFT-II, which is the general form of the sparse Fourier transform (SFT). Firstly, we propose the cyclic convolution in the non-uniform frequency domain and derive the product and convolution theorem. Secondly, the relationship is deduced between the inverse NUDFT of a uniformly sampled sequence in the frequency domain and that of a non-uniformly sampled sequence. Then, based on the relationship and cyclic convolution, we establish a random permutation operation, a non-uniform flat window filtering operation, and a frequency subsampling operation for NUDFT-II to compress sparse signal length. Meanwhile, a method to estimate the significant frequencies' locations and values is introduced. Finally, we propose the non-uniform sparse Fourier transform (NUSFT). Simulation results demonstrate that the proposed method has low complexity in sample and runtime, and has high robustness. Furthermore, the NUSFT algorithm has been applied to signal detection and reconstruction, which shows that the required signals are obtained accurately. And the NUSFT can detect frequencies that cannot be detected by the SFT.

An Overview of the Development of Speaker Recognition Techniques for Various Applications

Speech Enhancement (SE) is a significant research issue in audio signal processing where the goal is to enhance the clarity and quality of speech signals corrupted by noise. Because of its different applications, it becomes a compelling research topic nowadays. The focus of this paper is dedicated to one of these applications which is Speaker recognition. In this paper, the fundamentals and applications of speaker recognition are discussed. A brief study on the performance and the recognition accuracy of different speaker modeling techniques has been conducted. Furthermore, while there have been several studies about the technologies used in speaker recognition, there have been few studies about the applications of speaker recognition, and none of them considers combining or linking the applications and technology in the same study. This overview demonstrates the various technologies that can be used to achieve speaker recognition applications. It aims to give a new perspective of the existing technologies uses in various applications. The paper concludes with discussions on future trends and research opportunities in this area.

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking.

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.

Underwater target recognition using convolutional recurrent neural networks with 3-D Mel-spectrogram and data augmentation

Passive recognition of underwater acoustic targets is a hot research issue in acoustic signal processing. The long-term interference of irregular noise in the marine environment caused the relevance of the passive recognition method of underwater targets based on the traditional technical framework to gradually decrease. Due to the interference of irregular noise in the ocean, the passive recognition method used for underwater targets based on the traditional technical framework is gradually becoming less relevant. The feature extraction method that combines deep learning and time–frequency spectrogram can better describe the differences of different targets. In this paper, the proposed model contains three steps to deal with the recognition of underwater targets: feature extraction, data augmentation and deep neural network. For the feature extraction, we use a Mel-spectrogram, as well as the delta and delta-delta features in order to construct 3-D features. In the data augmentation part, we expand the dataset with SpecAugment in the time domain and frequency domain. In deep neural network prediction part, we use the convolutional recurrent neural network (CRNN) for acoustic target recognition. Through a comparison with the ablation test, it is clear that the pipeline in our method is effective in acquiring the recognition result. After evaluating our system through the carrying out of three tasks on the ShipsEar dataset, and the recognition accuracy are 94.6%, 87.5% and 72.6% in task 1, task 2 and task 3 respectively.

AFISTA: Accelerated FISTA for sparse signal recovery and compressive sensing

This paper presents a new fast iterative shrinkage-thresholding algorithm, termed AFISTA. The essential idea is to improve the convergence rate of FISTA using a new continuation strategy leading to a less number of iterations compared to FISTA. The convergence theorem of the AFISTA is proposed. In order to further accelerate the AFISTA method, it is equipped with the Barzilai-Borwein (BB) method. Also, for applications with orthogonal sensing matrix A, we proposed a specialized version of the AFISTA method. AFISTA is tailored for solving the basis pursuit problem which can be applied successfully on a variety of problems arising in signal and image processing issues such as sparse signal recovery, signal and image denoising, image restoration, and compressive sensing. To show the efficiency of the method, we compare our results with generalizations of linearized Bregman and fixed - point continuation (FPC) methods in sparse signal recovery applications, with split Bregman method in compressive sensing for sparse MRI and with Gradient projection for sparse reconstruction (GPSR) method in image deconvolution. Numerical results demonstrate that AFISTA overcomes all of the compared methods in convergence rate and some of them in both convergence rate and quality of reconstructed results.

Fast optimization of cache-enabled Cloud-RAN using determinantal point process

Cloud radio access network (Cloud-RAN) has been considered as a potential candidate for the next generation of radio access networks. It addresses many challenges in terms of flexibility, scalability, radio resource management and energy efficiency. Caching popular contents at radio remote heads (RRHs) plays an important role for reducing fronthaul traffic congestion and delay in cache-enabled Cloud-RAN. Although, mathematical optimization methods have shown to be providing numerical solutions for addressing key signal processing issues in Cloud-RAN, the exponential complexity hinders their application in practice, particularly in large networks. Learning-based methods have become attractive to overcome the complexity issues associated with the mathematical optimization methods. Several subset selection problems have been formulated as a mixed-integer non linear program (MINLP) in wireless networks. Determinantal point process (DPP) is a probabilistic model of choosing two similar items which are negatively correlated. In this paper, we propose a DPP based-learning (DPPL) framework to obtain a subset of admitted users for cache-enabled Cloud-RAN with limited fronthaul capacity. The formulated problem of minimizing the total network cost including power and fronthaul cost while admitting as many users as possible is converted into mixed-integer second order cone programming (MI-SOCP). The subset of admitted users is obtained by learning the quality-diversity trade-off of the DPP using the optimal subsets of admitted users which are obtained by the optimization approach. We then propose an optimization algorithm to determine the beamforming and the base station-user allocation for the obtained subset of admitted users. We provide numerical results to assess the performance and complexity of the proposed DPPL algorithm and compare it with its optimization counterpart. The results reveal that the proposed DPPL can achieve a comparable performance with much lower complexity.

Time-Varying DOA Tracking Algorithm Based on Generalized Labeled Multi-Bernoulli

Direction of arrival (DOA) tracking for multi-sources is a hot issue in array signal processing. To deal with the problem that sources DOA and their number are time-varying, a DOA tracking algorithm based on Generalized Labeled Multi-Bernoulli (GLMB) filter is proposed. Since the measurement value has only one set of data, the measurement association mapping (MAM) does not match, which leads to deviations in the GLMB filter update step. In this regard, we used the estimated sources number of the previous time step as the measurement number of the current time step, and successfully achieved MAM matching. Subsequently, particle filtering is used to approximate the posterior distribution of DOA, where the particle likelihood function can be calculated by the multi-signal classification (MUSIC) spatial spectrum function. In addition, by exponentially weighting the likelihood function, the number of particles in the high likelihood region of the posterior distribution increases, which makes the GLMB filter pruning and merging operations more effective. Simulation results show that the method is better than the probability hypothesis density DOA (PHD-DOA) algorithm in tracking state sources and estimating the number of targets.