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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>NEXRAD Antenna Scan Rate – Wind Shear Detection Investigation Project Plan</title>
      <link>https://trid.trb.org/View/2724654</link>
      <description><![CDATA[The purpose of this project is to determine if wind shear phenomena can be reliably detected and measured with the Airport Surveillance Radar (ASR)-8 pulse Doppler system employing a continuous antenna scan.]]></description>
      <pubDate>Mon, 20 Jul 2026 11:11:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724654</guid>
    </item>
    <item>
      <title>Thunderstorm Turbulence Detection with Pulse Doppler Radar</title>
      <link>https://trid.trb.org/View/2719316</link>
      <description><![CDATA[The purpose of this project is to determine if a pulse Doppler radar can detect the turbulence associated with thunderstorms and provide turbulence warnings to aircraft in airport terminal areas. The Massachusetts Institute of Technology (MIT) Lincoln Laboratory, under Inter-Agency Agreement DTFA01-80-Y-10546, has cooperated in the establishment of a test bed at the Federal Aviation Administration (FAA) Technical Center, Atlantic City Airport, New Jersey. This consists of a pulse Doppler radar and peripheral equipment to observe, process, display, and record the Doppler information, and an instrumented aircraft to measure and record turbulence concurrently with the radar observations. Data were collected in 1980 and 1981. Data collection will continue in 1982 and 1983. This report describes the test bed, the radar and aircraft turbulence measurements, and presents results from the 1980 data collection.]]></description>
      <pubDate>Sun, 12 Jul 2026 16:35:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719316</guid>
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    <item>
      <title>Wake Vortex Detection with Pulsed-Doppler Radar ASR-8 Feasibility Tests</title>
      <link>https://trid.trb.org/View/2717072</link>
      <description><![CDATA[The purpose of this project was to examine the feasibility of detecting aircraft wake vortices with the Technical Center's Airport Surveillance Radar (ASR)-8 weather system. The trailing wake vortices produced by large jet aircraft have long been identified as a problem confronting the Air Traffic Control (ATC) system. The problem arises primarily from small aircraft encounters with these vortices when following too closely behind large jets in terminal area operations. A number of damaging and/or fatal accidents have occurred under such circumstances.]]></description>
      <pubDate>Wed, 08 Jul 2026 10:02:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717072</guid>
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    <item>
      <title>Test and Evaluation of the Radar Thunderstorm Turbulence Detection System (Phase I)</title>
      <link>https://trid.trb.org/View/2711594</link>
      <description><![CDATA[A thunderstorm turbulence detection test bed was developed at the Federal Aviation Administration (FAA) Technical Center by the Massachusetts Institute of Technology, Lincoln Laboratory. This consists of a system to measure and process Doppler radar parameters, and an FAA aircraft instrumented to measure turbulence concurrently with the radar observations. The test bed is being used to investigate the relationship between radar- and aircraft-measured turbulence. Radar measurements of the Doppler spectrum width and aircraft measurements of airspeed fluctuations and center-of-gravity normal accelerations were converted to  Ɛ supra ⅓ (cube root of the turbulence dissipation factor) for comparison. Several data collections were made during the summer of 1980. Results of data analysis showed that the major turbulence sequences experienced by the aircraft were essentially reflected by the radar. However, linear correlation coefficients between radar and aircraft Ɛ supra ⅓ were only about 0.5. The low correlations are considered to be due to differences in response to turbulence by the two measuring systems, deficiencies in the radar processing, and radar data interpolation errors between the 80-second radar scans. In a more practical analysis, radar-measured turbulence, classified into ranges of light, moderate, and severe turbulence, showed a potentially useful relationship to aircraft turbulence. The predictive value was enhanced by consideration of radar reflectivity factor as a screening variable.]]></description>
      <pubDate>Tue, 23 Jun 2026 11:09:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711594</guid>
    </item>
    <item>
      <title>Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review</title>
      <link>https://trid.trb.org/View/2561816</link>
      <description><![CDATA[With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligent transportation. Among these equipped sensors, the radar sensor plays a crucial role in providing robust perception information in diverse environmental conditions. This review focuses on exploring different radar data representations utilized in autonomous driving systems. Firstly, we introduce the capabilities and limitations of the radar sensor by examining the working principles of radar perception and signal processing of radar measurements. Then, we delve into the generation process of five radar representations, including the ADC signal, radar tensor, point cloud, grid map, and micro-Doppler signature. For each radar representation, we examine the related datasets, methods, advantages and limitations. Furthermore, we discuss the challenges faced in these data representations and propose potential research directions. Above all, this comprehensive review offers an in-depth insight into how these representations enhance autonomous system capabilities, providing guidance for radar perception researchers. To facilitate retrieval and comparison of different data representations, datasets and methods, we provide an interactive website at https://radar-camera-fusion.github.io/radar.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561816</guid>
