Implementation of MIMO Beamforming on an OTS FMCW Automotive Radar

Pirkani, Anum; Pooni, Sukhjit; Cherniakov, M.

doi:10.23919/irs.2019.8768103

Cited by 18 publications

(5 citation statements)

References 3 publications

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“…The core components of the experimental setup included a Texas Instruments AWR1642 BOOST mmWave radar sensor and a Raspberry Pi singleboard computer. The mmWave radar sensor, equipped with two transmit antennas and four receiving antennas [32], was the primary sensing device responsible for detecting and tracking a moving person. In this setup, four receive antennas capture signals from varied angles, enhancing Doppler shift information.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

Chowdhury,

Melige,

Pal

et al. 2023

Electronics

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This paper introduces a computationally inexpensive technique for moving target detection in challenging outdoor environments using millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radars leveraging traditional signal processing methodologies. Conventional learning-based techniques for moving target detection suffer when there are variations in environmental conditions. Hence, the work described here leverages robust digital signal processing (DSP) methods, including wavelet transform, FIR filtering, and peak detection, to efficiently address variations in reflective data. The evaluation of this method is conducted in an outdoor environment, which includes obstructions like woods and trees, producing an accuracy score of 92.0% and precision of 91.5%. Notably, this approach outperforms deep learning methods when it comes to operating in changing environments that project extreme data variations.

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Section: Methodsmentioning

confidence: 99%

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

Chowdhury,

Melige,

Pal

et al. 2023

Electronics

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“…[138] Auto-motive Radar A real-time signal processing algorithm is developed for the Texas Instruments AWR1642 chipset, presenting it as a W-band MIMO-FMCW imaging radar for automotive applications with a range resolution of 4.1cm and an angular resolution of 14.2°, aligning with theoretical and simulation predictions in MATLAB. [139] Beam-forming in 5G and 6G…”

Section: Auto-motive Communicationsmentioning

confidence: 99%

Review of Joint Radar, Communication, and Integration of Beam-forming Technology

HUSSAIN,

2024

Preprint

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In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beam-forming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beam-forming technology and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beam-forming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future tech landscapes, while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beam-forming, and dual radar communication technologies.

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“…The same period of 30 s is used for each record. Since including more N Rx yield a better angular resolution, both transmitting antennas were used to simulate the effect of 2 × N Rx [30], resulting in an angular resolution of θ res = 3.19 • . Thus, activities as gesture and reaching, which require high angle and range resolutions, can be detected.…”

Section: A Experimental Setupmentioning

confidence: 99%

A MIMO Radar-Based Metric Learning Approach for Activity Recognition

Aziz¹,

Metwally²,

Weller³

et al. 2021

Preprint

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Human activity recognition is seen of great importance in the medical and surveillance fields. Radar has shown great feasibility for this field based on the captured micro-Doppler (µ-D) signatures. In this paper, a MIMO radar is used to formulate a novel micro-motion spectrogram for the angular velocity (µ-ω) in non-tangential scenarios. Combining both the µ-D and the µ-ω signatures have shown better performance. Classification accuracy of 88.9 % was achieved based on a metric learning approach. The experimental setup was designed to capture micro-motion signatures on different aspect angles and line of sight (LOS). The utilized training dataset was of smaller size compared to the state-of-the-art techniques, where eight activities were captured. A few-shot learning approach is used to adapt the pre-trained model for fall detection. The final model has shown a classification accuracy of 86.42 % for ten activities.

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Implementation of MIMO Beamforming on an OTS FMCW Automotive Radar

Cited by 18 publications

References 3 publications

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

Review of Joint Radar, Communication, and Integration of Beam-forming Technology

A MIMO Radar-Based Metric Learning Approach for Activity Recognition

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