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Pareto distribution has been introduced into the radar community recently as a suitable model for X-band high resolution maritime sea clutter returns. This intensity clutter model is a simple two parameter power law distribution. It is thus important to consider the development of constant false alarm rate detection processes for targets embedded within such clutter. It is shown that a simple functional transform can produce such detection schemes, whose false alarm probability and threshold are related through simple analytical expressions. These relationships are related intrinsically to Gaussian detection counterparts. Three detectors will be introduced and their performance analysed in both homogeneous and heterogeneous clutter environments. The effect of interfering targets in the training cells will also be examined.

This study examines ground and sea clutter from three different FM-based passive bistatic radars (PBRs). Measurement data has been obtained for low-grazing angles. The PBRs used to collect the displayed data, with all their relevant parameters, are defined and clutter analysis procedures are described. This work, based on measured data, yields valuable insight into the nature of VHF bistatic clutter.

A training sample selection method is proposed by fusing the generalized inner product (GIP) statistic with geographic information to construct the fused metric of the sample set. Based on this metric, more suitable training samples are selected. The experimental results demonstrate that the proposed method exhibits excellent robustness in different clutter environments, particularly in complex ground environments containing discrete moving scatterers, where its clutter suppression performance is superior to that of both GIP and knowledge‐aided space‐time adaptive processing (KA‐STAP) methods. The method expands sample‐screening dimensions through multidimensional information‐weighted fusion, enabling the selection of more suitable sample sets. Compared with the GIP and knowledge‐aided clutter suppression methods, our proposed method exhibits excellent robustness in different clutter environments and achieves improved clutter‐suppression performance in complex ground environments containing discrete moving scatterers.

Accurate identification of insulator faults is a critical guarantee for the secure operation of modern power transmission systems, especially amid the escalating demand for automated and real-time inspection. Deep learning-based detection methods have markedly boosted inspection efficiency, yet striking a practical balance between detection precision and computational efficiency remains a tough challenge in intricate outdoor environments. To address this issue, this study proposes an enhanced insulator fault detection framework built upon YOLOv8. A lightweight MobileNetV3 backbone is adopted to reduce computational burden while preserving essential feature extraction capability. In addition, a novel XXC2f module is introduced to jointly exploit spatial-domain structures and frequency-domain characteristics, thereby improving robustness to background clutter and scale variation. Furthermore, the SIoU loss is employed to guide bounding box regression with improved stability for slender and irregular targets. Experimental results demonstrate that the proposed approach achieves superior detection accuracy with reduced model complexity, making it suitable for real-time insulator inspection applications.

Range sidelobe modulation (RSM) limits the performance of pulse‐agile radars. This letter proposes an expectation–maximisation (EM)‐based range–Doppler (RD) estimator that learns the clutter and target distributions and adjusts a range–Doppler prior map. By optimising the shrinkage strength via prior weighting, the proposed method improves RSM suppression in the region of interest. Simulation results validate its effectiveness.

The Earth's surface radar reflection is one of the most important signals received by spaceborne radar systems. It is used in several scientific applications, including geolocation, terrain classification, and path-integrated attenuation estimation. A simulator based on the ray-tracing approach has been developed to reproduce the clutter reflectivity and the Doppler velocity signal for a conically scanning spaceborne Doppler radar system. The simulator exploits topographic information through a raster digital elevation model, land types from a regional classification database, and a normalized radar surface cross-section look-up table. The simulator is applied to the WInd VElocity Radar Nephoscop (WIVERN) mission, which proposes a conically scanning W-band Doppler radar to study in-cloud winds. Using an orbital model, detailed simulations for conical scans over the Piedmont region of Italy, which offers a variety of landscape conditions, are presented. The results highlight the strong departure of the reflectivity and Doppler velocity profiles in the presence of marked orography and the significant gradient in the surface radar backscattering properties. The simulations demonstrate the limitations and advantages of using the surface Doppler velocity over land as an antenna-pointing characterization technique. They represent the full strength range of the surface radar clutter over land surfaces for the WIVERN radar. The surface clutter tool applies to other spaceborne radar missions, such as the nadir-pointing EarthCARE and CloudSat Cloud Profiling Radar (CPR), or the cross-track scanning Global Precipitation Measurement (GPM) precipitation radars.

