MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors

Garcia, Javier Martinez; Zoeke, Dominik; Vossiek, Martin

doi:10.1109/access.2018.2857007

Cited by 24 publications

(5 citation statements)

References 17 publications

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“…These heatmaps are generated from ADC using fast Fourier transform in different dimensions, resulting in a high-order tensor with abundant information that meets the requirements of semantic segmentation. There are also many researchers who have demonstrated and studied the results in this area [ 18 , 19 , 20 ]. A representative example is that Ignacio Roldan et al addressed the issue of large data volume in radar heatmaps by using the Constant False Alarm Rate Method, which reduced the computational amount while maintaining high accuracy [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…This innovative approach reduces data volume and enhances the network’s ability to process the information contained within the radar heatmap, ultimately improving its overall performance. Another network architecture designed by Martinez et al introduced prior information into the model by taking into account the attributes of radar images, which significantly improved the recognition performance of radar heatmaps [ 19 ]. Sangtae Kim et al incorporated the long short-term memory network into radar signal processing and devised a deep neural network comprised of convolutional recursive units to enhance the dynamic target recognition capability in automotive radar systems [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation

Zhang,

Fan

2023

Sensors

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Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network’s performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation

Zhang,

Fan

2023

Sensors

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“…The usage of 2D images generated by a radar sensor, along with artificial intelligence, can be translated into the substitution of video cameras, allowing the establishment of a parking slot status through a convolutional neural network training that has the radar information as input data and throws slots status as results [57]. These sensors give the possibility to use one powerful device to cover a great number of parking slots.…”

Section: Radar Sensorsmentioning

confidence: 99%

Smart Parking: A Literature Review from the Technological Perspective

et al. 2019

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The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.

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“…Four basic pieces of information: range, velocity, angle, and size, are necessary for radar target recognition/imaging. Many studies have been carried out to improve the resolution and accuracy of the estimation of this information, such as structure enhancement for radar implementation [5]- [8], multiple-input multiple-output (MIMO) radar [9]- [12], virtual antennas [13], and multiple signal classification (MUSIC) algorithms [14]. In practical automotive radar systems, multiple target imaging capabilities are essential.…”

Section: Introductionmentioning

confidence: 99%

Antenna Element Space Interference Cancelling Radar for Angle Estimations of Multiple Targets

et al. 2021

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This paper focuses on estimating the angle and size of different-sized targets using a radar system that estimates the angles of trucks, vehicles, bicycles, and pedestrians. The problem is that the presence of a sidelobe from the larger target degrades the accuracy of estimating the angle and size of the smaller target in multiple target detection. In this paper, a novel scheme, called antenna element space interference cancelling (AIC), is proposed for reducing the influence of the sidelobe. The performance of the proposed AIC radar system was evaluated through both computer simulations and experiments. The simulation results show that the proposed AIC radar system can estimate the angles of smaller targets within a 1-degree error, and the size estimation error is 1 dB. The experimental results also show that the proposed AIC radar is effective in detecting smaller targets that cannot be detected with traditional methods due to the sidelobes of larger targets.

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MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors

Cited by 24 publications

References 17 publications

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation

Smart Parking: A Literature Review from the Technological Perspective

Antenna Element Space Interference Cancelling Radar for Angle Estimations of Multiple Targets

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