Search Results (70)

Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework. Full article

The increasing activity of unmanned aerial vehicles (UAVs) has intensified the demand for reliable and computationally efficient methods for passive radio-frequency (RF) signal detection. In practical RF monitoring scenarios, the environment is often non-stationary and affected by varying noise conditions. Under such circumstances, classical energy-based detectors are sensitive to noise uncertainty, while more robust approaches, such as cyclostationary analysis, require substantially higher computational resources. This work presents a burst-aware cascade method for UAV RF signal presence detection that explicitly addresses this trade-off. The proposed framework combines fast energy-based screening with temporal burst aggregation, applying spectral correlation function (SCF) analysis selectively and only when sustained signal activity is indicated. Detection is performed on fixed-length RF signal chunks, while additional segment-level duration constraints are used to characterize sustained transmissions. The method is evaluated using the publicly available DroneRF dataset and compared against six baseline detectors, including fixed-threshold energy, wavelet-based, blind cyclostationary, two adaptive energy detector variants, and a lightweight convolutional neural network. Experimental results confirm that chunk-level detection remains difficult for all considered methods. Temporal aggregation across longer intervals yields a substantial improvement: the cascade achieves $P_d$ = 1.000 and AUC = 1.000 at the segment level, matching exhaustive cyclostationary detection while reducing per-segment processing time by a factor of 2.46. An additional result is that burst-level concatenation prior to SCF estimation provides implicit coherent integration, preserving $P_d$ = 1.000 at signal amplitude reductions of up to −20 dB where standalone detectors degrade to $P_d$ = 0.995. Overall, burst-aware cascade architectures offer a practical and interpretable approach to RF-based UAV monitoring, providing a well-grounded compromise between detection reliability and computational efficiency under realistic operating conditions. Full article

Rotor-induced micro-Doppler signatures are essential for radar-based characterization of rotary-wing UAVs, but practical echoes are often dominated by a strong quasi-static body return concentrated near zero Doppler. In hovering or low-speed scenarios, rotor-induced components may intermittently overlap this near-zero region, where hard DC suppression discards informative rotor content and fragments micro-Doppler structures. Data-driven decompositions such as EMD and VMD avoid fixed cutoffs, yet without explicit constraints on rotor periodicity they are vulnerable to mode mixing and residual leakage under low-SNR conditions. This paper proposes a Cyclic-Power-Guided Soft Mask (CPGSM) framework that exploits cyclostationary periodicity as a physically grounded prior for rotor–body separation. A CPS-guided soft masking procedure consisting of a DC-dominant overlap band is first identified from quasi-static dominance; within this band, cyclic power spectrum analysis yields a continuous rotor-consistency score that guides smooth time–frequency soft allocation, while deterministic assignment is applied elsewhere. Simulations demonstrate improved micro-Doppler continuity, reduced body leakage, and more stable performance from 5–30 dB SNR compared with hard DC isolation and EMD/VMD, together with consistent rotor-speed estimates across sensing configurations. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

This paper introduces an Improved Red-Billed Blue Magpie Optimizer (IRBMO) to enhance the Maximum Second-Order Cyclostationary Blind Deconvolution (CYCBD) method, which traditionally depends on manual, experience-based setting of its key parameters (filter length $L$ and cyclic frequency $\mathsf{\alpha }$). By adopting an Improved Envelope Spectrum Entropy (EK) as the fitness function, the IRBMO autonomously optimizes these parameters, eliminating the need for prior knowledge and improving its applicability in industrial settings. The Improved Red-Billed Blue Magpie algorithm is employed to adaptively optimize the penalty parameter and kernel function parameter of the support vector machine, thereby obtaining an optimal support vector machine model. By introducing fuzzy entropy theory, the feature vectors of filtered signals—processed by the Cyclostationary Blind Deconvolution method with optimal parameters—are extracted and used as input for the optimally parameterized support vector machine, achieving multi-fault classification for bogie bearings. The results show that the IRBMO-CYCBD method significantly enhances the periodic weak fault impulse components and improves the signal-to-noise ratio of the processed signal. Envelope spectrum analysis of the filtered signal allows for clear observation of shaft frequency components, as evidenced by the accurate identification of the 110 Hz fundamental frequency and its harmonic components at 220 Hz, 330 Hz, and 440 Hz in the spectrum. Simulation tests verify the efficacy of the IRBMO-CYCBD method in processing rolling bearing vibration signals under strong noise interference. Under laboratory conditions, simulation experiments were conducted by collecting vibration acceleration signals from rolling bearings in various states. The aforementioned method was applied for fault diagnosis, achieving a maximum diagnostic accuracy of 100%. Through repeated experiments, it was verified that this method meets the fault diagnosis requirements for rolling bearings in metro train bogies. Full article

