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In recent years, maritime radar networks have become essential for ensuring the safety and security of maritime operations. However, with the increased interconnectivity of these systems, they have also become vulnerable to cyber-attacks, posing significant risks to critical infrastructure. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving attacks in real-time due to their reliance on manual feature extraction and shallow machine learning techniques. This research addresses this gap by introducing MARINERNet, a deep learning-based intrusion detection system designed specifically for maritime radar networks. The proposed system uses a novel architecture that integrates 1D convolutional layers, squeeze-and-excitation blocks, and residual connections to automatically extract relevant features from raw radar network data, enhancing detection accuracy without manual intervention. MARINERNet is evaluated on both binary and multiclass classification tasks, demonstrating state-of-the-art performance. Specifically, the model achieves 98.52% accuracy for multiclass classification and perfect accuracy for anomaly detection (binary classification). The approach is scalable, capable of handling large datasets, and adaptable to real-time intrusion detection, making it suitable for deployment in dynamic radar environments. This research not only provides an effective solution for detecting intrusions in maritime radar networks but also contributes to the broader field of cybersecurity by offering a robust, deep learning-based approach that can be applied to other network systems.

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.

Dual-band weather radar networks enable complementary multi-radar observations, improving the accuracy, three-dimensional characterization, and early warning capability for severe convective weather. S-band radar provides strong penetration and long detection range but suffers from limited spatial resolution, whereas X-band radar offers high resolution with weaker penetration, posing challenges for dual-frequency data fusion. To address the resolution mismatch and fusion modeling issues between dual-band radars, this study proposes a super-resolution reconstruction method for S-band reflectivity based on dual-frequency radar observations. S-band and X-band radar data, together with key polarimetric parameters, are jointly incorporated into a deep neural network-based fusion model to enhance the spatial resolution of S-band reflectivity. Experimental results under typical severe weather conditions demonstrate that the proposed method achieves improved detail recovery and structural reconstruction, with the model achieving PSNR 30.84, SSIM 0.8755, and MAE 0.24178, which shows obvious advantages compared with other models and effectively enhances radar network data quality, and it outperforms single S-band super-resolution approaches in both objective metrics and subjective evaluations.

Distributed radar networks have emerged as a key technology in remote sensing and surveillance due to their high transmission power and robustness against node failures. When performing coherent beamforming with multiple radars, frequency and phase deviations introduced by independent oscillators lead to a decrease in transmission power. This paper proposes an optimized diffuse Kalman filter (ODKF) for the frequency and phase synchronization. Specifically, each radar locally estimates its frequency and phase, then shares this information with neighboring nodes, which are used for incremental update and diffusion update to adjust local estimates. To further reduce synchronization errors, we incorporate a self-feedback strategy in the diffusion step, in which each node balances its own estimate with neighbor information by optimizing the diagonal weights in the diffusion matrix. Numerical simulations demonstrate the superior performance of the proposed method in terms of mean squared deviation (MSD) and convergence speed.

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) facility has been at the forefront of millimeter-wavelength radar development and operations since the late 1990s. The operational performance of the ARM cloud radar network is very high; however, the calibration of the historical record is not well established. Here, a well-characterized spaceborne 94 GHz cloud profiling radar (CloudSat) is used to characterize the calibration of the ARM cloud radars. The calibration extends from 2007 to 2017 and includes both fixed and mobile deployments. Collectively, over 43 years of ARM profiling cloud radar observations are compared to CloudSat and the calibration offsets are reported as a function of time using a sliding window of 6 months. The study also provides the calibration offsets for each operating mode of the ARM cloud radars. Overall, significant calibration offsets are found that exceed the uncertainty of the technique (1–2 dB). The findings of this study are critical to past, ongoing, and planned studies of cloud and precipitation and should assist the DOE ARM to build a legacy decadal ground-based cloud radar dataset for global climate model validation.

In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is rendered ineffective due to occurrences of frequency spectrum interference and main-lobe deceptive interference with arbitrary time delays. Therefore, a cognitive FDA-MIMO radar network (CFDA-MIMORN) transmit element selection algorithm is introduced. At first, the target is discriminated from the false targets. The Kalman filter is used to track the target, then available information is used to infer the target’s position in the next time step. The finite transmit elements of the radar network are organized to enhance tracking performance, especially in the presence of frequency spectrum interferences. The numerical simulations demonstrate that the proposed CFDA-MIMORN can effectively discriminate the true target from false targets, and optimize the allocation of transmit elements to avoid interferences, resulting in improved tracking accuracy.

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.

Weather radars are essential in the Quantitative Precipitation Estimates (QPE) but are susceptible to calibration errors. Previous work demonstrated that observations from the Ku-band Dual Polarization Radar (DPR) radar on board the Global Precipitation Measurement Mission Dual-Precipitation Radar (GPM) are suitable for ground radar calibration. Several studies volume-matched ground radar and GPM DPR Ku-band reflectivities for the absolute calibration of ground radars, by applying different constraints and filters in the volume-matching procedure. This study compares and evaluates volume-matching thresholds and data filtering schemes for the Rizoelia, Larnaca (LCA) and Nata, Pafos (PFO) radars of the Cyprus weather radar network from October 2017 till May 2023. Excluding reflectivities below and within the melting layer with a 250 m buffer yielded consistent results for both ground radars. The selected calibration schemes were combined, and the resulting offsets were compared to stable radar parameters to identify stable calibration periods. The consistency of the wet hydrological year October 2019 to September 2020 suggests that radar calibration results are prone to differences in meteorological conditions, as scarce rainfall can result in insufficient data for reliable calibration. Future work will incorporate disdrometer measurements and extend the analysis to quantitative precipitation estimation.

Led by the National Space Science Center of the Chinese Academy of Sciences, we have built a Chinese dual auroral radar network in northern China, which is called the CN‐DARN. The CN‐DARN consists of three pairs of high‐frequency coherent scattering radar facilities and is one of the key parts of the Chinese Meridian Project Phase II. It has been fully constructed and started trial operations at the end of 2023. The detection range of the radar network extends longitudinally over approximately 9 hr of local times and covers the middle to high latitudes of the entire Asia region above 40°^{\\circ}$ . In this paper, we present the basic design of the CN‐DARN and its preliminary observations of ionospheric irregularities, subauroral polarization streams (SAPSs) and traveling ionospheric disturbances (TIDs). We also investigate its contribution to the ionospheric convection pattern of the Northern Hemisphere derived from Super Dual Auroral Radar Network (SuperDARN) observations. The results indicate that the CN‐DARN provides excellent measurements and better specifications of flows in the Asian sector, improving our understanding of the global‐scale ionospheric convection pattern in the Northern Hemisphere. These encouraging results lead us to believe that the CN‐DARN will play an important role in studies on the evolution of ionospheric irregularities, the characteristics and evolution of SAPSs, the propagation of TIDs, and global‐scale ionospheric convection dynamics.

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.