Effects of signal distortion in a FMCW radar on range resolution

Bezoušek, Pavel; Hájek, Martin; Pola, Marek

doi:10.1109/comite.2010.5481557

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…With a fixed radar FMCW cycle, the modulation bandwidth and radar ranging error have an inverse relationship (1), where

is the speed of light and

represents the bandwidth of FMCW. Changes in the linearity of FMCW modulation

also affect the mixed signal, causing range errors, and the magnitude of range errors is directly proportional to the actual range between the target and the radar (2); when the target has radial motion relative to the radar, the Doppler frequency shift produced by the radial velocity

also interferes with the radar’s ranging results (3), where

represents the frequency modulation period of FMCW,

represents the wavelength corresponding to the operating frequency, and

represents the angle between the target and the radar normal [ 25 , 26 , 27 ].

…”

Section: Principles Of Autonomous Driving Radarmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

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“…With a fixed radar FMCW cycle, the modulation bandwidth and radar ranging error have an inverse relationship (1), where

is the speed of light and

represents the bandwidth of FMCW. Changes in the linearity of FMCW modulation

also interferes with the radar’s ranging results (3), where

represents the frequency modulation period of FMCW,

represents the wavelength corresponding to the operating frequency, and

represents the angle between the target and the radar normal [ 25 , 26 , 27 ].

…”

Section: Principles Of Autonomous Driving Radarmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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“…The FMICW system works as a combination of the pulse radar and FMCW (Frequency Modulated Continuous Wave) radar. Examples of FMCW systems are described in [2,3]. The principle of the FMICW radar is described in [4], but this system was developed for the measurement of the ionosphere, and the pulse part of the system does not have a big influence on the signals.…”

Section: Fmicw Radar Descriptionmentioning

confidence: 99%

“…If we look at the neuron model, we can see that the neuron is based on principles of digital filters. The mathematical description of one neuron is in Equation (3). The benefit of this model is the easy application on the FPGA with faster processing speed.…”

Section: Neural Networkmentioning

confidence: 99%

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Neural Networks Application for Processing of the Data from the FMICW Radars

et al. 2019

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In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

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