Adaptive Detection Research Articles

Moving-Target Detection for FDA-MIMO Radar in Partially Homogeneous Environments

This paper delves into the problem of moving-target detection in partially homogeneous environments (PHE) with unknown Gaussian disturbance using a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Using training data, we have derived expressions for four adaptive detectors, including the one-step and two-step generalized likelihood ratio test (GLRT), two-step Rao (TRao) test, and two-step Wald (TWald) test criteria, respectively. All the proposed detectors are characterized by the constant false-alarm rate (CFAR). The theoretical analysis and simulation results validate the effectiveness of the proposed detectors.

Trajectory Predictor and Conflict Detection Figures of Merit for a Performance-Based Adaptive Air Traffic Monitoring System

This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar track data. To achieve these objectives, two figures of merit were proposed: System Dynamic Range and System Tuning Envelope. To examine the conflict detection’s performance variability under different uncertainty levels and predictor types, simple multi-horizon trajectory predictors trained with two machine learning techniques of different characteristics are assessed at various look-ahead times: extreme gradient boosting with a discrete nature and a multi-layer perceptron regressor with a continuous nature. The results highlight the interdependence between the performances of the trajectory predictor and the conflict detector, and the quantification of this relationship can be represented through a sigmoid function. In addition, the two proposed figures of merit are effective for selecting suitable operating points in an adaptive conflict detector, based on dynamic thresholds and the performance requirements necessary for the trajectory predictors to achieve the expected detection performance at different look-ahead time.

A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies

Achieving the balance between convergence and diversity is a key and challenging issue in many-objective optimization. Reference vector guided selection is an exemplary method for decomposition-based many-objective evolutionary algorithms (MaOEAs). However, there are some problems with it such as insufficient number of obtained solutions and inefficient convergence evaluation metric. Aiming at solving or alleviating these problems, this paper proposes a many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategies. The proposed algorithm introduces two new strategies namely adaptive sparse region detection and convergence-only selection. The former is to adaptively detect sparse regions of current elite population, while the latter is to prevent the elimination of solutions with prominent convergence performance. Together with a newly proposed elite retention strategy, these two strategies can achieve diversity and convergence enhancement on the basis on reference vector guided selection. Besides, A new selection criterion for reference vector guided selection is proposed to better measure the convergence of solutions in high dimensionality. Experimental results on widely used test problem suites up to 15 objectives indicate that the proposed algorithm is highly competitive in comparison with seven state-of-the-art MaOEAs.

Adaptive event-triggered fault detection filter for unmanned surface vehicles against randomly occurring injection attacks

This paper focuses on the problem of event-triggered fault detection for network-based unmanned surface vehicles (USVs) subject to network-induced delay, external disturbance, and cyber attacks. First, a novel adaptive event-triggered strategy is presented to relieve the bandwidth burden of the communication network while mitigating the impact of cyber attacks on the USV performance. Second, the network-induced communication delay is characterized as a distributed delay with a probability density function as its kernel. Third, to identify the time when the fault occurs, a fault detection filter (FDF) is designed to obtain a residual system. Based on the established framework, criteria for asymptotic stability analysis are derived via the Lyapunov–Krasovskii functional approach, and then the co-design of FDF and the event-triggered parameter matrix is given by the matrix transformation technique. Finally, simulation results are provided to substantiate the feasibility of the proposed approach.

An adaptive lightweight small object detection method for incremental few-shot scenarios of unmanned surface vehicles

