Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

With the operational deployment of the Stepped Frequency Microwave Radiometer (SFMR), hurricane reconnaissance and research aircraft provide near real-time observations of the 10 $\mathrm{m}$ ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 to 45 $\mathrm{m}$ $\mathrm{s}$ ⁻¹ with respect to GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45 mm $\mathrm{h}$ ⁻¹ at tropical storm- and hurricane-force speeds present in the current operational model. We also update the SFMR radiative transfer model to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current SFMR calibration procedures are required.

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.

This paper describes the development and validation of a statistical algorithm to retrieve global all-weather sea surface wind speeds (GAWS) from microwave radiometers in operational environment. Measurements from the Advanced Microwave Scanning Radiometer-2 (AMSR2) are utilized to demonstrate the efficacy of the new all-weather wind speed data product. The GAWS algorithm exploits the linear combination of dual-polarized radiometer channels to significantly mitigate the effect of rain contamination while maintaining sensitivity to all wind speed regimes from global winds to tropical cyclone conditions. The GAWS algorithm was developed using ~1000 AMSR2 orbits from 2013 - 2021 covering all possible variations of brightness temperatures and wind speeds. The Global Data Assimilation System (GDAS) and the Hurricane Weather Research and Forecasting Model (HWRF) were used as the assumed surface truth for training and validation. Results from comprehensive quantitative and qualitative analyses show that GAWS retrievals are less susceptible to rain than standard microwave radiometer wind speeds and can reach hurricane force winds up to hurricane category 5 (> 70 m/s).

The Advanced Microwave Scanning Radiometer-2 (AMSR2) on board the Global Change Observation Mission-Water (GCOM-W) launched in May 2012 by the Japan Aerospace Exploration Agency (JAXA) is acquiring electromagnetic radiation from the Earth for the purpose of monitoring its environmental and climate system. Among a suite of oceanic environmental data records (EDR), the Ocean Surface Winds Team of the Satellite Applications and Research (STAR) group at the National Oceanic and Atmospheric Administration (NOAA) has developed an all-weather wind speed (AWS) by exploiting AMSR2 brightness temperature (Tb) measurements. The new product provides wind speeds in normal and extreme weather conditions with minimal flagging and excellent accuracy. Validation results of this novel AMSR2 AWS product, represented in this paper, show a mean bias of 0 m/s and an rms error < 2 m/s when compared to numerical weather models under all weather conditions.

Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a novel OVW retrieval algorithm for tropical cyclones which improves the accuracy of scatterometer based ocean surface winds when compared to low-flying aircraft with in-situ and remotely sensed observations. Unlike the traditional maximum likelihood estimation (MLE) wind vector retrieval technique, this new approach sequentially estimates scalar wind directions and wind speeds. A detailed description of the algorithm is provided along with results for ten QuikSCAT hurricane overpasses (from 2003–2008) to evaluate the performance of the new algorithm. Results are compared with independent surface wind analyses from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s H*Wind surface analyses and with the corresponding SeaWinds Project’s L2B-12.5 km OVW products. They demonstrate that the proposed algorithm extends the SeaWinds capability to retrieve wind speeds beyond the current range of approximately 35 m/s (minimal hurricane category-1) with improved wind direction accuracy, making this new approach a potential candidate for current and future conically scanning scatterometer wind retrieval algorithms.