    </item>
    <item>
      <title>Study of Parameters Influencing the Accuracy of the SDF Method Localization</title>
      <link>https://trid.trb.org/View/2624155</link>
      <description><![CDATA[Modern military operations increasingly use unmanned aerial vehicles (UAVs) not only for observation and reconnaissance, but also for active localization of radio emission sources. One of the methods used for this purpose is the signal Doppler frequency method (SDF), based on the analysis of the frequency of the signal received by the moving sensor. The paper presents a theoretical analysis and simulation studies aimed at determining the effect of selected parameters on the accuracy of emitter localization using the SDF method. In particular, the factors such as data acquisition time, accuracy of Doppler frequency estimation, carrier frequency and the velocity of the moving sensor were considered. The aim of the work is to indicate which of these parameters are crucial for the quality of localization. We formulate conclusions that can support the development of the resistant to interference and more precise localization systems based on the SDF method. The presented approach can be used both in real armed conflicts and in work on autonomous electronic reconnaissance systems.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624155</guid>
    </item>
    <item>
      <title>Radar4Motion: IMU-Free 4D Radar Odometry With Robust Dynamic Filtering and RCS-Weighted Matching</title>
      <link>https://trid.trb.org/View/2617955</link>
      <description><![CDATA[Accurate localization is crucial for autonomous vehicle navigation. In particular, there is active research in odometry that estimates a vehicle's transformation over time. To achieve this, various environmental perception sensors are utilized, among which radar sensors stand out for their cost-effectiveness and robustness against adverse weather conditions compared to LiDAR sensors. However, traditional radar sensors provide only 2D spatial information (x, y, doppler). Recent technical advancements have introduced 4D imaging radar, providing 3D spatial (x, y, z, doppler) information. Nonetheless, radar data remains sparse and noisy when compared to LiDAR data, making it challenging to apply conventional LiDAR-based odometry algorithms. To address this challenge, we propose a radar point cloud odometry system that leverages Doppler, representing relative velocity, and Radar cross-section (RCS), a unique measure of an object's reflective ability. The IMU-free ego-motion estimation step utilizes Doppler data to generate an initial guess for registration. Additionally, to effectively extract meaningful points, we propose a polar-grid-based feature extraction algorithm utilizing RCS. To overcome the sparsity of radar point cloud data, we perform RCS-weighted accumulated scans-to-submap matching, where weights for point cloud registration are modeled based on RCS values to achieve robust odometry. Our proposed algorithm was experimentally evaluated using the View-of-Delft dataset. The results demonstrate that our algorithm outperforms existing methods, providing superior odometry performance.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617955</guid>
    </item>
    <item>
      <title>Impact of sea cluttering and wave shadowing on U2S MIMO channel model incorporating UAV-ship 6D motion in maritime environments</title>
      <link>https://trid.trb.org/View/2587169</link>
      <description><![CDATA[Unmanned aerial vehicles (UAVs) are increasingly integrated into maritime communication systems, presenting unique challenges due to complex maritime scenario. By considering six-dimensional (6D) motion of both UAV and ship alongside sea cluttering and wave shadowing phenomena, this paper presents a novel non-stationary 6D geometry-based multiple-input multiple-output (MIMO) channel model for UAV to ship (U2S) communications for maritime scenario. Besides, the dynamic interactions between UAV and ship motions and maritime environments are also described in the proposed model. The time-variant channel coefficient and channel parameters like, path loss (PL), shadow fading (SF), Doppler frequencies, wave shadowing, sea cluttering, time-variant distances, time-variant delay, time-variant power, time-variant angles, are derived and analyzed thoroughly in this proposed method. Additionally, the theoretical and statistical properties like, probability density function (PDF), autocorrelation function (ACF), level crossing rate (LCR), Doppler power spectral density (DPSD), and signal to clutter noise ratio (SCNR) are investigated with the effect of sea cluttering and wave shadowing. Finally, the validation of the channel model and its theoretical derivations highlight its suitability for evaluating and designing U2S communication systems in maritime environments. The suggested model can be useful for improving U2S communication systems, to enhance reliability and performance in maritime communication environments.]]></description>
