Search Results (742)

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

With the advancement in electronic attack technologies, intelligent jamming poses a significant challenge to the reliable transmission of wireless communications. Traditional anti-jamming methods often fail to adapt to dynamic nonlinear jamming environments. This paper addresses nonlinear swept-frequency jamming by modeling anti-jamming communication as a sequential decision-making problem and proposes an intelligent anti-jamming method based on proximal policy optimization (PPO) to optimize dynamic channel selection. Firstly, the channel selection problem is formalized as a Markov decision process (MDP), where a state space integrating jamming patterns and communication status is designed, the channel set is defined as the action space, and a multi-objective reward function trades off jamming avoidance against switching overhead. A dual-network architecture comprising a policy network and a value network is constructed, and the PPO algorithm is employed for policy updates, where a clipping mechanism is used to enhance training stability. The system optimizes the anti-jamming strategy online through a closed-loop process of “sensing–decision–learning–communication”. Simulation results demonstrate that compared to conventional methods, the proposed method significantly improves key performance indicators such as packet success rate and throughput. It can rapidly track changes in jamming, exhibiting excellent real-time performance and environmental robustness, and thus provides an effective solution for reliable communication in dynamic jamming environments. Full article

We present a novel framework for modeling reaction time distributions in the context of video games aimed at providing a performance tool to support the design of new levels. Modern games generate rich behavioral telemetry (including reaction times, success rates, and interaction patterns) that can be leveraged to understand player behavior and inform adaptive game design. Given a set of general parameters describing a newly designed level, the framework predicts the corresponding reaction time distribution, offering actionable insight during the design process. To address this problem, we employ a combination of statistical fitting via maximum likelihood estimation, weighted approximations, and Lipschitz-based estimators in Wasserstein space. This mathematical framework establishes the groundwork for future AI-based extensions, using metric-space learning to predict distributions for unseen level configurations. The methodology provides theoretical guarantees under mild mathematical assumptions, ensuring bounded estimation errors through the assumption of Lipschitz continuity. Three approaches are proposed, all grounded in a Lipschitz characterization of the metric model parameters, which embeds the vector representation of levels into the 1-Wasserstein space of reaction time distributions. The practical applicability of the framework is demonstrated on a dataset of 480 gameplay observations across 24 participants and 20 distinct trials, testing all three fitting procedures on a set of representative examples. Full article

Public health emergencies require spatial crowdsourcing platforms to finish urgent tasks while limiting unnecessary movement across regions. Most online task assignment studies focus on profit, travel distance, latency, task coverage, or service quality. However, isolation sensitive scenarios need a different assignment goal. In such scenarios, regional crossings should be directly controlled during worker–task matching. This paper studies an isolation sensitive online task assignment problem in spatial crowdsourcing. The service space is modeled as a regional adjacency graph. The matching objective combines cross-region movement cost, an urgency reward for delayed task completion, and a dummy no-assignment cost for carry-over decisions. To handle dynamic arrivals, a time-sliced online process is used. Unfinished tasks are carried over to later time slots, and the priority of each carried-over task increases with waiting time. Based on this framework, we design two algorithms. OnlineKM serves as the basic priority-aware online matching algorithm. OnlineKM builds a matching problem in each time slot and applies KM-based partial matching with the information currently available. OnlineARC further uses δ-balanced adaptive regional coarsening. OnlineARC merges adjacent regions according to recent supply–demand balance before matching. This step adjusts the regional granularity used for movement cost evaluation and helps keep assignments close to local regions when regional merging is suitable. Experiments are conducted using a real-world task locations dataset from a 2022 COVID-19-related scenario in Changchun, with simulated worker availability and online arrivals. The results show that the proposed methods usually reduce the combined assignment objective value under the tested settings. The service quality and movement control metrics show that OnlineARC reduces the cross-region assignment ratio and average hop distance while maintaining a high task completion rate under the representative setting. OnlineKM improves running efficiency through time-sliced matching, while OnlineARC provides a trade-off between adaptive coarsening cost and locality-aware movement cost evaluation. These results suggest that adaptive regional coarsening can serve as a practical heuristic for locality-aware online task assignment in isolation sensitive spatial crowdsourcing under suitable worker–task distributions. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

In UAV-based remote sensing, accurate and efficient image mosaicing is crucial for achieving real-time monitoring. Traditional cloud-centric processing paradigms, however, face core scientific challenges such as high latency, bandwidth bottlenecks, and limited autonomy, making them inadequate for dynamic, real-time scenarios. To address these issues, this paper proposes an edge-computing-enabled UAV image mosaicing system. The system consists of a UAV remote sensing platform and an edge computing terminal, with the core being our novel B-SIFT-ILS algorithm. The algorithm first uses geographic coordinates for unified registration, constructs a Gaussian scale space for multi-resolution representation, and then precisely locates extrema in the Difference of Gaussian (DoG) space using a 3D quadratic function. A BANSAC algorithm is subsequently employed to refine feature points and extract stable SIFT features, and finally, Iterative Least Squares (ILS) are used to achieve seamless mosaicing. Experimental results demonstrate that, compared with classical RANSAC, the proposed method achieves superior feature sampling accuracy (rotation: 0.879, translation: 0.877) and lower latency. The ILS-based smoothing stage effectively eliminates noise and ghosting without introducing gradient reversal, performing comparably to deep learning methods while significantly outperforming direct averaging and Gaussian approaches. On the NVIDIA Jetson Orin NX edge terminal, a single processing instance requires only 1124 ms, highlighting its strong potential for real-time, low-latency, and autonomous mosaicing tasks. Future research will focus on extending the approach to non-planar terrains and implementing adaptive parameter tuning for the BANSAC algorithm. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

