Interference Detection Method Research Articles

Multicomponent signals interference detection exploiting HP-splines frequency parameter

Multicomponent signals play a key role in many application fields, such as biology, audio processing, seismology, air traffic control and security. They are well represented in the time-frequency plane where they are mainly characterized by special curves, called ridges, which carry information about the instantaneous frequency (IF) of each signal component. However, ridges identification usually is a difficult task for signals having interfering components and requires the automatic localization of time-frequency interference regions (IRs). This paper presents a study on the use of the frequency parameter of a hyperbolic-polynomial penalized spline (HP-spline) to predict the presence of interference regions. Since HP-splines are suitably designed for signal regression, it is proved that their frequency parameter can capture the change caused by the interaction between signal components in the time-frequency representation. In addition, the same parameter allows us to define a data-driven approach for IR localization, namely HP-spline Signal Interference Detection (HP-SID) method. Numerical experiments show that the proposed HP-SID can identify specific interference regions for different types of multicomponent signals by means of an efficient algorithm that does not require explicit data regression.

A Dynamic Interference Detection Method of Underwater Scenes Based on Deep Learning and Attention Mechanism.

Improving the three-dimensional reconstruction of underwater scenes is a challenging and hot topic in the field of underwater robot vision system research. High dynamic interference underwater has always been one of the key issues affecting the 3D reconstruction of underwater scenes. However, due to the complex underwater environment and insufficient light, existing target detection algorithms cannot meet the requirements. This paper uses the YOLOv8 network as the basis of the algorithm and proposes an underwater dynamic target detection algorithm based on improved YOLOv8. This algorithm first improves the feature extraction layer of the YOLOv8 network, improves the convolutional network structure of Bottleneck, reduces the amount of calculation and improves detection accuracy. Secondly, it adds an improved SE attention mechanism to make the network have a better feature extraction effect; in addition, the confidence box loss function of the network is improved, and the CIoU loss function is replaced by the MPDIoU loss function, which effectively improves the model convergence speed. Experimental results show that the mAP value of the improved YOLOv8 underwater dynamic target detection algorithm proposed in this article can reach 95.1%, and it can detect underwater dynamic targets more accurately, especially small dynamic targets in complex underwater scenes.

Enhancing shipyard transportation efficiency through dynamic scheduling using digital twin technology.

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers.

Low-background interference detection of glyphosate, glufosinate, and AMPA in foods using UPLC-MS/MS without derivatization.

The abuse of herbicides has emerged as a great threat to food security. Herein, a low-background interference detection method based on UPLC-MS was developed for the simultaneous determination of glufosinate, glyphosate, and its metabolite aminomethylphosphonic acid (AMPA) in foods. Initially, this study proposed a simple and rapid pretreatment method, utilizing water extraction and PRiME HLB purification to isolate glyphosate, glufosinate, and AMPA from food samples. After the optimization of pretreatment conditions, the processed samples are then analyzed directly by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) without pre-column derivatization. The method can effectively reduce interference from by-products of pre-column derivatization and background substrates of food sample, showing low matrix effects (ME) ranging from - 24.83 to 32.10%. Subsequently, the method has been validated by 13 kinds of food samples. The recoveries of the three herbicides in the food samples range from 84.2 to 115.6%. The limit of detection (LOD) is lower to 0.073mg/kg, 0.017mg/kg, and 0.037mg/kg, respectively. Moreover, the method shows an excellent reproducibility with relative standard deviations (RSD) within 16.9%. Thus, the method can provide high trueness, reproducibility, sensitivity, and interference-free detection to ensure human health safety.

Self-Supervised Transformers for Unsupervised SAR Complex Interference Detection Using Canny Edge Detector

As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system.

Support Vector Machines Based Mutual Interference Mitigation for Millimeter‐Wave Radars

With the intelligent development of vehicles, the number of vehicles equipped with millimeter‐wave (mmWave) radars is increasing, and the possibility of interference between radars is rising dramatically. In automatic driving, it will be common for target detection to be affected by multiple interfering radars. Addressing the mutual interference challenges, an adaptive interference detection method based on support vector machines (SVMs) is proposed. First, a window selection is performed on the received signal and features describing the difference between the normal signal and the interference are extracted. Then, we use a nonlinear SVM to distinguish between the interference and the normal signal. After completing the localization of the interference, we use an autoregressive (AR) prediction model to reconstruct the target echo signal. Results from both multiple interference simulation scenarios and real experimental scenarios show that the accuracy of interference localization and the effect of interference mitigation of the proposed method outperforms the mainstream methods.

