Search Results (131)

The bistatic radar has been widely applied in moving target detection and tracking due to its unique bistatic perspective, low power, and good concealment. With the growing demand for detecting remote and high-speed moving targets, two challenges inevitably arise in the bistatic radar. The first challenge is the range ambiguity and Doppler ambiguity caused by long-range and high-speed targets. The second challenge is the low signal-to-noise ratio (SNR) of the target caused by insufficient echo power. Addressing these challenges is essential for enhancing the performance of the bistatic radar. This paper proposes a robust two-step ambiguity resolution algorithm for detecting and tracking moving targets using a time-division multiple pulse repetition frequency (PRF) multiframe (TD-MPMF) under the bistatic radar. By exploring the coupling relationship between measurement data under different PRFs and frames, the data in a single frame is divided into multiple subframes to formulate a maximization problem, where each subframe corresponds to a specific PRF. Firstly, all possible state values of the measurement data in each subframe are listed based on the maximum unambiguous range and the maximum unambiguous Doppler. Secondly, a coarse threshold is applied based on prior knowledge of potential targets to filter out candidates. Thirdly, the sequence is transformed from the polar coordinate into the feature transform domain. Based on the linear relationship between the range and velocity of multiple PRFs with moving targets in the feature domain, the support vector machine (SVM) is used to classify the target measurements. By employing the SVM to determine the maximum margin hyperplane, the true target range and Doppler are derived, thereby enabling the generation of the target trajectory. Simulation results show better ambiguity resolution performance and more robust qualities than the traditional algorithm. An experiment using a TD-MPMF bistatic radar is conducted, successfully tracking an aircraft target. Full article

In strong clutter and maneuvering scenarios, radar track initiation faces the dual challenges of a low initiation rate and high false alarm rate. Although the existing deep learning methods show promise, the commonly adopted “feature flattening” input strategy destroys the intrinsic temporal structure and feature relationships of track data, limiting its discriminative performance. To address this issue, this paper proposes a novel radar track initiation method based on Dual-Attention Temporal Convolutional Network (DA-TCN), reformulating track initiation as a binary classification task for very short multi-channel time series that preserve complete temporal structure. The DA-TCN model employs the TCN as its backbone network to extract local dynamic features and innovatively constructs a dual-attention architecture: a channel attention branch dynamically calibrates the importance of each kinematic feature, while a temporal attention branch integrates Bi-GRU and self-attention mechanisms to capture the dependencies at critical time steps. Ultimately, a learnable gated fusion mechanism adaptively weights the dual-branch information for optimal characterization of track characteristics. Experimental results on maneuvering target datasets demonstrate that the proposed method significantly outperforms multiple baseline models across varying clutter densities: Under the highest clutter density, DA-TCN achieves 95.12% true track initiation rate (+1.6% over best baseline) with 9.65% false alarm rate (3.63% reduction), validating its effectiveness for high-precision and highly robust track initiation in complex environments. Full article

Hypersonic glide targets (HGTs) pose significant challenges for radar tracking due to complex maneuver strategies and time-varying statistics of measurement noise. Conventional single-model tracking methods are generally insufficient to fully capture maneuver modes, while existing multiple-model methods face trade-offs between model set completeness and computational efficiency. In addition, existing tracking methods struggle to cope with the non-Gaussian noise during hypersonic flight. To overcome these limitations, a Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed in this paper. Firstly, a comprehensive model set is constructed to cover characteristic maneuver modes. Subsequently, a hierarchical multiple-model framework is developed where: (1) a coarse model set is dynamically adapted by multi-frame posterior probability evolution and Rényi divergence criteria; (2) a fine model set is generated based on the moment matching method. Furthermore, the minimum error entropy cubature Kalman filter (MEECKF) is proposed to suppress the non-Gaussian measurement noise with high stability. Monte Carlo simulations demonstrate that the proposed method achieves improved positioning accuracy and faster convergence. Full article