This study proposes a novel intrusion detection and response architecture that formulates network traffic analysis as a sequential decision-making problem rather than a static classification task. The architecture integrates a Transformer Encoder for temporal feature extraction with a Dueling Double Deep Q-Network (DDQN) to enable autonomous and risk-aware security decisions. Network flows are modeled within a Markov Decision Process, where the agent learns an optimal policy over a hierarchical action space consisting of IGNORE, LOG, ESCALATE, and BLOCK actions. To evaluate generalization capability, a transfer learning-based cross-domain adaptation strategy was employed. The CICIDS2018 and CICIoT2023 datasets were re-partitioned using a stratified 70/15/15 train/validation/test split. The proposed model achieved high detection performance on these datasets with F1-scores of 99.48% and 99.13%, respectively. After transfer learning to the AWID3 dataset, the model preserved strong generalization capability with F1-scores of 96.76% and 96.61%, demonstrating its robustness across wired, IoT, and wireless network environments. A risk-aware reward function is designed to balance detection accuracy and operational cost, while Integrated Gradients-based explainability is incorporated to analyze decision behavior. Experimental results further show that the proposed Transformer–DDQN framework achieves more stable learning, lower optimization loss, and more consistent action policies compared to alternative reinforcement learning-based approaches. The model operates with high computational efficiency while maintaining real-time processing capability in high-throughput network environments. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article

The growing sophistication of the internet and web space has spawned highly dynamic, multi-vector cyber threats that cannot be handled by automated detectives and hence the necessity to introduce analyst-oriented, cognitively powerful security analysis apparatus. The character of current visualization-based security frameworks is that they are inclined to deliver data unproactively, fail to engage the dynamic setting, and fail to comprehend the evolving motive of assailants, resulting in subsequent identification and a fractured understanding of coordinated web attacks. The paper introduces a new model of interactive security visualization known as Context-Oriented Visual Exploration of Resilient Threats (COVERT), a hybrid of behavioral context modeling, adaptive visual storytelling, and intent-sensitive interaction. COVERT is dynamically rearranged to the development of threats, patterns of interaction between analysts, and objectives of the possible attacks, which helps in releasing relevant security capabilities gradually. The framework integrates graphical threat flows, attention-directed visual cues, and real-time feedback loops to align system responses to the thinking processes of the analysts. The evaluation of high-scale web traffic and attack simulation dataset indicates that COVERT is much more effective in the multi-stage detection of attacks, false-positive interpretation is minimized, and the investigation period is reduced compared to the visualization infrastructure of the static and semi-interactive infrastructure. According to user studies, there is higher situation awareness, enhanced correlation of distributed events, and enhanced decision-making in complex web intrusion situations, such as advanced persistent threats and web exploitation coordination. Combining contextual intelligence with adaptive interaction and visualization of security, COVERT reveals that intent-based visual analytics may greatly improve internet and web threat detection and analysis systems to support more agile and resilient cyber defense procedures. The proposed COVERT strategy achieved 93% threat-detection rate, the false positives were reduced to 6%, the response time of the analysts was reduced to 140 s, and the situational awareness was increased to 88%. Full article

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

The main aim of the airborne early warning (AEW) system is to search the potential targets in a large surveillance area. The underlying assumption is that the desired target signals only exist in a few range cells for space-time adaptive processing (STAP), i.e., targets (with certain look direction and Doppler) are sparsely distributed in the entire range cells and most of the range cells are target-free. By utilizing the sparsity of the target distribution, we propose a new STAP method by minimizing the l1-norm of the output magnitude. Unlike conventional STAP methods, which exclude the cell under test from the training samples to avoid target self-nulling, our method processes the cell under test (CUT) and the training samples simultaneously without sample selection. Moreover, to achieve robustness against target steering vector mismatch, we constrain the l1-modulus of the response of any steering vector within a rhombus uncertainty set to exceed unity. Additionally, based on a new definition of the l1-norm of a complex-valued vector, the original nonlinear programming problem can be transformed into a linear programming problem. On the other hand, unlike the slide window processor (SWP) whose weights need to be updated for each range cell, the adaptive weight of our method for a block of samples requires no updating. Consequently, the computational complexity of the proposed method is much lower than that of conventional STAP methods. Finally, since the CUT is used to compute the STAP weights, our method can also suppress the discrete interference. The robustness, computational effectiveness and superiority of the proposed STAP method are verified based on simulated data and the MCARM data. Full article

The frequency diverse array (FDA) radar can provide an increased degree of freedom (DOF) in the range domain and offer benefits in range-dependent interference suppression. This paper proposes a vertical frequency diverse array (VFDA)-based approach to suppress deceptive jamming for forward-looking array radar under diving motion. Specifically, the effects of diving motion and frequency increment on the beampattern and clutter spectrum are analyzed. Diving motion introduces azimuth information into the elevation dimension echo, and VFDA offers extra DOF in the elevation dimension to distinguish the false-target jamming from the true target. On this basis, an elevation filter is designed using the linearly constrained minimum variance (LCMV) criterion to mitigate deceptive jamming. Then, the space–time adaptive processing (STAP) processor with clutter compensation is applied to suppress the remaining clutter. In addition, the design criterion for the frequency increment and computational complexity analysis are provided. Numerical simulations verify the effectiveness of the proposed method. The signal model is developed for the diving forward-looking array radar based on the VFDA. The effects of the diving motion and frequency increment on the beampattern and clutter spectrum are analyzed. A novel VFDA STAP approach is proposed to mitigate clutter and deceptive jamming. The design criterion for the frequency increment is provided and the computational complexities are analyzed. MSC: 94A12 Full article