A Novel GNSS Sweep Interference Detection and Mitigation Method Based on Radon–Wigner Transform

In this paper, a novel Radon-Wigner transform (RWT) based sweep interference detection and mitigation method has been proposed for global navigation satellite system (GNSS) receivers working in the disturbing scenarios. The proposed GNSS sweep interference detection and mitigation method has been tested using real GPS L1-C/A data disturbed by the sweep interference in a controlled test bench experiment. The results have shown that the proposed method can effectively deal with the cross-term problems with the conventional time-frequency (TF) analysis techniques and provide much improved GNSS interference detection performance in comparison to the existed Wigner-Ville distribution (WVD) and Wigner-Hough transform (WHT) approaches; and the characteristic parameters of the GNSS sweep interfering signal can be efficiently estimated according to the peak position of the RWT computed in the parameter space. In order to excite the GNSS sweep interfering signal, the dechirping and notch filtering techniques have been suggested to realize the GNSS anti-interference functionality, providing satisfactory anti-interference performance for GNSS receivers, this has been further verified by the GNSS signal acquisition experiment in the interfering environment.

FMCW Radar System Interference Mitigation Based on Time-Domain Signal Reconstruction.

In this study, an interference detection and mitigation method is proposed for frequency-modulated continuous-wave radar systems based on time-domain signal reconstruction. The interference detection method uses the difference in one-dimensional fast Fourier transform (1D-FFT) results between targets and interferences. In the 1D-FFT results, the target appears as a peak at the same frequency point for all chirps within one frame, whereas the interference appears as the absence of target peaks within the first or last few chirps within one frame or as a shift in the target peak position in different chirps. Then, the interference mitigation method reconstructs the interference signal in the time domain by the estimated parameter from the 1D-FFT results, so the interference signal can be removed from the time domain without affecting the target signal. The simulation results show that the proposed interference mitigation algorithm can reduce the amplitude of interference by about 25 dB. The experimental results show that the amplitude of interference is reduced by 20-25 dB, proving the effectiveness of the simulation results.

Presence of macroproteins on the measurement of vitamin B12: studying high vitamin B12 levels using polyethylene glycol and heterophile antibody blocking tubes

Immune methods are measurement systems that can be affected by interference. The interfering substance may be endogenous immunoglobulins, antibodies, analyte-antibody complexes, and heterophile antibodies. In our study, we aimed to investigate the effect of macrovitamin B12 on high vitamin B12 levels measured using immune methods. Among the serum samples studied in our laboratory; serum from 145 individuals was determined as the patient group and serum from 50 individuals was determined as the control group. We investigated the effect of macroprotein on B12 measurement by using polyethylene glycol (PEG) precipitation, heterophile antibody blocking tube (HBT) and serial dilution methods. According to the recovery evaluation and the modified reference range evaluation after precipitation with PEG, the macrovitamin B12 percentage in the patient group was 58 (40%) and 65 (44.8%), respectively. There was no decrease in vitamin B12 values after the study with HBT. A lack of linearity was observed in 23 (18.7%) of 123 sera with serial dilution method. Macrovitamin B12 should be considered as a differential diagnosis of unexpectedly high vitamin B12 levels. According to the results of our study, we think that it would be appropriate to precipitate with PEG first when there is a suspicion of macrovitamin B12. In collaboration with clinical and laboratory, we recommend that the patient’s clinical status and additional laboratory findings be examined, and the patient’s sample should be evaluated and interpreted by a specialist in biochemistry by selecting the appropriate interference detection method.

Mechanical Movement in Stamping Die Design Based on Computer Analysis

With the rapid development of the national economy, the supporting role of molds to the national industry is becoming more and more indispensable. The purpose of this paper is to study the control and application of mechanical motion in stamping die design based on computer analysis. According to the principle of stamping die, the related mechanical motion of the die in the stamping process and its definition method are sorted out, which lays a foundation for the mechanical motion control. The interference detection algorithm based on die stamping motion simulation is established, and the method of automatically acquiring CATIA digital-analog information is studied, which not only standardizes the die design process, but also provides a basis for the realization of simulation motion control. each movement model. The static interference detection method adopted by the system is determined, and the method of distinguishing normal interference and abnormal interference is analyzed. Combining the CATIA interference detection with the motion law and motion curve of the process parts, the analysis of the mechanical motion control system is completed, which is of great significance to improve the production efficiency of the stamping line and speed up the processing speed of the stamping parts.

Research on Detection Technology of Spoofing under the Mixed Narrowband and Spoofing Interference

The global navigation satellite system has achieved great success in the civil and military fields and is an important resource for space-time information services. However, spoof interference has always been one of the main threats to the application security of satellite navigation receivers. In order to further improve the application security of satellite navigation receivers, this paper focuses on the application scenarios where narrowband and spoofing interference exist at the same time, studies the problem of spoofing interference detection under mixed interference conditions, then proposes a spoofing interference detection method based on the tracking loop identification curve. This method can effectively deal with the detection of spoofing interference under the conditions of narrowband interference and, at the same time, it can effectively detect the spoofing interference of gradual deviation. Simulation experiments verify the effectiveness of the spoofing interference detection method, based on the tracking loop discrimination curve. In typical jamming and spoofing scenarios, when the spoofing signal is about 7.5 m away from the real signal, the method used in this paper can achieve effective detection. The proposed detection method is of great significance for improving the anti-spoofing capability of satellite navigation receivers.

Digital-twin driven energy-efficient multi-crane scheduling and crane number selection in workshops

In multi-crane systems, interference between cranes frequently occurs, and a significant amount of energy is wasted to avoid these interferences. Energy-efficient multi-crane scheduling and employing the required number of cranes are critical for saving energy. The energy consumption of a crane varies during operation; thus, it is important to accurately and effectively evaluate the operational energy consumption of a crane for different working phases. Digital twins (DTs) have various advantages such as virtual reality interaction, real-time mapping, and dynamic performance evaluation. Therefore, a DT-driven energy-efficient multi-crane scheduling and crane number selection approach is proposed in this study. The crane scheduling problem was described and modeled by considering energy consumption. Subsequently, a DT framework was established. Under this framework, a multi-crane system based on simulation was built in a virtual space. After inputting tasks into the virtual multi-crane system, a solution was generated based on the crane scheduling rules, interference detection, and avoidance methods. Then, the energy consumption of the program was evaluated. Finally, a case study was considered to illustrate the advantages of the proposed method.

An Automatic Generation Method of Exploded View Based on Projection

Exploded view is a kind of schematic view which decomposes the assembly into parts and arranges according to certain rules to explain the product structural relationship and guide the relevant technical personnel to carry out product assembly or maintenance. It is necessary to consider the visibility constraints and explosion sequence rules to construct the exploded view. Manual creation will lead to repeated modification and low efficiency. In this paper, a projection based algorithm for automatic generation of exploded view is proposed. In this method, the explosion set of products is constructed based on disassembly matrix and the assembly sequence of complex products is represented as a set of ordered hierarchical exploded view, so as to ensure that the exploded view can clearly express the feasible assembly sequence. The projection interference detection method is proposed to ensure the visibility of the exploded view under a given angle of view, and the method of selecting the explosion direction is proposed to improve the compactness of the generated exploded view, and the explosion direction guideline is established to improve the readability of the exploded view. The algorithm has small storage space, fast search speed, and is not limited to a single specific perspective, so it is more general. The feasibility and efficiency of the algorithm are verified by an example of automatic generation of explosive drawing of fixture model.

Compressive narrowband interference detection and parameter estimation in direct sequence spread spectrum communication

The Shannon-Nyquist sampling theorem that is based on narrowband interference (NBI) detection and parameter estimation methods in direct sequence spread spectrum (DSSS) communication is limited by the high sampling rate. Compressive sensing (CS) is adopted to address the problem. But it will change the signal nature, which leads to the unavailability of Shannon-Nyquist sampling theorem-based interference detection and parameter estimation methods. According to the different posterior probability distribution features of NBI, DSSS signal, and noise in compressed domain, a posterior probability model of whether NBI exists in the received signal is constructed by using the compressed measurements. The posterior probability that whether NBI exists in the received signal is employed as the feature parameters of NBI detection and parameter estimation. With the feature parameters detected, NBI detection can be achieved and the edge location of NBI components can be located. The relationship between the edge location of NBI components and the edge frequency of NBI is constructed, which will contribute to estimate the NBI edge frequency. The numerical simulation results demonstrate that the proposed method can effectively achieve NBI detection and parameter estimation in the compressed domain and it performs significantly better than the other existing methods.

Research and application on an embedded cabin non-contact detection and precision docking technology

In the assembly missions of spacecraft, the cabins docking are the final part of the product assembly to form the probe into a whole, which is one of the most critical parts of the assembly task. The spacecraft of Insertion and Containment in cabin docking faces difficulties such as eccentricity of mass, small safety spacing, and non-visibility. In this paper, interference detection method based on reverse engineering non-contact virtual assembly and precision docking process method with tiny spacing of cabin eccentricity were proposed, and a large eccentric inline cabin docking device was developed to solve the problem of inline cabin precision docking. The above-mentioned method was applied to achieve nearly 100 successful dockings during the AIT process, which ensured the success of the spacecraft mission.

A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform.

This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner–Ville distribution, pseudo -Wigner–Ville distribution and smoothed pseudo-Wigner–Ville distribution. This process results in the corresponding Wigner–Hough transform, pseudo-Wigner–Hough transform and smoothed pseudo-Wigner–Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner–Hough transform method is at least double that of the Wigner–Hough transform-based approach; the smoothed pseudo-Wigner–Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner–Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner–Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.

An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions

The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While these methods are based on JAEs measured in controlled settings, we anticipate that JAE measurements could enable accessible and affordable diagnosis of acute knee injuries also in field-deployable settings. However, in such settings, the noise and interference would be greater than in sterile, laboratory environments, which could decrease the performance of existing knee health classification methods using JAEs. To address the need for an objective noise and interference detection method for JAE measurements as a step towards field-deployable settings, we propose a novel experimental data augmentation method to locate and then, remove the corrupted parts of JAEs measured in clinical settings. In the clinic, we recruited 30 participants, and collected data from both knees, totaling 60 knees (36 healthy and 24 injured knees) to be used subsequently for knee health classification. We also recruited 10 healthy participants to collect artifact and joint sounds (JS) click templates, which are audible, short duration and high amplitude JAEs from the knee. Spectral and temporal features were extracted, and clinical data was augmented in five-dimensional subspace by fusing the existing clinical dataset into experimentally collected templates. Then knee scores were calculated by training and testing a linear soft classifier utilizing leave-one-subject-out cross-validation (LOSO-CV). The area under the curve (AUC) was 0.76 for baseline performance without any window removal with a logistic regression classifier (sensitivity = 0.75, specificity = 0.78). We obtained an AUC of 0.86 with the proposed algorithm (sensitivity = 0.80, specificity = 0.89), and on average, 95% of all clinical data was used to achieve this performance. The proposed algorithm improved knee health classification performance by the added information through identification and collection of common artifact sources in JAE measurements. This method when combined with wearable systems could provide clinically relevant supplementary information for both underserved populations and individuals requiring point-of-injury diagnosis in field-deployable settings.

A high order statistics based multipath interference detection method

A high order statistics based multipath interference detection method

A New GNSS Interference Detection Method Based on Rearranged Wavelet-Hough Transform.

Since radio frequency interference (RFI) seriously degrades the performance of a global navigation satellite system (GNSS) receiver, interference detection becomes very important for GNSS receivers. In this paper, a novel rearranged wavelet–Hough transform (RWHT) method is proposed in GNSS interference detection, which is obtained by the combination of rearranged wavelet transform and Hough transform (HT). The proposed RWHT method is tested for detecting sweep interference and continuous wave (CW) interference, the major types of GNSS interfering signals generated by a GNSS jammer in a controlled test bench experiment. The performance of the proposed RWHT method is compared with the conventional techniques such as Wigner–Ville distribution (WVD) and Wigner–Hough transform (WHT). The analysis results show that the proposed RWHT method reduces the influence of cross-item problem and improves the energy aggregation property in GNSS interference detection. When compared with the WHT approach, this proposed RWHT method presents about 90.3% and 30.8% performance improvement in the initial frequency and chirp rate estimation of the GNSS sweep interfering signal, respectively. These results can be further considered to be the proof of the validity and effectiveness of the developed GNSS interference detection method using RWHT.

Carrier phase double-difference GNSS spoofing detection technology based on generalized likelihood ratio

In recent years, with the wide application of Global Navigation Satellite System (GNSS), the security problem of GNSS has attracted wide attention. Spoofing interference will become an important threat to the security of satellite navigation information because of its concealment. In this paper, we propose a new double antenna structure spoofing interference detection method according to the spatial characteristics of GNSS signal. The test statistics are constructed by the generalized likelihood ratio (GLR) theory, and the GNSS spoofing interference detection methods under the same direction spoofing interference and different direction spoofing interference are obtained respectively. The feasibility of this method is verified by simulation.