This data set consists of data files and a report that gives a summary of the Yale Vertically Pointing K-Band Radar data. The non-standard data format requires a file Grafik.exe, which is included in the data set, to read the data files. Ron Smith, the contact for this data set, can provide advice about analysis methods upon request. The Yale Radar was installed before 10 March 2006 in the western foothills of the Sierra Nevada mountain range at the Foothills Visitor Center and Park Headquarters of the Sequoia National Park.

Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets.

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This dataset contains post-processed data from a METEK first generation vertically profiling K-band Micro Rain Radar (MRR) deployed at C-band Doppler radar site (COW site) in support of the WINTRE-MIX field campaign. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. Several other sites also collected MRR data during WINTRE-MIX.

The NASA Jet Propulsion Laboratory (JPL) Glacier and Ice Surface Topography Interferometer (GLISTIN-A) sensor was flown over the SnowEx study sites in Grand Mesa and Senator Beck Basin (labeled Telluride), Colorado in September 2016 (baseline, snow-off conditions) and in February 2017. The instrument was mounted in the wing pod of a Gulfstream-III jet. Ka-Band data from the GLISTIN-A sensor were collected along 14 flight lines flown between 27 September and 28 September 2016 and 63 flight lines flown between 09 February and 25 February 2017. All SnowEx GLISTIN-A data are stored and available for download from the JPL UAVSAR Data Search page. Find the SnowEx 2017 data on the JPL website by selecting “Ka-band” from the list of Band options and typing “Telluride, CO” or “Grand Mesa, CO” in the search box under Find.

The global 3D and 4D military radar market is experiencing robust growth, driven by escalating geopolitical tensions, modernization of defense forces worldwide, and the increasing demand for advanced surveillance and targeting systems. The market, currently valued at approximately $8 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of around 7% from 2025 to 2033, reaching an estimated value of $14 billion by 2033. This growth is fueled by several key factors. Technological advancements, particularly in areas like AESA (Active Electronically Scanned Array) technology and improved signal processing capabilities, are enhancing the performance and capabilities of these radars. Furthermore, the growing need for enhanced situational awareness, improved target identification and tracking, and counter-drone capabilities is driving the adoption of these sophisticated radar systems across various military applications. The segmentation of the market highlights a strong demand across different branches of the military – army, navy, and air force – with each branch presenting unique requirements influencing specific radar frequency band preferences (UHF, VHF, S-band, L-band, X-band, C-band, K-, Ku-, and Ka-bands). North America and Europe currently hold significant market shares due to the presence of major radar manufacturers and robust defense budgets in these regions; however, Asia-Pacific is expected to exhibit substantial growth in the coming years driven by increasing military spending in countries like China and India. Market restraints include high initial investment costs associated with advanced radar systems, the complexity of integration with existing defense infrastructure, and the potential for technological obsolescence. Despite these challenges, the strategic importance of 3D and 4D military radars in modern warfare ensures continued investment and innovation within this sector. The competitive landscape is characterized by a handful of established players, including Thales Group, Northrop Grumman, Lockheed Martin, Saab, and Raytheon Technologies, alongside several regional players, engaged in a dynamic market constantly evolving to meet the demands of evolving military needs. The focus on developing cost-effective, reliable, and easily deployable systems will continue to shape future market trends.

The raw dataset is simulated by 24Ghz FMCW radar, containing 11 daily human activities. Standing in a fixed position while rotating his body (B); kicking (K), punching (P), grabbing an object (G), walking back and forth in front of the radar (W), standing up from chair (SU), sitting down on chair (SD), stands up from chair to walk (STW), walks to sit on chair (WTS), walks to fall on the ground (WTF), standing up from ground to walk (FTW).

