A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

The NCAR Airborne 94-GHz Cloud Radar: Calibration and Data Processing

The 94-GHz airborne HIAPER Cloud Radar (HCR) has been deployed in three major field campaigns, sampling clouds over the Pacific between California and Hawaii (2015), over the cold waters of the Southern Ocean (2018), and characterizing tropical convection in the Western Caribbean and Pacific waters off Panama and Costa Rica (2019). An extensive set of quality assurance and quality control procedures were developed and applied to all collected data. Engineering measurements yielded calibration characteristics for the antenna, reflector, and radome, which were applied during flight, to produce the radar moments in real-time. Temperature changes in the instrument during flight affect the receiver gains, leading to some bias. Post project, we estimate the temperature-induced gain errors and apply gain corrections to improve the quality of the data. The reflectivity calibration is monitored by comparing sea surface cross-section measurements against theoretically calculated model values. These comparisons indicate that the HCR is calibrated to within 1–2 dB of the theory. A radar echo classification algorithm was developed to identify “cloud echo” and distinguish it from artifacts. Model reanalysis data and digital terrain elevation data were interpolated to the time-range grid of the radar data, to provide an environmental reference.

Towards a multi-year urban precipitation climatology at 100 m scale using X-band radar observations

<p>An operational, single-polarized X-band weather radar provides measurements in Hamburg’s city center for almost eight years. This weather radar operates at an elevation angle (~3.5°) with a high temporal (30 s), range (60 m), and sampling (1°) resolution resulting<span> in a</span> high information density within <span>the</span> 20 km <span>scan radius</span>. <span>Studies on short time periods (several months) proofs the performance of this low-cost local area weather radar. </span><span>For example, a</span><span> case study on a tornado in a rain event demonstrates its refined resolution </span><span>compared to</span><span> the German nationwide C-band radars. </span><span>Now, we aim for a eight-year precipitation climatology with 100 m resolution. This data set will enable reliable studies on urban extreme precipitation. This presentation will describe h</span><span>ow we </span><span>can</span><span> infer a precipitation estimate based on multi-</span><span>year</span><span> weather radar observations in the urban area of Hamburg.</span></p><p>The single-polarization and <span>small</span> <span>wavelength</span> <span>comes along with</span> high resolution <span>but at the same time</span> high uncertainties. We address several sources of errors affecting th<span>e</span> radar-based <span>precipitation</span> estimate, like the radar calibration, alignment, attenuation, noise, non-meteorologial echoes, <span>and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at relevant heights reducing errors effectively concerning the radar calibration and required statistical relations (</span><span><em>k</em></span><span>-</span><span><em>Z</em></span><span> and </span><span><em>Z</em></span><span>-</span><span><em>R</em></span><span> relation). We outline the performance of the correction methods for long time periods and discuss open issues and limitations.</span></p><p><span>With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of </span><span>urban </span><span>precipitation will be possible. </span><span>This data set will be used for</span><span> further hydrological research in an urban area </span><span>within the project <em>Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides</em> of the</span><span> Cluster of Excellence <em>Climate Climatic Change, and Society</em> (CliCCS).</span></p>

On the Spectral and Polarimetric Signatures of a Bright Scatterer before and after Hardware Replacement

A previous study has used the stable and peculiar echoes backscattered by a single “bright scatterer” (BS) during five winter days to characterize the hardware of C-band, the dual-polarization radar located at Monte Lema (1625 m altitude) in Southern Switzerland. The BS is the 90 m tall metallic tower on Cimetta (1633 m altitude, 18 km range). In this note, the statistics of the echoes from the BS were derived from other ten dry days with normal propagation conditions in winter 2015 and January 2019. The study confirms that spectral signatures, such as spectrum width, wideband noise and Doppler velocity, were persistently stable. Regarding the polarimetric signatures, the large values (with small dispersion) of the copolar correlation coefficient between horizontal and vertical polarization were also confirmed: the average value was 0.9961 (0.9982) in winter 2015 (January 2019); the daily standard deviations were very small, ranging from 0.0007 to 0.0030. The dispersion of the differential phase shift was also confirmed to be quite small: the daily standard deviation ranged from a minimum of 2.5° to a maximum of 5.3°. Radar reflectivities in both polarizations were typically around 80 dBz and were confirmed to be among the largest values observed in the surveillance volume of the Monte Lema radar. Finally, another recent 5-day data set from January 2020 was analyzed after the replacement of the radar calibration unit that includes low noise amplifiers: these five days show poorer characteristics of the polarimetric signatures and a few outliers affecting the spectral signatures. It was shown that the “historical” polarimetric and spectral signatures of a bright scatterer could represent a benchmark for an in-depth comparison after hardware replacements.

Absolute calibration method for frequency-modulated continuous wave (FMCW) cloud radars based on corner reflectors

This article presents a new cloud radar calibration methodology using solid reference reflectors mounted on masts, developed during two field experiments held in 2018 and 2019 at the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA) atmospheric observatory, located in Palaiseau, France, in the framework of the Aerosol Clouds Trace gases Research InfraStructure version 2 (ACTRIS-2) research and innovation program. The experimental setup includes 10 and 20 cm triangular trihedral targets installed at the top of 10 and 20 m masts, respectively. The 10 cm target is mounted on a pan-tilt motor at the top of the 10 m mast to precisely align its boresight with the radar beam. Sources of calibration bias and uncertainty are identified and quantified. Specifically, this work assesses the impact of receiver compression, temperature variations inside the radar, frequency-dependent losses in the receiver's intermediate frequency (IF), clutter and experimental setup misalignment. Setup misalignment is a source of bias, previously undocumented in the literature, that can have an impact of the order of tenths of a decibel in calibration retrievals of W-band radars. A detailed analysis enabled the quantification of the importance of each uncertainty source to the final cloud radar calibration uncertainty. The dominant uncertainty source comes from the uncharacterized reference target which reached 2 dB. Additionally, the analysis revealed that our 20 m mast setup with an approximate alignment approach is preferred to the 10 m mast setup with the motor-driven alignment system. The calibration uncertainty associated with signal-to-clutter ratio of the former is 10 times smaller than for the latter. Following the proposed methodology, it is possible to reduce the added contribution from all uncertainty terms, excluding the target characterization, down to 0.4 dB. Therefore, this procedure should enable the achievement of calibration uncertainties under 1 dB when characterized reflectors are available. Cloud radar calibration results are found to be repeatable when comparing results from a total of 18 independent tests. Once calibrated, the cloud radar provides valid reflectivity values when sampling midtropospheric clouds. Thus, we conclude that the method is repeatable and robust, and that the uncertainties are precisely characterized. The method can be implemented under different configurations as long as the proposed principles are respected. It could be extended to reference reflectors held by other lifting devices such as tethered balloons or unmanned aerial vehicles.