Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals

Sensor arrays distributed over a large area can theoretically provide high resolution and coherent gain against diffuse noise, but are difficult to calibrate in practice. In many cases, however, some elements of the array may be well-calibrated while others are not. This paper leverages the size and resolving power of a large, distributed array while only requiring calibration knowledge on a small portion of the array. In simulation, we show that a 32-element uniform line array (ULA) with as few as four calibrated elements is capable of detecting and localizing a weak source in the presence of a strong interferer while using only the calibrated portion of the array is unable to resolve the target. Finally, we demonstrate the detection of a weak source in the presence of nearby strong interference using a distributed array consisting of 12 circular microphone sub-arrays with uncertain inter-array calibration.

Target direction-of-arrival (DOA) estimation is often difficult in the presence of strong interference-especially when the target DOA is very close to the interference DOA-since the strong interference signal can mask the weak target signal and make the DOA estimation hard. To address this problem, an efficient sparse method for DOA estimation is proposed in this paper, in which the effect of strong interference on the target DOA estimation is significantly reduced. An on-grid version of the grid evolution technique is then developed to nonuniformly refine the grid, thereby reducing the computational complexity while retaining reasonable accuracy. Numerical simulations and experimental results demonstrate that, compared to state-of-art methods, the proposed method achieves higher efficiency and better DOA estimation performance in the presence of strong interference.

Accurate azimuth detection of targets in High Frequency Surface Wave Radars in an essential task of the primary signal processing in order to determine target locations. In the presence of strong interference, it becomes more difficult and represents an important challenge because it can degrade the radar's performance. In this paper, we propose an improved method for high-resolution azimuth detection in such systems under strong external interference which manifests itself as a grouping of false targets by azimuth. The paper contains the detailed method description, the description of the proposed method for improved azimuth detection and the performance analysis based on the experimentally obtained radar data. The results show that it is possible to achieve significant improvements in the detectability of targets.

In film and television production, efficient and precise image processing is vital for achieving realistic visual effects. Therefore, exploring and applying advanced image processing technologies has become an essential method for elevating the production quality of film and television projects. This work investigates the application of artificial intelligence (AI) technology in the processing and production of animated images in film and television scenarios. By comparing the performance of standard Generative Adversarial Network (GAN), DenseNet, and CycleGAN models under different noise conditions, it is found that CycleGAN performs the best in image denoising and detail restoration. Experimental results demonstrate that CycleGAN achieves a Peak Signal-to-noise Ratio (PSNR) of 30.1dB and a Structural Similarity Index Measure (SSIM) of 0.88 under Gaussian noise conditions. Moreover, CycleGAN achieves a PSNR of 29.5dB and an SSIM of 0.85 under salt-and-pepper noise conditions. It outperforms the other models in both conditions. Additionally, CycleGAN’s mean absolute error is significantly lower than that of the other models. This work demonstrates that CycleGAN can more effectively handle complex noise and generate high-quality images under unsupervised learning conditions. These findings provide new directions for future image processing research and offer important references for model selection in practical applications. This work not only offers new perspectives on the development of animation image processing technology but also establishes a theoretical foundation for applying advanced AI techniques in film and television production. Through comparative analysis of various deep learning models, this work highlights the superior performance of CycleGAN under complex noise conditions. This advancement not only drives progress in image processing technology but also provides effective solutions for efficient production and quality enhancement of future film and television works.

T-wave alternans (TWA) is an indicator of cardiac instability and is associated with life-threatening ventricular arrhythmias. Detection of TWA in the adult has been widely investigated and is used routinely for cardiac risk assessment. Detection of TWA in the fetus, however, is much more difficult due to the low amplitude and variable configuration of the signal, the presence of strong interferences, and the brevity of fetal TWA episodes. In this paper, we present a statistical detector based on the generalized likelihood ratio test that is designed for detection of TWA in the fetus. The performance of the detector is evaluated by constructing receiver-operator characteristic curves, using simulated data and real data from subjects with macroscopic TWA. The detector is capable of detecting TWA episodes as brief as 20 beats. The detection performance is improved significantly by modeling the fetal T-wave as a low-rank, low bandwidth signal, and using maximum likelihood estimation to estimate the model parameters. This approach enables all of the data to be used to estimate the noise statistics, providing highly effective suppression of interference, including maternal interference. The method is suitable for routine use because it can be applied to raw, unprocessed recordings, allowing automated analysis of extended fetal magnetocardiography recordings.

Based on the extended noise subspace, the case of weak signal direction of arrival (DOA) estimation in presence of strong jamming is studied, and a novel approach is given in this paper. Under the condition knowing the number of strong interferences, first, the method forms the extended noise subspace, then, uses the conventional multiple signal classification (MUSIC) algorithm to constrain the strong interferences effectively and estimates weak signals' DOA information accurately based on the extended noise subspace. The method does not require the angle information of strong interferences and it has the same computation burden as conventional MUSIC. Theoretic analysis and computer simulation demonstrate the method is effective and feasible.