The Airborne Fire Control Radar market, valued at $2565.2 million in 2025, is projected to experience steady growth, driven by increasing defense budgets globally and the ongoing modernization of military aircraft fleets. The market's 2.9% CAGR indicates a consistent, albeit moderate, expansion over the forecast period (2025-2033). Key drivers include the demand for enhanced situational awareness and precision targeting capabilities in both military and commercial aviation sectors. Technological advancements, such as the integration of advanced signal processing techniques and the development of more compact and energy-efficient radar systems, are shaping market trends. However, the market faces certain restraints, including the high cost of development and deployment of these sophisticated systems and the increasing competition from other surveillance technologies. The segmentation reveals a strong presence of military applications, particularly within the S-band, X-band, Ku/K/Ka band radar systems. Leading companies such as Lockheed Martin, Northrop Grumman, and Raytheon are major players, leveraging their technological expertise and established customer relationships to maintain market dominance. Geographic distribution shows significant presence across North America and Europe, with emerging markets in Asia-Pacific demonstrating growing potential. The market's growth will be influenced by several factors. The increasing integration of airborne fire control radars with other advanced sensors and weapon systems will fuel demand. Furthermore, the rising adoption of these systems in unmanned aerial vehicles (UAVs) and other unmanned platforms is anticipated to contribute to market expansion. Conversely, budgetary constraints in certain regions and technological disruptions may pose challenges. The ongoing geopolitical uncertainties and regional conflicts contribute to the continued need for advanced fire control capabilities, sustaining market growth throughout the forecast period. The commercial sector is anticipated to contribute gradually, driven by increasing demand for enhanced safety and collision avoidance systems in air traffic management.

This archive contains data from the Rapid Scanning X-Band Polarimetric (RaXPol) radar, Atmospheric Imaging Radar (AIR), and Shared Mobile Atmospheric Research and Teaching radar (SMART-R) for 4 September 2018 in central Oklahoma. This is a rapid-scan dual-Doppler dataset of a convective storm. RaXPol and AIR were the two radars that can be used for dual-Doppler retrievals and the SMART-R data can be used for verification of vertical velocity.

The AIR and RaXPol data have been quality controlled and are provided in cfRadial format. The SMART-R data has not been quality controlled and are available in its raw data format. All data can be read using the Python ARM Radar Toolkit.

This dataset was used for the manuscript:

Gebauer, J. G., A. Shapiro, C. K. Potvin, N. A. Dahl, M. I. Biggerstaff, and A. A. Alford, 2021: Evaluating vertical velocity retrievals from vertical vorticity equation constrained dual-Doppler analysis of rapid-scan radar data. J. Atmos. Meas. Tech., in review.

The global radar detector market is experiencing steady growth, driven by increasing vehicle ownership, stricter traffic enforcement, and advancements in radar detection technology. While precise market size figures aren't provided, considering the presence of major players like Cobra Electronics, Escort, and Uniden, and a reasonably competitive landscape, a 2025 market size of approximately $500 million seems plausible. This is based on an assumed historical growth trajectory and extrapolation, accounting for factors like economic fluctuations and technological innovation. The market's Compound Annual Growth Rate (CAGR) is expected to remain positive, with a projected rate of around 5-7% over the forecast period (2025-2033). This sustained growth can be attributed to several factors, including rising demand for advanced features like GPS integration, real-time alerts, and improved signal processing capabilities. Furthermore, the increasing adoption of driver assistance systems, while potentially impacting the market in the long term, is currently fostering consumer awareness about speed monitoring technologies, thereby indirectly boosting demand for radar detectors. However, the market faces certain restraints. Stringent regulations concerning the legality of radar detectors in various regions pose a significant challenge. Furthermore, the increasing prevalence of advanced traffic enforcement technologies such as lidar and mobile speed cameras, which are less susceptible to detection by traditional radar detectors, represent a technological disruption. The market segmentation reflects this complexity, with distinct categories based on technology (e.g., K-band, Ka-band, X-band), features (GPS, laser detection), and price points. Key regional markets include North America, Europe, and Asia-Pacific, with varying degrees of market penetration based on legal frameworks and consumer preferences. The competitive landscape is characterized by established brands focused on technological innovation and new entrants aiming to capture market share through cost-effective solutions. The forecast period of 2025-2033 presents opportunities for players who can adapt to evolving regulations and consumer demands while introducing innovative and cost-effective products.

This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project.The basic task is to bring together retrievals of water vapor and cloud properties from multiple "A-train" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each "scene" (instrument look) using the cloud information,and develop a merged, multi-sensor climatology of atmospheric water vapor as afunction of altitude, stratified by the cloud classes. This is a large scienceanalysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, findspace/time "matchups" between pairs of instruments, and process years ofsatellite data to produce the climate data records.The short name for this collection is AIRSM_CPR_MATParameters contained in the data files include the following:Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m*2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K)End of parameter information

RadarPWN is a comprehensive, easy-to-use, and documented dataset, including cyberattacks against radar communication in modern Integrated Bridge Systems. RadarPWN was recorded using a simulation environment consisting of the BridgeCommand ship simulator, our Radar Attack Tool and the OpenCPN chart plotter. The resulting data consists of Navico BR24 and NMEA 0183 network traffic in benign and "attack" scenarios.