With the advancement of electromagnetic interference and counter-interference technology, complex and unpredictable interference signals greatly reduce radar detection, tracking, and recognition performance. In multi-interference environments, the overlap of interference cross-correlation peaks can mask target signals, weakening radar interference suppression capability. To address this, we propose a joint waveform and filter design method called Multi-Interference Suppression Network (MISNet) for effective interference suppression. First, we develop a design criterion based on suppression coefficients for different interferences, minimizing both cross-correlation energy and interference peak models. Then, for the non-smooth, non-convex optimization problem, we use complex neural networks and gating mechanisms, transforming it into a differentiable problem via end-to-end training to optimize the transmit waveform and receive filter efficiently. Simulation results show that compared to traditional algorithms, MISNet effectively reduces interference cross-correlation peaks and autocorrelation sidelobes in single interference environments; it demonstrates excellent robustness in multi-interference environments, significantly outperforming CNN, PSO, and ANN comparison methods, effectively improving radar interference suppression performance in complex multi-interference scenarios. Full article

To address the challenges of radar multi-target detection in marine environments, this paper proposes an information geometry (IG)-based, two-stage track-before-detect (TBD) framework. Specifically, multi-target measurements are first modeled on the manifold, leveraging its geometric properties for enhanced detection. The designed scoring function incorporates both the feature dissimilarity between targets and clutter, as well as the precise inter-target path associations. Consequently, a novel merit function combining feature dissimilarity and transition cost is derived to mitigate the mutual interference between adjacent targets. Subsequently, to overcome the integrated merit function expansion phenomenon, a two-stage integration strategy combining dynamic programming (DP) and greedy integration (GI) algorithms was adopted. To tackle the challenges of unknown target numbers and computationally infeasible multi-hypothesis testing, a target cancellation detection scheme is proposed. Furthermore, by exploiting the independence of multi-target motions, an efficient implementation method for the detector is developed. Experimental results demonstrate that the proposed algorithm inherits the superior clutter discrimination capability of IG detectors in sea clutter environments while effectively resolving track mismatches between neighboring targets. Finally, the effectiveness of the proposed method was validated using real-recorded sea clutter data, showing significant improvements over conventional approaches, and the signal-to-clutter ratio was improved by at least 2 dB. Full article

Target tracking in typical radar applications faces critical challenges in complex environments, including nonlinear dynamics, non-Gaussian noise, and sensor outliers. Current robustness-enhanced approaches remain constrained by empirical kernel tuning and computational trade-offs, failing to achieve balanced noise suppression and real-time efficiency. To address these limitations, this paper proposes the variational Bayesian-based maximum correntropy criterion cubature Kalman filter (VBMCC-CKF), which integrates variational Bayesian inference with CKF to establish a fully adaptive robust filtering framework for nonlinear systems. The core innovation lies in constructing a joint estimation framework of state and kernel size, where the kernel size is modeled as an inverse-gamma distributed random variable. Leveraging the conjugate properties of Gaussian-inverse gamma distributions, the method synchronously optimizes the state posterior distribution and kernel size parameters via variational Bayesian inference, eliminating reliance on manual empirical adjustments inherent to conventional correntropy-based filters. Simulation confirms the robust performance of the VBMCC-CKF framework in both single and multi-target tracking under non-Gaussian noise conditions. For the single-target case, it achieves a reduction in trajectory average root mean square error (Avg-RMSE) by at least 14.33% compared to benchmark methods while maintaining real-time computational efficiency. Integrated with multi-Bernoulli filtering, the method achieves a 40% lower optimal subpattern assignment (OSPA) distance even under 10-fold covariance mutations, accompanied by superior hit rates (HRs) and minimal trajectory position RMSEs in cluttered environments. These results substantiate its precision and adaptability for dynamic tracking scenarios. Full article

In passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets. Nevertheless, non-uniform clutter in practical scenarios causes misestimation of component weights, thereby generating false targets. To solve the false targets problem, a feature-matching MM-PHD (FM-MM-GM-PHD) algorithm for passive radar tracking is proposed in this paper. First, the measurement likelihood function was refined by leveraging target radar cross-section (RCS) and Doppler features to assist in suppressing false targets and reduce clutter interference. Additionally, the proposed algorithm incorporated adaptive component pruning and absorption processes to enhance tracking accuracy. Finally, a missed-alarm correction mechanism was introduced to compensate for measurement losses. Simulations of the passive radar results validated the findings that the proposed algorithm outperformed the traditional MM-PHD filter in both tracking accuracy and cardinality estimation. This superiority was particularly pronounced in non-uniform clutter environments under low detection probabilities. Full article

This study develops and operationalizes a multi-dimensional framework for sustainable building retrofit that aligns with national 2050 net-zero objectives. First, we conduct a scoping review of international standards (e.g., ISO), sustainability reporting guidelines (GRI G4), and peer-reviewed studies to define an indicator system spanning three pillars—environmental (carbon neutrality, resource circulation, pollution management), social (habitability, durability/safety, regional impact), and economic (direct support, deregulation). Building on this structure, we propose a transparent 0–3 rubric at the sub-indicator level and introduce the Sustainable Building Retrofit Index (SRI) to enable cross-case comparability and over-time monitoring. We then apply the framework to seven countries (United States, Canada, United Kingdom, France, Germany, Japan, and South Korea), score their retrofit systems/policies, and synthesize results through radar plots and a composite SRI. The analysis shows broad emphasis on carbon neutrality and habitability but persistent gaps in resource circulation, pollution management, regional impacts, and deregulatory mechanisms. For South Korea, policies remain energy-centric, with relatively limited treatment of resource/pollution issues and place-based social outcomes; economic instruments predominantly favor direct financial support. To address these gaps, we propose (i) life-cycle assessment (LCA)–based reporting that covers greenhouse gas and six additional impact categories for retrofit projects; (ii) a support program requiring community and ecosystem-impact reporting with performance-linked incentives; and (iii) targeted deregulation to reduce uptake barriers. The paper’s novelty lies in translating diffuse sustainability principles into a replicable, quantitative index (SRI) that supports benchmarking, policy revision, and longitudinal tracking across jurisdictions. The framework offers actionable guidance for policymakers and a foundation for future extensions (e.g., additional countries, legal/municipal instruments, refined weights). Full article

With the development of multi-sensor collaborative detection technology, radar track fusion has become a key means to improve target tracking accuracy. Traditional fusion methods based on Kalman filtering and weighted averaging have the problem of insufficient adaptability in complex environments. This paper proposes an end-to-end deep learning track fusion method, which achieves high-precision track reconstruction through residual extraction and parallel network fusion, providing a new end-to-end method for track fusion. The method combines the attention mechanism and the long short-term memory network in parallel and optimizes the computational complexity. Through the uncertainty weighting mechanism, the fusion weight is dynamically adjusted according to the reliability of the track features. Experimental results show that the mean absolute error of fusion accuracy of this method is 79% lower than the Kalman filter algorithm and about 87% lower than the mainstream deep learning model, providing an effective way for multi-radar track fusion in complex scenarios. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GERT) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions. Full article

This paper presents a compact 24 GHz radar with a 4-transmit (4Tx) and 4-receive (4Rx) CMOS radar IC, integrated with a 4 × 4 Tx array and four 1 × 4 receive Rx array antennas, optimized for enhancing small drone detection. By employing the hybrid beamforming technique based on analog beamforming on the transmit side and independent four-channel digital reception, the proposed radar achieves high spatial resolution and robust target tracking. The proposed radar features an elevation scan range of ±45° with an azimuth fan-beam half-power beamwidth (HPBW) of 80° for a comprehensive detection field. Tests with a small drone measuring 20.3 × 15.9 × 7 cm³, positioned at various elevation angles of up to 45° and azimuth angles of up to ±60° at a distance of 4 m from the radar, verified its detection capability and highlighted the radar’s effectiveness in tracking small aerial targets. This architecture emphasizes the advantages of analog beamforming on Tx and multi-channel Rx, addressing the increasing demands for precise drone detection and monitoring in both civilian and defense domains. Full article

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches. Full article

Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing. Full article