Propeller noise is the main source of ship-radiated noise. Extracting and analyzing the modulation characteristics from the propeller noise plays a crucial role in classifying and identifying vessel targets. Existing demodulation methods such as Detection of Envelope Modulation On Noise (DEMON), narrowband demodulation, and cyclostationary analysis can be used to extract modulation features. However, capturing the modulation features on the envelope spectrum may be hard under low signal-to-noise ratio scenarios, since the envelope spectrum is contaminated by interference noise. To address this challenge, selecting an optimal frequency band rich in modulation information can significantly enhance demodulation performance. This paper proposes an Adaptive Hoyer-L-moment Envelope Spectrum (AHLES) method. The method first introduces an optimal frequency band selection method based on the golden section search strategy. A Hoyer-L-moment metric is then designed to quantify the modulation intensity within narrow frequency bands. Based on this metric, the optimal spectral coherence integration band is adaptively selected according to the signal’s inherent modulation characteristics, thereby enhancing demodulation performance. The effectiveness of the proposed method is validated through experiments on both simulated signals and merchant ship data. Full article

Cognitive radio networks (CRNs) rely heavily on spectral sensing to detect primary user (PU) activity, yet detection at low signal-to-noise ratios (SNRs) remains a major challenge. Hence, a novel “Deep Wavelet Cyclostationary Independent Gaussian Markov Fourier Transform Dempster–Shafer Network” is proposed. When the signal waveform is submerged within the noise envelope and residual correlation emerges in the noise, it violates white Gaussian assumptions, leading to misidentification of signal presence. To resolve this, the Adaptive Continuous Wavelet Cyclostationary Denoising Autoencoder (ACWC-DAE) is introduced, in which the Adaptive Continuous Wavelet Transform (ACWT), Cyclostationary Independent Component Analysis Detection (CICAD), and Denoising Autoencoder (DAE) are introduced into the first hidden layer of a Deep Q-Network (DQN). It restores the bursty signal structure, separates the structured noise, and reconstructs clean signals, leading to accurate signal detection. Additionally, bursty and fading-affected primary user signals become fragmented and dip below the noise floor, causing conventional fixed-window sensing to fail in accumulating reliable evidence for detection under intermittent and low-duty-cycle conditions. Therefore, the Adaptive Gaussian Short-Time Fourier Transform Dempster–Shafer Model (AGSTFT-DSM) is incorporated into the second DQN layer, Adaptive Gaussian Mixture Hidden Markov Modeling (AGMHMM) tracks the hidden activity states, Adaptive Short-Time Fourier Transform (ASFT) resolves brief signal bursts, and Dempster–Shafer Theory (DST) fuses uncertain evidence to infer occupancy, thereby detecting an accurate user signal. The results obtained by the proposed model have a low error and detection time of 0.12 and 30.10 ms and a high accuracy of 97.8%, revealing the novel insight that adaptive wavelet denoising, along with uncertainty-aware evidence fusion, supports reliable spectrum detection under low-SNR conditions where existing models fail. Full article

Modern inspection mobile robots can carry multiple sensors that can provide opportunities to take advantage of the fusion of information obtained from different sensors. In real-world condition monitoring, harsh environmental conditions can significantly affect the sensor’s accuracy. To address this issue in this paper, we introduced a fusion approach around information gaps to handle the portion of false information that can be captured by the employed sensors. To test our idea, we looked at various types of data, such as sounds, color images, and infrared images taken by a mobile robot inspecting a mining site to check the condition of the belt conveyor idlers. The RGB images are used to classify the rotating idlers as stuck ones (late-stage faults); on the other hand, the acoustic signals are employed to identify early-stage faults. In this work, the cyclostationary analysis approach is employed to process the captured acoustic data to visualize the bearing fault signature in the form of Cyclic Spectral Coherence. Since convolutional neural networks (CNNs) and their transfer learning (TL) forms are popular approaches for performing classification tasks, a comparison study of eight CNN-TL models was conducted to find the best models to classify different fault signatures in captured RGB images and acquired Cyclic Spectral Coherence. Finally, to combine the collected information, we suggest a method called dynamic weighted majority voting, where each model’s importance is regularly adjusted for each sample based on the surface temperature of the idler taken from IR images. We demonstrate that our method of combining information from multiple classifiers can work better than using just one sensor for monitoring conditions in real-world situations. Full article