The dataset is available in two forms:

• radarpwn-x-y-z contains all network captures as pcaps as well as configuration files used for the simulation runs and logs
• radarpwn-x-y-z-screen-captures contains all of the above in addition to screen recording videos of all of the simulation runs showing the chart plotter, the radar screen and the logs of the Radar Attack Tool, which can be helpful to visualize the attacks contained in the network captures.

Details on the structure and content of the dataset are provided in the README.md file (or README.html). Working with the provided packet captures can be facilitated by using our Wireshark dissector.

For more information, please see K. Wolsing et al., "Network Attacks Against Marine Radar Systems: A Taxonomy, Simulation Environment, and Dataset," 2022 IEEE 47th Conference on Local Computer Networks (LCN), 2022, pp. 114-122, DOI: 10.1109/LCN53696.2022.9843801.

Changelog:

• 1.0.2: Updated README files to include link to the radarsec-lab simulation environment.

• 1.0.1: Removed Docker-compose logs, renamed scenario list files to ensure the dataset archive is the default preview file on Zenodo.

The global velocity speed radar gun market is experiencing robust growth, driven by increasing demand from law enforcement agencies, sports training facilities, and speed monitoring applications across various sectors. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 million by 2033. This growth is fueled by technological advancements leading to more accurate, portable, and affordable radar guns, as well as stricter speed limit enforcement globally. Furthermore, the rising adoption of radar guns in sports analytics for performance enhancement contributes significantly to market expansion. Key restraints include stringent regulatory approvals for new technologies and the potential for technological obsolescence. The market is segmented by type (handheld, stationary), application (law enforcement, sports, traffic management), and technology (Doppler, K-band, Ka-band). Major players like RCSpeeds, Stalker Radar, and Escort Ltd. are actively innovating and expanding their product portfolios to capture a larger market share. Geographic expansion, especially in developing economies with growing infrastructure and vehicle ownership, is also a key factor driving market growth. The competitive landscape is characterized by a mix of established players and emerging companies. Existing players are focusing on product diversification, technological upgrades, and strategic partnerships to maintain their market positions. The rising adoption of advanced features such as GPS integration, video recording capabilities, and data analytics is shaping market dynamics. The handheld segment currently dominates the market due to its portability and ease of use, while the demand for stationary radar guns is also growing steadily in traffic management applications. North America and Europe currently hold the largest market share due to high adoption rates and stringent safety regulations, but the Asia-Pacific region is poised for significant growth in the coming years due to rapid economic development and urbanization. The forecast period (2025-2033) is expected to witness significant market expansion driven by the factors mentioned above.

The GPM Ground Validation Micro Rain Radar (MRR) OLYMPEX dataset was gathered during the Global Precipitation Measurement (GPM) Ground Validation OLYMPEX field campaign held at Washington’s Olympic Peninsula from October 31, 2014 through May 22, 2016. The dataset contains measured and derived data from MRR instruments placed in four separate locations within the study region. The MRR is a Biral/Metek 24 GHz (K-band) vertically oriented Frequency Modulated Continuous Wave (FM-CW) radar that measures signal backscatter from which Doppler spectra, radar reflectivity, Doppler velocity, drop size distribution, rain rate, liquid water content, and path integrated attenuation are derived. Data files are available in ASCII data format.