The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014.

The spaceborne-airborne bistatic radar (SABR) system employs a spaceborne transmitter and an airborne receiver, offering significant advantages, such as wide coverage, outstanding anti-stealth capabilities, and notable resistance to jamming. However, SABR operates in a downward-looking configuration, and due to the separation of the transmitter and receiver, non-side-looking array reception, and the effects of Earth’s rotation, clutter exhibits both spatial-temporal coupling and distance dependence. These factors cause substantial expansion in spatial and temporal frequency domains, leading to severe degradation in radar detection performance for moving targets. This paper establishes an SABR clutter signal model that applies to arbitrary geometric configurations to respond to these challenges. The paper uses this model to analyze the non-side-looking clutter characteristics in a geostationary spaceborne-airborne bistatic radar configuration. Furthermore, the paper investigates the impact of various observation areas and geometric configurations on detection performance, using SCNR loss as the performance index. Finally, this paper gives suggestions on the transceiver’s geometric configuration and the observation area selection.

A synergistic analysis of the radar‐only and combined radar‐lidar observations across the three platforms was conducted. To align with well‐calibrated CloudSat cloud profiling radar (CPR) (and HCR) reflectivity measurements, a constant 4.5 dB offset was applied to all M‐WACR reflectivitives during the MARCUS. This brings M‐WACR data into better agreement with both HCR and CPR reflectivity measurements and facilitates a more reliable cloud fraction (CF) comparison. The total CFs (CFTs) derived from the three radars show excellent agreement. All three radars detect large drizzle drops, but M‐WACR and HCR excel at detecting smaller cloud droplets that are often missed by CPR. The underestimated CFs by CPR are due to increased attenuation of CPR measurements below 3 km, and the combined effects of attenuation and surface clutter below 1 km. Combining radar and lidar observations enhanced cloud detection by 20%–60%. The results from this study provide new insights for designing future cloud radar systems. Plain Language Summary Clouds play a crucial role in the radiation budget, as they impact how much radiation enters and leaves the Earth system. To understand how clouds affect the radiation budget, scientists use various instruments to observe them. In this study, we compare the total cloud fractions (CFTs) and the vertical distributions of clouds over the Southern Ocean using data collected from different platforms: a ship‐based radar‐lidar system (MARCUS), an airplane‐based radar‐lidar system (SOCRATES), and a satellite‐based CPR (CloudSat) and lidar (CALIPSO). Each platform employs similar radar and combined radar‐lidar systems that observe clouds from different perspectives and provide estimates of cloud coverage or the fraction of sky covered by clouds. Cloud fraction values derived from each platform depend on the sensitivity, calibration, and direction of observation of the platform sensors. The goal of this study is to facilitate best use of existing radar reflectivity measurements from the three platforms. Key Points A constant 4.5 dB offset was applied to all M‐WACR reflectivities during the ship‐based MARCUS using collocated cloud profiling radar (CPR) measurements The total cloud fractions (CFTs) derived from CPR (CloudSat), HCR (airborne‐based SOCRATES), and corrected M‐WACR show excellent agreement CloudSat CPR captured most large drizzle drops but missed a significant portion of small cloud droplets and underestimated CFs below 3 km

In ground‐based passive radar, the surveillance channel is mainly affected by zero‐Doppler clutter, while Doppler‐spread clutter caused by vegetation motion or structural vibration is also unavoidable in practice. Although the Doppler‐extended extensive cancellation algorithm (ECA) can effectively suppress both types of clutter, it incurs high computational and memory costs due to high‐dimensional subspace construction and associated computations, which may hinder real‐time target detection in practical applications. To address this issue, this paper proposes an ultra‐low‐complexity algorithm, termed ECA‐U, which avoids explicit subspace formation through circular shifts and frequency‐domain reuse of diagonal block structures. Simulation results demonstrate suppression performance comparable to Doppler‐extended ECA while achieving improvements of over an order of magnitude in runtime and storage efficiency.