Real-time and accurate detection of sea surface objects has become an important research topic for unmanned surface vehicles (USVs). During the execution of tasks, USVs sometimes need to upgrade their model to detect new categories, and the initial data is often limited. This requires quick adaptation to new categories under few-shot scenarios. We propose a lightweight neural network for detecting small sea surface objects named Shuffle-High-Resolution-Net (SHRDet), which integrates the enhanced Shuffle Block based on High-Resolution-Net (HRNet), lightweight feature fusion module, and Focal Efficient Intersection over Union loss. Based on SHRDet, a fast adaptation method named SHRDet-N for incremental few-shot categories is proposed. It generates category enhancement features through a cross-attention mechanism, and introduces elastic weight consolidation and feature distance to solve catastrophic forgetting when learning incremental few-shot categories. The algorithms have been applied to an intelligent USV platform for various surface missions, such as security patrol, ocean investigation, and marine engineering. The experimental results on public datasets indicate that SHRDet achieves 80.7 % mean Average Precision (mAP) on the Water Surface Object Detection Dataset (WSODD) with only 0.69 M parameters and less calculation quantity, and SHRDet is significantly superior to state-of-the-art methods in terms of lightweight and accuracy. Moreover, SHRDet-N effectively solves the learning problem of incremental few-shot categories. When the sample number of a new category is set to 20, the mAP of the base categories is 82.5 % and that of the new category is 63.8 %, which is 2.8 % and 4.2 % higher than that of state-of-the-art models like Sylph.

Residual current detection method based on improved VMD-BPNN.

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.

AdvMix: Adversarial Mixing Strategy for Unsupervised Domain Adaptive Object Detection

Recent object detection networks suffer from performance degradation when training data and test data are distinct in image styles and content distributions. In this paper, we propose a domain adaptive method, Adversarial Mixing (AdvMix), where the label-rich source domain and unlabeled target domain are jointly trained by the adversarial feature alignment and a self-training strategy. To diminish the style gap, we design the Adversarial Gradient Reversal Layer (AdvGRL), containing a global-level domain discriminator to align the domain features by gradient reversal, and an adversarial weight mapping function to enhance the stability of domain-invariant features by hard example mining. To eliminate the content gap, we introduce a region mixing self-supervised training strategy where a region of the target image with the highest confidence is selected to merge with the source image, and the synthesis image is self-supervised by the consistency loss. To improve the reliability of self-training, we propose a strict confidence metric combining both object and bounding box uncertainty. Extensive experiments conducted on three benchmarks demonstrate that AdvMix achieves prominent performance in terms of detection accuracy, surpassing existing domain adaptive methods by nearly 5% mAP.

A data‐driven adaptive geospatial hotspot detection approach in smart cities

AbstractHotspot detection from geo‐referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi‐density urban data. In this article, a data‐driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small‐scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo‐information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.

AdaDet: An Adaptive Object Detection System Based on Early-Exit Neural Networks

Recent researchers have proposed adaptive inference methods with an early-exiting mechanism, which stops the inference procedure of input if the prediction is with high confidence, leading to a fine-grained resource allocation based on the complexity of inputs. However, the adaptive inference strategy can only be applied for image classification tasks, such a system for object detection is still under-investigated since it lacks an appropriate way to measure the uncertainty of detection results for multiple objects in an image. To address this issue, in this paper, we propose a novel adaptive object detection system (AdaDet) based on early-exit neural networks. By using stochastic variables to represent localization predictions, our method can measure the uncertainty of the detection result for an object, based on which we further develop an entropy-based criterion to estimate the reliability of the whole detection results for an input image. With the proposed method, samples that achieve high-confidence detection results will exit early from the detection model, leading to low computational costs for inferring these samples. While only complex images need to finish the whole computational graph to achieve better detection results during inference. We experimentally demonstrate that the proposed AdaDet enhances detection performance under anytime prediction and adaptive inference settings on the most common dataset (MS COCO) used in this field.

Nonlinear Filtering and Reinforcement Learning Based Consensus Achievement of Uncertain Multi-Agent Systems

Abstract This paper introduces a novel approach for designing estimators to achieve consensus in uncertain multi-agent systems (MAS), even when various fault conditions are present and communication is assumed to be undirected and connected. The method includes an adaptive fault detection technique to detect faults and a unique adaptation in the unscented Kalman filter (UKF) to adjust noise covariance matrices and reconstruct uncertain states in the multi-agent system is proposed in the framework of Q-learning. Additionally, it involves training neural network internal parameters using previous measurements. A Chebyshev neural network (CNN) is employed to model the uncertain plant, and a hyperbolic tangent-based robust control term is used to mitigate neural network approximation errors. This novel approach is known as reinforced UKF (RUKF). The paper also discusses the asymptotic stability of the proposed method and presents numerical simulations to demonstrate its effectiveness with reduced computational load.