Last update: February 2021.The dataset folder includes both raw and post-processed radar data used for training and testing the networks proposed in Sect. VIII of the article “Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview”.The folder Human Activity Classification contains“Raw” folder where 150 files acquired with our FMCW radar sensor are given inside the “doppler_dataset” zip folder; they are divided in 50 for walking, 50 for jumping and 50 for running;“Post_process” divided in- “Machine Learning” folder including “dataset_ML_doppler_real_activities.mat”; this dataset has been used for training and testing the SVM, K-NN and Adaboost described in Sect. VIII-A).- The 150x4 matrix “X_meas” including the features described by eqs. (227)-(234) and the 150x1 vector of char “labels_py” containing the associated labels.- “Deep Learning” folder containing “dataset_DL_doppler_real_activities.mat”; this dataset is composed by 150 structs of data, where each of them, associated to a specific activity, includes:- The label associated to the considered activity,- The overall range variations from the beginning to the end of the motion “delta_R”;- The Range-Doppler map “RD_map”;- The normalized spectrogram “SP_Norm”;- The Cadence Velocity Diagram “CVD”;- The period of the spectrogram “per”;- The peaks associated to the greatest three cadence frequencies in “peaks_cad”;- The three strongest cadence frequencies and their normalized version in “cad_freqs” and “cad_freqs_norm”;- The strongest cadence frequency “c1”;- The three velocity profiles associated to the three strongest cadence frequencies “matr_vex”.The spectrogram images (SP_Norm) contained in this dataset were used for training and testing the CNN in Sect. VIII-A).The folder Obstacle Detection contains“Raw” folder where raw data acquired with our radar system and TOF camera in the presence of a multi target or single target scenario are given inside the “obst_detect_Raw_mat” zip folder. It’s important to note that each radar frame and each TOF camera image have their own time stamp, but since they come from different sensor have to be synchronized.“Post_process” divided in- “Neural Net” folder containing “inputs_bis_1.mat” and “t_d_1.mat”, where- “inputs_bis_1.mat” contains the vectors of features of size 32x1, used for training and testing the feed-forward neural network described in Sect. VIII-B) (see eqs. (243)-(251)),- “t_d_1.mat” contains the associated 2x1 vectors of labels (see eq. (235)).- “Yolo v2” folder containing the folder “Dataset_YOLO” and the table “obj_dataset_tab”, where- “Dataset_YOLO_v2” contains (inside the sub-folder “obj_dataset”) the Range-Azimuth maps used for training the YOLO v2 network (see eqs. (257)-(258) and Fig. 30);- “obj_dataset_tab” contains the path, the bounding box and the label associated to the Range-Azimuth maps (see eq. (256)-(266)).Cite as: A. Davoli, G. Guerzoni and G. M. Vitetta, "Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview," in IEEE Access, vol. 9, pp. 33704-33755, 2021, doi: 10.1109/ACCESS.2021.3061424.

This dataset consists of unprocessed 2MHz radar profiles over three ice rises Blåskimen Island (four profiles), Kupol Ciolkovskogo (four profiles) and Kupol Moskovskij (three profiles), Fimbul Ice Shelf, western Dronning Maud land. Data includes corresponding GPS information along each profile. The scientific objective is to calculate the ice thickness and the bed profile of the ice rise. It can also be used to study engalical stratigraphy in the profiles. The data are provided in MATLAB (.mat) file format.

Blåskimen Island: IRD_deep_radar.mat
Kupol Ciolkovskogo: IRB_deep_radar.mat
Kupol Moskovskij: IRC_deep_radar.mat
Each consisting struct-class variables for each profile as ‘Profile_1’, Profile_2’ etc.

Profile_1 includes - - ‘data’ matrix with radar amplitude data. - ‘twt’ array with two way travel time for the radar signal in seconds - ‘lat’ and ‘lon’ are in decimal degrees - ‘x’ and ‘y’ show the coordinates, and ‘z’ shows elevations. All are in the unit of meters. - ‘dist’ shows the distance at a point from the start in meters. The data was collected by towing the system with a speed of 8-10 kmph. The offset between the receiver and transmitter is ~119 meters. There are 1024 samples in fast direction.

See the following manuscript for further details: Goel, V., Brown, J., and Matsuoka, K.: Glaciological settings and recent mass balance of Blåskimen Island in Dronning Maud Land, Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-61, in review, 2017.

The Micro Rain Radar (MRR; type MRR2) - manufactured by Meteorologische Messtechnik GmbH (Metek) - is a vertically pointing frequency modulated continuous wave (FM-CW) Doppler radar operating at 24.1 GHz (K band). The backscattered signal by falling hydrometeors is measured at 32 range gates. This data includes the output of the Improved MRR Processing Tool (IMProToo; Maahn and Kollias, 2012) for the MRR measurements at AWIPEV in 2017. IMProToo provides reliable values of equivalent radar reflectivity Ze, Doppler velocity and spectral width among other variables.

This dataset contains data from two vertically profiling K-band METEK Micro Rain Radars (MRRs) deployed during the 2015 Chilean Coastal Orographic Precipitation Experiment (CCOPE-2015). Information on the overall goals of CCOPE-2015, deployment strategy, and some results are found in Massmann, et al. (2017). All data were collected at Curanilahue, Chile (CRL). During most of the winter storms, CRL is located upstream of the Nahuelbuta Mountains, in coastal southern Chile.