Single Chip Radar Processing for Object Detection

Nagalikar, Shivasharan; Mody, Mihir; Baranwal, Ankur; Kumar, VC; Shankar, Prithvi; Farooqui, Mohammad Asif; Shah, Malav; Sangani, Nikhil; Rakesh, Yc; Karkisaval, Abhishek; Chellappan, Sriraj; Sm, Stalin; Pratti, Anita; Rosales, Randy; Dixit, Tanu; Murphy, Sean

doi:10.1109/icce56470.2023.10043549

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Cited by 3 publications

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“…In object detection [4,5,6,7], the neck network primarily enhances the extraction of features from the backbone network. It separately extracts low-level features from different layers of the backbone network to obtain higher-level features and fuses features of different levels and resolutions.…”

Section: Introductionmentioning

confidence: 99%

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

Dong,

Zhang,

Zhang

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Section: Introductionmentioning

confidence: 99%

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

Dong,

Zhang,

Zhang

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Nature-Like Structural Node Components for Software and Hardware Complexes on the Basis of Single-Board Computers

Poletikin

Mengazetdinov

Semenkov

2023

2023 International Russian Smart Industry Conference (SmartIndustryCon)

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A Survey of Automotive Radar and Lidar Signal Processing and Architectures

Giuffrida,

Masera,

Martina

2023

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In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle’s surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS’s intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed.

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Single Chip Radar Processing for Object Detection

Cited by 3 publications

References 5 publications

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

Nature-Like Structural Node Components for Software and Hardware Complexes on the Basis of Single-Board Computers

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

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