For maritime cognitive radio networks (MCRN), spectrum sensing (SS) is challenging due to the movement of the sea, channel interference, and unstable link quality. Besides the basic sensing capabilities that are required, SS in MCRN also requires the ability to adapt to complex and dynamic environments. By transforming spectrum sensing into a classification problem and leveraging cyclostationary features and Convolutional Neural Networks (CNN), This paper proposes a classification-guided TC2NND (Transfer Cyclostationary- feature and Convolutional Neural Networks Detection) spectrum sensing algorithm, which regards the maritime spectrum sensing as a classification problem. The TC2NND algorithm first classifies the received signal features by extracting cycle power spectrum (CPS) features using the FFT (Fast Fourier Transform) Accumulation Method (FAM), and then makes a judgment using a variety of C2NND decision models. The experimental results demonstrate that the proposed TC2NND algorithm could achieve a detection probability of 91.5% with a false-alarm probability of 5% at SNR = −10 dB, which significantly outperforms the conventional methods. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level ($L$) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of $L$and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at $L=3$ was best across the three locations. Full article

In this paper, a reliability assessment and prediction method based on bearing vibration signals is proposed, which combines Adaptive Cyclostationary Blind Deconvolution (ACYCBD) and AdaBoost-Mixed Kernel Relevance Vector Machine. Firstly, CYCBD parameters were optimized by the Ivy optimization algorithm to enhance the noise reduction effect, and then multidimensional features were extracted and dimensionalization was reduced by PaCMAP. Based on dimensionality reduction features, logistic regression was used to evaluate reliability, and AdaBoost-MKRVM was combined to predict reliability. The experimental results show that the mean absolute error (MAE) of the proposed method on the bearing life dataset of Xi’an Jiaotong University is 0.052, which is better than the traditional method, and provides a new idea for the performance prediction of rolling bearings. Full article

To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds. Full article

Doppler shift estimation continues to be a critical challenge of utmost significance in both theoretical research and practical engineering applications. Many innovators have crafted solutions specific to this issue, with notable contributions across various signals and scenarios. Given that cyclostationary signals are prevalent in both artificial and natural phenomena, we propose a novel framework based on the phased fractional lower-order moment (PFLOM) for estimating Doppler shift in mixed cyclostationary signals. During the estimation process, a more realistic impulse noise model is examined in contrast to the ideal Gaussian noise typically assumed in conventional methods. This approach is meticulously derived through a series of detailed steps in line with cyclostationary signal processing and PFLOM principles. Furthermore, an extensive simulation has been conducted to validate the efficacy and robustness of our proposed method. It is anticipated that the concept and method presented here could be applied more broadly due to its solid theoretical underpinnings. Full article

Automatic and blind parameter estimation based on the inherent features of wireless signals is a major research area due to the fact that these techniques lead to the simplification of receivers, especially in terms of coarse synchronization, and more importantly reduce the signaling load at the control channels. Thus, in the literature, many techniques are proposed to estimate a vast set of parameters including modulation types and orders, data and chip rates, phase and frequency offsets, and so on. In this paper, a cyclostationary feature detection (CFD) based method is proposed to estimate the carrier numbers and carrier spacing of carrier-aggregated direct sequence spread spectrum (DSSS) cellular signals blindly. The particular chip rate of the signal is also estimated through the process jointly. The proposed CFD-based method unearths the inhered and hidden second-order periodicities of carrier-aggregated DSSS signals, particularly targeting repeated pseudorandom noise sequences of users over the carriers. Throughout the paper, after the proposed method is formulated, the measurement setup that is developed to collect the data for the validation of the method is introduced. The measurement results are post-processed for performance analysis purposes. To that end, the method is investigated in terms of signal-to-noise ratio (SNR) values, different channel conditions, and measurement durations. Furthermore, the performance of the proposed method is compared with that of energy detection. The measurement results indicate superior performance of the proposed method under significant wireless channel impairments and in low-SNR regions, e.g., for 0 dB the proposed method provides more than $0.9$ detection performance for the case of $0.1$ false alarm rate, while the performance of ED is $0.6$ under the same wireless channel impairments. The raw outputs of the method can be utilized to train a convolutional neural network to eliminate the statistical estimation process in future work. Full article