Mr. Yunish Shrestha Profile

Yunish Shrestha

at Advanced Radar Research Ctr

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Author

Publications (5)

Proceedings Article | 27 May 2022 Presentation + Paper

Development of polarimetric radar product and initial application for airborne HIWC detection

Yunish Shrestha, Yan Rockee Zhang, Jakob Fusselman, Greg McFarquhar, WIlliam Blake, Steven Harrah

Proceedings Volume 12108, 1210806 (2022) https://doi.org/10.1117/12.2618447

KEYWORDS: Radar, Monte Carlo methods, Reflectivity, Data modeling, Particles, Scattering, Autoregressive models, Antennas, Sensors, Polarimetry

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Proceedings Article | 27 May 2022 Presentation + Paper

Application of impulse radar to snow and ice penetration: a case for supporting snow layer modeling and characterizations

Jakob Fusselman, Yunish Shrestha, Hernan Suarez, Yan Rockee Zhang, Sudantha Perera, Gordon Pratt, Nicole Rota

Proceedings Volume 12108, 121080I (2022) https://doi.org/10.1117/12.2618261

KEYWORDS: Radar, 3D modeling, Antennas, Finite-difference time-domain method, Data modeling, Wave propagation, Sensors, Electromagnetism, Radar sensor technology, Foam

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Proceedings Article | 12 April 2021 Presentation + Paper

Ultra-compact ultra-wideband radar for high-speed target tracking

Jakob Fusselman, Matthew Gilliam, Yunish Shrestha, Yan (Rockee) Zhang, Keith Kelly

Proceedings Volume 11742, 117420F (2021) https://doi.org/10.1117/12.2587215

KEYWORDS: Radar, Sensors, UWB radar, Antennas, X band, Target detection, Radar sensor technology, Oceanography

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Proceedings Article | 12 April 2021 Presentation + Paper

Development of simulation and evaluation for high ice water content (HIWC) hazard detection using airborne radar

Yunish Shrestha, Yan (Rockee) Zhang, Greg McFarquhar, William Blake, Steven Harrah

Proceedings Volume 11742, 1174211 (2021) https://doi.org/10.1117/12.2586347

KEYWORDS: Radar, 3D modeling, Particles, Data modeling, Clouds, Standards development, Systems modeling, Space operations, Sensors, Safety

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High Ice Water Content (HIWC) is an atmospheric condition at high altitude that may lead to failure of jet engines. As a potential threat to aviation safety and space launch operation, it has received significant attentions from cross-disciplinary communities. Detecting HIWC conditions with airborne radar is essential to the safe monitoring of this type of hazard, however it has unique and significant challenges. For example, in general, small ice particles and clusters of ice particles do not register strong radar reflectivity, which is a challenge to the sensitivities and resolutions of small aperture airborne radars. Second, it is difficult to discriminate HIWC from other atmosphere conditions, such as general precipitations, and evaluate the threat level (in quantity of Ice Water Content, or IWC) with remote sensing only. In this study, we developed a novel simulation-based approach, which uses the in-situ probe collected HIWC cloud probe data during a series of flight test campaigns, as well as the microphysical particle models retrieved from these data as the basis of simulations. Then, we combine and reconcile these models with the ground-radar measurements, which leads to a three-dimensional truth gird. Using this truth field, we developed a single-cell-Monte-Carlo (SCMC) simulation implementation, which creates and generates airborne weather radar signatures and moments for each individual resolution cell. The simulation has incorporated (1) An initial framework of airborne radar system and sensor modeling, (2) Modeling of ground clutters and effect of antenna patterns. The simulation tool has significant applications in the areas of (1) Guidance of designing and development of next generation airborne hazard sensing and avoidance radars. (2) Support industry standard making and performance evaluations such as FAA, and (3) Support scientific studies on airborne radar signatures and techniques for further understanding of hazardous atmosphere conditions for aviation.

Proceedings Article | 23 April 2020 Presentation + Paper

Orthogonality using frequency division multiplexing to enable multiple input multiple output in an automotive radar

Yunish Shrestha, Yan Zhang

Proceedings Volume 11408, 1140813 (2020) https://doi.org/10.1117/12.2558263

KEYWORDS: Radar, Antennas, Time division multiplexing, Spatial resolution, Frequency division multiplexing, Sensors

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Autonomous vehicles have always been a field of considerable research interest. Past research have demonstrated achievements assuring that self-driving cars are, in fact the future of mobility. Self-driving cars have been made possible by sensor fusion technique, which incorporates sensors, including camera and radar. Cameras have the best resolution. Nevertheless, their ability to sense may be affected in extreme weather or night conditions. Radars are not affected by these conditions but lack the resolution when compared with radar. Most of the automotive radars are Frequency Modulated Continuous Wave (FMCW) radars whose range resolution depends on the bandwidth of the FMCW chirp, and spatial resolution depends upon the number of the receiving antennas. Having a higher number of receiving antenna elements will improve the angular resolution. Instead of increasing physical receiving antennas, it is possible to generate virtual receiving antennas by adding transmitting antennas, commonly known as the Multiple Input Multiple Output (MIMO) technique. MIMO requires orthogonal signals in multiple transmitting antennas. Commercial automotive radars have implemented the capability of MIMO using Time Division Multiplexing (TDM) and Binary Phase Modulation (BPM) in 2Tx and 4Rx systems. Although the angular resolution is improved, the maximum unambiguous velocity is reduced by half. This paper proposes the Frequency Division Multiplexing (FDM) Technique to achieve orthogonality. A full radar system has been simulated in MATLAB environment, which shows the possibility of using FDM in automotive radars without compromising the maximum unambiguous velocity. Frequency modulated signal with different starting frequencies for two Tx antenna is used to create 8 Rx virtual channels. FDM usually requires an increment in sampling frequency of Analog to Digital Converter (ADC). In this paper, the two starting frequencies are chosen, such that the requirement of higher sampling rate has been eliminated.

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