      <pubDate>Tue, 25 Nov 2025 08:55:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587169</guid>
    </item>
    <item>
      <title>RaViDeep: Target Detection Based on Deep Fusion of Radar and Vision in Berthing Scenarios</title>
      <link>https://trid.trb.org/View/2591917</link>
      <description><![CDATA[Current radar-vision fusion techniques struggle to fully leverage the complementary data from sparse radar points and depth-deficient images, impacting their overall effectiveness. This paper proposes RaViDeep, a novel target detection method based on deep fusion of millimeter-wave radar and monocular image. Initially, a Semantic-based Point Cloud Registration (SPCR) module combines image semantics, radar hierarchical features, and Doppler data to enhance target spatial representation, yielding dense and stable semantic radar points. Subsequently, a Radar-Guided Depth Estimation (RGDE) module with Gaussian enhancement is introduced, utilizing accurate radar depth measurements to guide image depth estimation. This approach fosters a comprehensive scene understanding and effectively reduces measurement and calibration errors. Finally, a pseudo point cloud, generated by the estimated image depth, is integrated with the semantic radar points to facilitate target detection. Tailored for autonomous berthing tasks in wharf scenarios, a novel Non-Occupied Overlap (NOO) metric is developed. Experimental results demonstrate that RaViDeep surpasses state-of-the-art methods, achieving a 12.10% improvement in the NOO metric and a 13.90% improvement in recall. These results verify the superior performance and robustness of our method in practical wharf scenarios.]]></description>
      <pubDate>Wed, 12 Nov 2025 09:36:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591917</guid>
    </item>
    <item>
      <title>MEMS-Based Gyro De-Noising via AUV Dynamic for Enhancing Navigation</title>
      <link>https://trid.trb.org/View/2591651</link>
      <description><![CDATA[Inertial navigation using low cost micro-electromechanical systems (MEMS) inertial measurement units (IMUs) loses its accuracy after a short time. Although integration with auxiliary sensors, such as Doppler Velocity Log (DVL), control the rate of INS drift error, Inertial Navigation System (INS)-DVL navigation using low-cost IMUs has not yet achieved sufficient accuracy, because of un-observability of gyro drift error (low-band frequency error). One of the ways to increase accuracy of this algorithm is pre-filtering the gyro. In the past works, stochastic models was used in the gyro pre-filter. But in this paper longitudinal and lateral deterministic dynamic model of vehicle are used in the gyro pre-filter process. These models include the coupling of linear and angular velocity components. This coupling allows the use of DVL to improve gyro accuracy by filtering the low-band frequency errors. It is shown that in the conventional INS-DVL algorithm, the mentioned error bias is not observable, but in the proposed method, it is observable. The proposed algorithm is evaluated in a simulation and two field tests. In the simulation, gyro pre-filering by first order Gauss Markov model reduces the Root Mean Square (RMS) position error by 25% compared to when the gyro was not pre-filtered. The gyro pre-filter using the proposed method reduces this error by 62%. In the field tests, these error reductions reaches 19% and 41%, respectively.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591651</guid>
    </item>
    <item>
      <title>Interim Traffic Monitoring System on U.S. 59 (Eastex) Freeway: Year One Report</title>
      <link>https://trid.trb.org/View/2570739</link>
      <description><![CDATA[Monitoring freeway traffic conditions in construction zones is difficult. A 17.7 kilometer (11 mile) section of U.S. 59 (Eastex) Freeway from I-610 North Loop to Beltway 8 North in Houston, Texas is under construction and was chosen for this demonstration project. The selected sensor technologies were the Doppler Radar and side-fire microwave ranging. Wooden utility poles installed near the freeway lanes provided sensor placements. Mobility of the sensor is paramount. At report time, the equipment had been purchased, an electrical contractor chosen, and equipment was being installed.]]></description>
      <pubDate>Tue, 26 Aug 2025 14:34:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570739</guid>
    </item>
    <item>
      <title>Status Report on Operations from September 1996 to August 1997: Final Report</title>
      <link>https://trid.trb.org/View/2549175</link>
      <description><![CDATA[Four alternative (to loop detection) sensor types were tested in freeway construction areas in Houston, Texas. The Whelen Doppler radar units, once attuned to the local conditions, appear to render acceptable speed measures. The side fire microwave ranging sensor (R TMS from EIS) appears to be very suspectable to creating multiple reflections when exposed to hard vertical surfaces (as found in concrete median barriers, abbreviated as CMB). The TraffiCam is a visual imaging device known primarily as an inexpensive, publicly available VIVDS product. Based on the performance of the unit tested, the software and hardware requires substantial improvements before utilization as an acceptable freeway alternative detector. The SmartSonic sensors were installed in an advantageous position for passive monitoring of two lanes of traffic, one sensor per lane. The short-term test indicated that occlusion occurs in sonic as well as VIVDS devices. The Sonic sensor would work best when positioned directly over the lane. The many calibration parameters and expansive range limits may confuse the operator before acceptable volume and speed measures for three length vehicle classes.]]></description>