Adaptive novelty detection with false discovery rate guarantee

Adaptive novelty detection with false discovery rate guarantee

A Metacognitive Approach to Adaptive Radar Detection

A Metacognitive Approach to Adaptive Radar Detection

Adaptive Energy Theft Detection in Smart Grids Using Self-Learning With Dual Neural Network

Adaptive Energy Theft Detection in Smart Grids Using Self-Learning With Dual Neural Network

Adaptive Detection of Range-Spread Targets in Homogeneous and Partially Homogeneous Clutter Plus Subspace Interference

Adaptive Detection of Range-Spread Targets in Homogeneous and Partially Homogeneous Clutter Plus Subspace Interference

Quantum-inspired adaptive loss detection and real-time image restoration for live optical quantum image transmission

Quantum-inspired adaptive loss detection and real-time image restoration for live optical quantum image transmission

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

AbstractPoisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

Retracted: Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Retracted: Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Array-Designed Triboelectric Nanogenerator for Healthcare Diagnostics: Current Progress and Future Perspectives

Array-designed triboelectric nanogenerators (AD-TENGs) have firmly established themselves as state-of-the-art technologies for adeptly converting mechanical interactions into electrical signals. Central to the AD-TENG’s prowess is its inherent modularity and the multifaceted, grid-like design that pave the way to robust and adaptable detection platforms for wearables and real-time health monitoring systems. In this review, we aim to elucidate the quintessential role of array design in AD-TENGs for healthcare detection, emphasizing its ability to heighten sensitivity, spatial resolution, and dynamic monitoring while ensuring redundancy and simultaneous multi-detection. We begin from the fundamental aspects, such as working principles and design basis, then venture into methodologies for optimizing AD-TENGs that ensure the capture of intricate physiological changes, from nuanced muscle movements to sensitive electronic skin. After this, our exploration extends to the possible cutting-edge electronic systems that are built with specific advantages in filtering noise, magnifying signal-to-noise ratios, and interpreting complex real-time datasets on the basis of AD-TENGs. Culminating our discourse, we highlight the challenges and prospective pathways in the evolution of array-designed AD-TENGs, stressing the necessity to refine their sensitivity, adaptability, and reliability to perfectly align with the exacting demands of contemporary healthcare diagnostics.

Adaptive Orthogonal Basis Function Detection Method for Unknown Magnetic Target Motion State

Traditional methods of orthogonal basis function decomposition have been extensively used to detect magnetic anomaly signals. However, the determination of the relative velocity between the detection platform and the magnetic target remains elusive in practical detection. And, the non-ideal uniform motion of the magnetic target further complicates the process and adversely impacts the detector’s performance. To address this challenge, this paper introduces an adaptive scale factor method based on orthogonal basis function decomposition. This new method can be used to adjust the relative velocity between the detection platform and the magnetic target and to refine the characteristic time in the basis functions. In this paper, a mathematical relationship between the scale factor and the relative velocity is established, which is subsequently fitted into a Gauss function curve. The optimal scale factor, denoted as β, is adaptively chosen from the fitting curve when the magnetic target moves at a non-ideal uniform velocity with an unknown motion state. The results of simulations indicate that the scale factor improves the signal-to-noise ratio of the magnetic anomaly signals in a non-ideal state. And, this method can improve the energy value of OBF decomposition by 17.7%. Simultaneously, this method ensures that the moment the magnetic target passes the CPA coincides with the energy peak of the orthogonal basis detection, which improves the accuracy by 54.1% compared with the traditional method. The effectiveness and precision of the proposed method are verified using simulations and practical experiments.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

BackgroundDengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.MethodsTo evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document}, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.ResultsIn the simulation study, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.ConclusionsBayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.