Towards precise complex AM-FM signals decomposition under strong noise conditions: A novel two-level chirp mode decomposition approach

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.

Signal or target detection is sometimes complicated by the presence of strong interference. When this interference occurs mainly in the sidelobes of the antenna pattern, a solution to this problem is realized through a sidelobe canceler (SLC) implementation. Since the false-alarm probability is a system parameter of special importance in radar, an interference-canceling technique for radar application should maintain the false-alarm probability constant over a wide range of incident interference power. With the requirements of sidelobe interference cancellation and constant false alarm rate (CFAR), a new algorithm for radar detection in the presence of sidelobe interference is developed from the generalized likelihood ratio test of Neyman-Pearson. In this development, the received interference is modeled as a nonstationary but slowly varying Gaussian random process. Cancellation of the sidelobe interference is based upon a 'synchronous' estimate of the spatial covariance of the interference for the range gate being tested. This algorithm provides a fixed false-alarm rate and a fixed threshold which depend only upon the parameters of the algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper proposes a new blind adaptive algorithm for computing the weight vector of an antenna array system that provides the beam pattern having its maximum gain along the direction of the mobile target signal source in the presence of strong interference. The proposed algorithm provides a suboptimal weight vector maximizing the SINR (signal to interference plus noise ratio) with a linear computational load. Based on the analysis obtained from various simulations, it is observed that the proposed algorithm is suitable for practical IMT2000 mobile communication environments.

The speed controller of the launcher has a very important role in stabilizing the control quality, increasing the probability of destroying the target. This paper presents the results of building a slide controller for the 9K35 STRELA-10 launcher drive system. The test results by simulation on Matlab-Simulink software show that this controller ensures accuracy in control; Stable speed even in the presence of strong interference.

This paper presents an experimental evaluation of a novel and innovative sequential beamspace (SBS) smart antenna system. The system is capable of accurate direction of arrival (DOA) estimation in beamspace and efficient element space beamforming. Moreover, the robust functionality of such a system includes high resolution radio frequency (RF) emitter DOA estimation and beamforming in a noisy environment in the presence of strong interference. The beamspace DOA estimation accuracy, beamforming pattern, and output signal quality have been thoroughly studied and quantified. The algorithms have been tailored to utilize an 8 element uniform circular array (UCA) and an 8 channel analog beamformer (BF) operating at 5.8 GHz to gather lab-based experimental results. The previously completed simulations in [1], and the experimental results herein show that the proposed system can achieve good performance once it is properly synchronized using a time delay correction filter. In addition, a significant decrease in hardware is realized when operating in beamspace versus element space.

The number of times that equipment can emit is limited, and exceeding the usage limit will affect its shooting accuracy. Existing research mainly focuses on the classification and detection of emitting times, with few studies focusing on the recognition of emitting times. This paper combines multi-scale feature extraction with the CRNN model and proposes an improved CRNN emission frequency recognition model. After experimental verification, the improved model has a high prediction accuracy on high-quality emitted sound data, with an accuracy of over 95.2%. The accuracy of the data in the presence of strong interference is 88.6%. The experimental results show that the method proposed in this paper is effective.

In this paper, we present an efficient strategy for satellite signal acquisition in the presence of strong interferences. A GNSS receiver equipped with a fully augmentable non-uniform linear array is assumed. The interference directions-of-arrival (DOAs) are estimated using a low-complexity and accurate Fourier-based approach. Interference suppression is then performed by placing nulls in the estimated directions. Using simulated data, the performance of the proposed scheme is compared with that of beamforming, root-MUSIC algorithm, and Minimum Output Power polynomial rooting method in terms of execution time, DOA estimation accuracy, and output signal-to-interference-plus-noise ratio.

Automated and coordinated vehicle platoon driving is challenging as the complexity of vehicle control and the presence of unreliable wireless inter-vehicle communication. This subject has an interdisciplinary nature, involving control theories, vehicle dynamics, and communication. Due to the constant interaction between control logic and communication topology, a co-design approach for the controller and for reliable communication protocol becomes necessary. This paper describes the AddP-CACC - a consensus-based solution to vehicle platoons. The proposed solution turns the communication topology into a design parameter that dynamically reconfigures the controller. In addition, the controller automatically compensates for outdated information caused by network losses and delays. AddP-CACC was implemented in the PLEXE tool and was compared with other related works. Simulation results have shown the robustness and performance of the proposed solution in various scenarios that would include realistic propagation conditions with interference caused by other vehicles. The results have also evidenced the ability of the proposed solution to maintain a stable vehicular platoon by using different communication topologies, different vehicle flows, even in the presence of strong interferences, delays, and fading conditions.