IET Signal Processing最新文献

Synthetic impulse and aperture radar (SIAR) is a technique that frequency diverse array (FDA) radars can imply in practice, thus overcoming some of their challenges. SIAR radars, used in various fields like transportation and defense, can detect the range, azimuth angle, elevation angle, and Doppler of the target with their 4D-matched filter and a single receiver. However, the challenge of high-amplitude sidelobes is a significant concern for researchers. They have attempted to reduce it through various approaches, including frequency code, range–angle coupling, and range–Doppler coupling, to accurately identify target characteristics. This paper presents the antenna place code (AP code) parameter as a significant factor in minimizing sidelobe amplitudes. The parameter specifies that, rather than having all antennas active, a certain number of antennas are active in each pulse repetition interval (PRI) to achieve a lower sidelobe. Researchers have found that using AP codes can effectively lower the amplitude of the range–angle sidelobe, the range–Doppler sidelobe, error coupling, the repetition of sidelobe strands, and the output of angle error for different target angles. All studies are conducted on a linear array for simplicity. The output of various AP codes is compared to the previously common uniform array.

In this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. To achieve the inequality-constrained estimation, the constrained state is computed by truncating the probability density function (PDF) of the estimated state after the variational update stage; the mean and covariance of the constrained state are the first and second moments of the truncated PDF. Considering the model uncertainties where the system dynamics are unpredictable, a multiple model VB truncated adaptive filter is proposed in the interacting multiple model framework. The performances of the proposed algorithms are evaluated via the target tracking simulations and the robot positioning experiments. Results show that the proposed algorithms improve estimation accuracy compared with the existing adaptive filters when the states suffer inequality constraints.

The analysis and interpretation of enormous amounts of data generated by 5G networks present several challenges related to noise, precision, and feasibility validation. Therefore, this study aims to evaluate the effectiveness of channel equalisation in the network and enhance it by distributing signals over all subcarriers and symbols. The error-free signal received ensures the reliable transmission of signals in the network connection. These simulations were undertaken to fulfil the needs of and adapt the transmission properties according to the specific conditions of the channel. The dataset consists of artificially generated radio waves to train signals through neural networks (NNs) and machine learning algorithms to detect errors properly. The primary objective is to achieve optimal signal performance. In this regard, an artificial neural network (ANN) was initially employed, explicitly utilising the back-propagation technique and a feedforward multilayer perceptron (MLP). In addition, the signals were subjected to train using a real-time simulator, employing feedforward neural network and support vector machine (SVM) to validate the proposed methodology. Feedforward MLP achieved the highest performance in simulations compared to SVM. The scheme is promising to achieve optimal signal performance in real-time.

It is a fundamental problem to analyze the performance bound of multiple-input multiple-output dual-functional radar-communication systems. To this end, we derive a performance bound on the communication function under a constraint on radar performance. To facilitate the analysis, in this paper, we consider a simplified situation where there is only one downlink user and one radar target. We analyze the properties of the performance bound and the corresponding waveform design strategy to achieve the bound. When the downlink user and the radar target meet certain conditions, we obtain analytical expressions for the bound and the corresponding waveform design strategy. The results reveal a tradeoff between communication and radar performance, which is essentially caused by the energy sharing and allocation between radar and communication functions of the system.

A labeled multi-Bernoulli filter is used to obtain estimates of the identities and states of targets in complex environments. However, when tracking multiple targets in dense clutters, the computational complexity of the traditional labeled multi-Bernoulli filter will increase exponentially. A labeled multi-Bernoulli tracking algorithm based on maximum likelihood recursive update is proposed, which can reduce the computational scale while maintaining tracking accuracy. Specifically, when performing posterior estimation, a maximum likelihood recursive update method is proposed to replace the complete enumeration, truncated enumeration, or sampling enumeration methods used in many traditional methods. Furthermore, combined with the Gaussian mixture technique, a maximum likelihood recursive updating labeled multi-Bernoulli tracking algorithm is constructed. Simulation results demonstrated that the proposed filter obtained a good balance between the tracking accuracy and computational efficiency.

Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross-term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low-order smoothed pseudo Wigner (FLOSPW), fractional low-order multi-windowed short-time Fourier transform (FLOMWSTFT), and improved fractional low-order multi-windowed short-time Fourier transform (IFLOMWSTFT) utilizing fractional low-order statistics and short-time Fourier transform (STFT) to mitigate cross-terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an α infinite variance process.

This paper investigates a backscatter communication (BackCom) based non-orthogonal multiple access (NOMA) system in a multiple-input and single-output (MISO) scenario, where two decoding methods are deployed, including the sum-capacity approach and QR decomposition. The goal is to maximize energy efficiency (EE) through the optimization of the beamforming matrix and the reflection coefficient of the BackCom devices. Two algorithms, Dinkelbach based on penalty semidefinite relaxation (SDR) and successive convex approximation (SCA), are proposed as high-performance and low-complexity solutions, respectively. Simulation results indicate that the combination of the sum-capacity approach and Dinkelbach yields the best performance, though at the highest complexity, while the amalgamation of QR decomposition and SCA offers the lowest performance but with minimal complexity.

Target detection in infrared remote sensing images has important practical applications. Among the current high-performance methods, the deep learning-based methods require training samples, and their generalization ability is also limited by the training set. The separation of low-rank and sparse matrix requires joint processing of multiple images with high computational complexity. The track-before-detect algorithms based on particle filtering also have high computational complexity. In this paper, the low-rank and sparse matrix of a single image are proposed for target detection, and a differentiable objective function is used in the separation. At the same time, an extended multitarget tracking algorithm based on random sets is used for target filtering between frames, and the design of the filters adopts the conjugate distribution under the Bayesian framework. Finally, the practical infrared sequence images containing multiple targets and complex backgrounds were employed to verify the performance of the proposed algorithms by comparing them with state-of-the-art algorithms.

Sample clustering techniques play a crucial role in the data-driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set-type samples. In this paper, we propose an incremental clustering algorithm for matrix-type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.

Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low-speed asynchronous sampled signals are modeled, and their in-phase quadrature components are used to generate a two-dimensional asynchronous in-phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low-speed asynchronous sampling is 6 dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments.