Adaptive Threshold Detection Research Articles

Distributed Passive Positioning and Sorting Method for Multi-Network Frequency-Hopping Time Division Multiple Access Signals

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods.

UAV Anomaly Detection Method Based on Convolutional Autoencoder and Support Vector Data Description with 0/1 Soft-Margin Loss

Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1 loss function, with the goal of enhancing the accuracy and robustness of anomaly detection. Since the 0/1 loss function is a bounded, non-convex, and non-continuous function, this paper proposes the Bregman ADMM algorithm to solve the L0/1-SVDD. Finally, the difference between the reconstructed and the actual value is employed to train the L0/1-SVDD, resulting in a hypersphere classifier that is capable of detecting UAV anomaly data. The experimental results using real flight data show that, compared with methods such as AE, LSTM, and LSTM-AE, the proposed method exhibits superior performance across five evaluation metrics.

Methodology for assessing the effectiveness of the impact intermittent noise interference to a radar station with adaptive detection threshold formation

Problem statement. For aviation suppression equipment, it is characteristic to separate the modes of interference and reconnaissance radiation in time due to the problem of their electromagnetic compatibility. It is also possible to alternately suppress various objects with a limited bandwidth of the interference station. This leads to periodic interruptions of the noise interference and, consequently, a decrease in its spectral density. A feature of radar stations of the latest and modernized means of intercepting air defense systems aimed at hitting targets in a complex interference environment is adaptive control of parameters and modes of operation of various subsystems. In particular, modern radar stations provide for the adaptive formation of a detection threshold based on estimates of the statistical characteristics of such interference. Such estimates are formed over the interference integration interval, the size of which is generally selected from the condition of insignificant influence of signals reflected from the targets on them. Moreover, for the most correct consideration of the interference situation in the vicinity of the detected target, the strobe pulses of the false alarm probability stabilization circuit, forming the integration interval, are placed on both sides of the analyzed range resolution element. Goal. To evaluate the effect of intermittent noise interference parameters on the operation of radar stations with adaptive detection threshold foration. Assumptions. When developing the methodology, the following typical assumptions were made: in target detection mode, the radar station emits pulse signals with a low or medium pulse repetition rate; the signal reflected from the target, noise interference and internal noise are independent normal random processes; interference and internal noise have a uniform spectral density within the bandwidth of the radar receiver; the receiving path of the radar station has a traditional structure, which includes: an intermediate frequency amplifier, a linear amplitude detector, an integrator and a threshold device. Results. For the first time, the obtained technique takes into account the influence of time and energy parameters of intermittent noise interference on the probability of correct target detection by a radar station with adaptive detection threshold formation. Reducing the duration of the interference pulse in the period of intermittent noise interference leads to a nonlinear increase in the probability of correct target detection. To maximize this indicator, it is necessary to ensure a sufficiently high accuracy in estimating the statistical characteristics of the power of intermittent noise interference. The reliability of the results obtained using the developed methodology is confirmed by the fact that the effectiveness of intermittent noise interference, while excluding pauses in its radiation, is reduced to the effectiveness of continuous noise interference. Practical significance. The developed technique makes it possible to clarify the technical requirements for radar stations that implement flexible control of the target detection mode under the influence of intermittent noise interference of unknown intensity.

Evaluation of a Condition Monitoring Algorithm for Early Bearing Fault Detection.

Roller bearings are critical components in various mechanical systems, and the timely detection of potential failures is essential for preventing costly downtimes and avoiding substantial machinery breakdown. This research focuses on finding and verifying a robust method that can detect failures early, without creating false positive failure states. Therefore, this paper introduces a novel algorithm for the early detection of roller bearing failures, particularly tailored to high-precision bearings and automotive test bed systems. The featured method (AFI-Advanced Failure Indicator) utilizes the Fast Fourier Transform (FFT) of wideband accelerometers to calculate the spectral content of vibration signals emitted by roller bearings. By calculating the frequency bands and tracking the movement of these bands within the spectra, the method provides an indicator of the machinery's health, mainly focusing on the early stages of bearing failure. The calculated channel can be used as a trend indicator, enabling the method to identify subtle deviations associated with impending failures. The AFI algorithm incorporates a non-static limit through moving average calculations and volatility analysis methods to determine critical changes in the signal. This thresholding mechanism ensures the algorithm's responsiveness to variations in operating conditions and environmental factors, contributing to its robustness in diverse industrial settings. Further refinement was achieved through an outlier detection filter, which reduces false positives and enhances the algorithm's accuracy in identifying genuine deviations from the normal operational state. To benchmark the developed algorithm, it was compared with three industry-standard algorithms: VRMS calculations per ISO 10813-3, Mean Absolute Value of Extremums (MAVE), and Envelope Frequency Band (EFB). This comparative analysis aimed to evaluate the efficacy of the novel algorithm against the established methods in the field, providing valuable insights into its potential advantages and limitations. In summary, this paper presents an innovative algorithm for the early detection of roller bearing failures, leveraging FFT-based spectral analysis, trend monitoring, adaptive thresholding, and outlier detection. Its ability to confirm the first failure state underscores the algorithm's effectiveness.

Pore Segmentation in Industrial Radiographic Images Using Adaptive Thresholding and Morphological Analysis

This study presents an innovative approach to segmenting pores in low-quality industrial radiographic images using advanced image processing and adaptive segmentation techniques. The process involves several steps, including conversion of the image to grayscale to simplify analysis. Gaussian and median filters are then applied to improve image quality and enable more accurate segmentation. The methodology is based on adaptive thresholding and edge detection using the Canny algorithm, achieving accurate pore area measurements, even for those unnoticed by experts, with measurement errors of less than 0.1 mm². This high accuracy supports the method's effectiveness in pore detection and measurement. The resulting tool has great potential in industrial radiography applications, from quality control to defect analysis. While recognizing the need for continued research to improve contrast and illumination, as well as to address potential sources of error, these refinements promise to further elevate an already reliable and accurate method with a wide reach in the industry.

The line-of-sight peak detection and tracking of underwater acoustic DSSS communications in the doubly spread channel

In the underwater acoustic (UWA) channel, the line-of-sight (LOS) path has the characteristics of stable magnitude and continuous phase, which is seldom affected by the surface/bottom reflection. Therefore, in the direct sequence spread spectrum (DSSS) communication systems, the receiver usually extracts the LOS path to recover the information bits and achieve the robust communication. However, the UWA channel is typically the doubly spread channel, i.e., multipath fast fading and non-uniform Doppler shifts, which seriously degrades the performance of extracting the LOS path by the conventional maximum energy method. Therefore, this paper proposes a LOS peak detection and tracking algorithm to extract the LOS path in the UWA DSSS communications. In the proposed algorithm, the candidate correlation peaks after de-spreading are firstly selected by the adaptive peak detection threshold. Then, according to the transmitter/receiver motion relationship, the Delay-Doppler joint state equation is established, and thus the nearest neighbor method adopting the Kalman filter is proposed to track the LOS path. Numerical simulation results demonstrate that the proposed algorithm has better processing performance in the doubly spread channels compared with the conventional method. Finally, the performance of the proposed algorithm is confirmed by the shallow water horizontal channel and deep sea vertical channel experiments.

Unmanned aerial vehicle remote sensing image registration based on an improved oriented FAST and rotated BRIEF- random sample consensus algorithm

Unmanned Aerial Vehicle (UAV) remote sensing image registration is the key step of remote sensing image stitching, image fusion and multi-frames image super-resolution. Its speed and accuracy determines the effect of remote sensing image applications, such as object detection, environment monitoring. To meet the speed and accuracy requirements of UAV remote sensing image registration, an improved Oriented FAST and Rotated BRIEF- Random sample consensus (ORB-RANSAC) algorithm is proposed. Firstly, images to be registered are divided into non-overlapping sub-images, and then a simplified image pyramid is constructed for these sub-images to get scale invariance. Secondly, the traditional FAST corner detection algorithm is improved by setting the adaptive corner detection threshold, and more feature points are detected. Meanwhile, the traditional quadtree algorithm is improved to remove redundant feature points and keep the remaining high-quality feature points. Thirdly, feature points coarse matching is done by bidirectional matching combined with cosine similarity method. Finally, the improved RANSAC algorithm is used for feature points fine matching to eliminate mismatches and calculate the transformation matrix. Experiment results show that, comparing with the traditional ORB algorithm, the number of feature points detected is significantly increased and its distribution is more uniform, and the correct matching rate increases by 58.10% in the case of image scale changing. Comparing with the state-of-the-art UAV remote sensing image registration algorithm, the correct matching rate and mutual information of our method are increased by 0.68% and 1.91% respectively, matching time and root mean square error are reduced by 3.89% and 11.2% respectively.

Adaptive robust FDI attack detection for cyber–physical​ systems with disturbance

This paper investigates the problem of attack detection for cyber–physical systems (CPSs) with disturbances, measurement noises, and false data injection (FDI) attacks. A classical linear discrete-time system attack model is constructed and a robust attack detector based on the mixed H_/H ∞ is designed. Firstly, a system with an actuator that suffered a malicious attack is modeled. Then, a robust attack detector based on the mixed H_/H ∞ is designed in which the H_ index and H ∞ index are used to characterize the sensitivity to attacks and robustness to disturbances and measurement noises, respectively. And an adaptive detection threshold with a compensation term is proposed. Besides, the designed robust attack detector enables the attack detection dynamic system to be asymptotically stable and to guarantee the H_/H ∞ performance, and the robust attack detector gains are solved from a convex optimization. Finally, the obtained theoretical results are validated through a numerical simulation and a three-area power system simulation.

Track Detection of Underwater Moving Targets Based on CFAR

In this paper, we propose a 2D-Weibull-Constant False Alarm Rate (2D-Weibull-CFAR) detection algorithm to solve the problem that detecting current underwater targets is difficult due to the influence of reverberation noise. Specifically, referring to the idea that CFAR uses the probability distribution of reference units to detect objects, this paper introduces the pixel distribution of reverberation noise into the CFAR detector. After that, the probability distribution of the extracted reference units is estimated, and then the adaptive detection threshold is obtained to achieve reliable detection of underwater targets. Finally, the Hough transform extracts the trajectory of the detection results. The experimental test shows that the algorithm in this paper can solve the problem of false alarms and missed alarms in detecting targets hidden in the reverberation noise. The algorithm in this paper can effectively detect the target in the reverberation noise. The detection results show that the algorithm in this paper has higher accuracy and lower false alarm rate than the comparison algorithm.

Heuristic adaptive threshold detection method for neuronal spikes

AbstractIn recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high‐precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain‐computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike‐detection algorithm that removes noise and reduces the phase shift using a zero‐phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes.

Intelligent Spectrum Sharing and Sensing in Cognitive Radio Network by Using AROA (Adaptive Rider Optimization Algorithm)

Wireless spectrum has been allocated to licensees for large geographic areas on a long-term basis in recent years. Cognitive Radio Networks (CRN) will offer mobile users with a huge amount of available bandwidth. Due to spectrum management issues such as spectrum sensing and sharing, CRN networks pose some challenges. Hence in this paper, Adaptive Rider Optimization (AROA) is developed to improve the energy efficiency for different spectrum sensing conditions. The proposed algorithm is utilized to compute the sensing time, sequence length, and detection threshold. In order to detect the spectrum with optimal values of transmission power and sensing bandwidth, the AROA uses the adaptive threshold detection method. The spectrum sensing and sharing of the CRN network are achieved with the help of the AROA algorithm. The proposed method is implemented in MATLAB and the performances such as Normalized Energy consumption, delay, SNR, Jitter, blocking probability, convergence analysis, and Throughput are evaluated. The proposed method is contrasted with the existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), respectively.

Uncertainty-Aware LSTM Based Dynamic Flight Fault Detection for UAV Actuator

Accurate fault detection for unmanned aerial vehicle (UAV) actuators is essential for ensuring flight safety and mission completion. Without the requirement of modeling complex physical mechanism, data-driven actuator fault detection approaches have attracted much attention. Among them, the long short-term memory (LSTM) approach has shown superior performance due to its capability of modeling complex spatial–temporal features. However, the modeling uncertainty of LSTM is actually changeable under different flight conditions, which has not been well considered in the existing researches. In this article, a novel uncertainty-aware LSTM (UA-LSTM) based dynamic flight condition fault detection approach for UAV actuator is proposed. A prediction-based fault detection model is first set up based on LSTM, utilizing the information from both the flight action modes and the actuator effect. Its inputs are specifically selected by both physical mechanism and data correlation analysis. Furthermore, time-series features indicating the prediction uncertainty of the model are constructed on the selected inputs to characterize dynamic flight conditions more accurately. Then, an adaptive threshold estimation space is set up based on an enhanced distribution-based condition clustering approach. Fault detection thresholds for various flight conditions are obtained and are smoothed to reduce the influence of disturbances. Finally, the fault detection model with stepwise adaptive detection threshold is acquired. Experimental results on both simulation and real flight data illustrate that the proposed approach is superior for actuator fault detection under dynamic flight conditions.

Spectral minima tracking for improving the energy detection under noise power uncertainty

Spectrum sensing involves identifying the unused license bands in Cognitive Radio Networks. Energy Detection (ED) is the most widely studied form of spectrum sensing. ED detects potentially unknown signals and it is mainly characterized by its low complexity. However, the ED performance is considerably deteriorated by the presence of noise uncertainty (NU). To increase the robustness of ED in NU scenarios, in this work we propose to use an adaptive detection threshold, which is calculated from the noise power estimate obtained at each sensing interval. The noise power is estimated using the technique called Spectral Minima Tracking (SMT). Moreover, a correlation analysis is conducted to quantify the degree of relationship between the parameters of the SMT technique and the resulting noise power estimate. The findings made in the correlation analysis allowed to adjust the parameters of the SMT technique, and thus giving place to the improvement of the quality of the noise power estimate. Simulation results show an improvement in the detection rate of the adaptive ED due to the reduction of the bias in the noise power estimate, as compared to the non-adaptive ED.

Dynamic Reduced-Order Observer-Based Detection of False Data Injection Attacks With Application to Smart Grid Systems

This article investigates the problem of attack detection of false data injection attacks for a class of large-scale smart grid systems in the context of cyber–physical systems. First, by exploiting the graph theory to decompose the considered system into multiple interconnected subsystems, a bank of dynamic reduced-order observers are delicately constructed to generate residual signals for the attack detection task. Then, a novel decentralized attack detection scheme is proposed based on the adaptive detection thresholds with prescribed performance. Compared with the existing results, the proposed detection scheme has less conservative thresholds and enhanced robustness against process disturbance and measurement noise, such that the detectability is improved. Finally, the effectiveness and availability of the proposed scheme are verified by two simulation examples and the experimental results from IEEE 30-bus system built in the OPAL-RT real-time simulator.

An automatic defect-inspection method for optical isolators using image analysis

Abstract Manual inspection is still widely used for defect detection in optical isolator factories. The manual method is not only inefficient, but also low reliability, and easy to be disturbed by visual fatigue. This paper proposes an automatic defect detection algorithm for optical isolators, which is an extension to a previous conference paper, with more algorithms and detailed description of the detection algorithm added. In the focusing part of the algorithm, an improved Laplace operator which increases the attention of oblique gradient is adopted. In the detection part of the algorithm, the image-enhancement based on adaptive threshold, the line and ring detection based on Hough transform, and process the detection results by clustering method are used. Experiments show that the scheme proposed in this paper can inspect a series of defects on the crystal area of the optical isolator in real time, the manpower demand can be reduced to less than 10 % and the detection accuracy is 93 %.

Detection and Isolation of False Data Injection Attack in Intelligent Transportation System via Robust State Observer

As the future of transportation systems, the intelligent transportation system is a promising technology to improve the increasingly serious traffic problems. However, the integration of cyber-physical systems makes them vulnerable to new cyber–physical attacks. To ensure the security of intelligent transportation systems, a novel robust state observer-based detection and isolation method against false data injection attacks is developed. Based on the constructed dynamic model of intelligent vehicle networking, the covert characteristic of a false data injection attack is analyzed. Then, a novel state residuals-based detection criterion is developed by using a real-time observed state. To shorten the detection time, an adaptive detection threshold is designed to replace the existing computed threshold. In addition, robust state observer banks are established to isolate multiple injected attacks. Finally, simulation results on the vehicle networking system demonstrate the effectiveness of the developed detection and isolation method against false data injection attacks.

An Adaptive Threshold Detection Circuit for Action Potential

In order to find the action potential hidden in the neural signal, this study proposes a method of detecting the action potential through adaptive threshold, and designs an adaptive threshold detection circuit which can be used for the action potential. Different from the past, the circuit controls the gain by the current of the input signal itself, so as to generate an appropriate adaptive comparison threshold. This circuit not only retains the advantages of the traditional threshold detection method, but also can quickly respond to the input signal, generate an appropriate comparison threshold and detect the action potential without external supervision. The spectre simulation shows that when the power supply voltage is 1.8V, the adaptive threshold generation circuit can accurately detect the action potential without missing or wrong detection.

Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition

The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.

Stateful-Service-Based Pupil Recognition in Natural Light Environments.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Cognitive radio is one of the most promising technologies due to the spectrum scarcity, especially in the microwave band. Spectrum sensing forms an essential functionality for CR systems. However, such detection performance is usually compromised by shadowing and fading channel conditions. Cooperative sensing is one of the crucial solutions to overcome degraded detection performance. A distributed architecture for the processing and fusion of sensing information is proposed to improve sensing performance and reduce reporting error. The decision fusion for cooperated users could be complex in dense network scenarios and reported sensing traffic may require significant bandwidth. The paper presents a new cooperative distributed PUs detection and proposes dynamic threshold based on controlled probability of false alarm to enhance sensing efficiency and reliability in a Rayleigh fading environments. A cooperative distributed PUs detection is realized by fusion node (FNs) that dynamically selected from the group members. The adaptive detection threshold is computed dynamically using the link quality indicators (LQIs) of the sensing channels. Moreover, the proposed scheme can significantly minimize the typically transmitted bits in the reporting channels. This work also deliberated the design parameters of the CR on the performance of fusion values. The simulation evaluation presents that the adapted threshold considerably improves the performance of the distributed cooperative sensing (DCS) process. The simulation results verified that the error was minimized remarkably. The ROC graph is notably enhanced for the sensing process in term of probability of detection and probability of false alarm. Finally, it was presented that with the proposed scheme the sensitivity requirements could be significantly enhanced up to 0.95.