      <pubDate>Wed, 25 Jun 2025 14:47:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549175</guid>
    </item>
    <item>
      <title>Multiple Subbands Coherent Accumulation Target Detection Algorithm for DDMA-MIMO Radar</title>
      <link>https://trid.trb.org/View/2425300</link>
      <description><![CDATA[Compared with the traditional time division multiple access - multiple input multiple output (TDMA-MIMO) radar, Doppler division multiple access - multiple input multiple output (DDMA-MIMO) radar enables the design of orthogonal waveform in Doppler domain instead of in time domain. Thus DDMA-MIMO radar performs well in long range radar application. However, it suffers from Doppler ambiguous and energy defocus of the target spectrum in DDMA-MIMO radar, and as a result the target detection performance degrades. To tackle these problems, a target detection algorithm based on multiple subbands coherent-accumulation (MSCA) is proposed in this paper. First, the empty-band is inserted into the Doppler domain by modifying the phase modulation of the transmitting antennas. Based on the periodic extension of Doppler in DDMA-MIMO system, a coherent accumulation and Doppler ambiguity mitigation method based on subband correlation is proposed. By exploiting the low energy characteristic of the empty subband, this method compares the energy of accumulated subband after MSCA to extract the real velocity of target. It also solves the problem of Doppler ambiguity caused by the period extension of target velocity. Besides, the proposed method can improve the detection performance, avoiding the target being drowned by noise. Simulation and real data results show that the proposed method can effectively alleviate the Doppler ambiguity, improve the target detection performance under low signal-to-noise scenarios.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425300</guid>
    </item>
    <item>
      <title>Machine Learning Methods for Track Condition Assessment Using Repeated Inspection Data</title>
      <link>https://trid.trb.org/View/2447213</link>
      <description><![CDATA[The Railway Technologies Laboratory (RTL) at Virginia Tech has significantly advanced the science of using Doppler velocimetry methods to evaluate track stability. This report provides an overview of the progress made during the project. The project was split into two primary tasks. Task 1 concentrates on analyzing existing data from past field testing of a Multifunction Doppler LIDAR system. Task 2 focused on preparing and making the necessary improvements to the LIDAR instruments for future track testing. It was determined that designing and assembling a new set of digital platforms (i.e., special-purpose computers) would be important for the long-term success of testing the Multifunction Doppler LIDAR in track testing. During this study, two new digital platforms were assembled in addition to a second LIDAR Electro-optical unit. Further, new focusing lenses were assembled and the output laser power was increased to maximize the signal-to-noise ratio (SNR) of the LIDAR instrument. These electro-optical upgrades are expected to yield an SNR improvement of up to four times the previous units. Bench top system tests were performed with the new LIDAR instrumentation and the new instruments are now ready for field testing.]]></description>
      <pubDate>Wed, 20 Nov 2024 13:08:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447213</guid>
    </item>
    <item>
      <title>Application of Doppler Lidar Sensors for Assessing Track Gauge Widening in Curves and Locations With High-Lateral Forces</title>
      <link>https://trid.trb.org/View/2447208</link>
      <description><![CDATA[The primary purpose of this study is to evaluate the use of Doppler Lidar sensors for assessing track weakening that would indicate early stages of track instability. Such track weakening could lead to gage widening or track buckling due to rail thermal expansion. A series of tests were performed at the Transportation Technology Center’s High Tonnage Loop, where two sections of track were “doctored” to have weaker lateral strength, one on a tangent and another one in a curve. Multiple tests were performed at speeds ranging from 10 – 40 mph during which the lateral and vertical deflections of the rail were measured under the weight of the passing wheels of a heavily loaded gondola. The track weakness is created by removing the rail spikes from eight consecutive ties. The measurements from the soft sections were compared with a track section on a tangent length of track that is determined to have nominally sufficient (“good”) stiffness. The objectives of this project are to: (1) evaluate the capabilities of the Doppler Lidar sensors to measure changes in track conditions in both tangent and curved track, (2) compare the effect of speed on the reliability of the track condition detection, (3) assess the application of different analytical tools to determine if an automated detection process is feasible, and (4) provide recommendations for further study into the measurement of track dynamics with Doppler Lidar systems.]]></description>
      <pubDate>Fri, 15 Nov 2024 09:47:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2447208</guid>
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