A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Mohanna, Ammar; Gianoglio, Christian; Rizik, Ali; Valle, Maurizio

doi:10.3390/s22031048

Cited by 4 publications

(5 citation statements)

References 37 publications

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“…If so, it could be that shadowing differs based on the pupils' tendency to participate in class and those tending to participate more should be seated in the back. Another non‐ecological example is that of a ‘radar shadow': one military plane, further away from the enemy radar, avoiding detection, by hiding in the ‘shadow' of another plane, closer to the radar (Mohanna et al 2022). If we push the limit a bit further, memory search is another domain in which shadow competition may be present.…”

Section: Future Research On Shadow Competitionmentioning

confidence: 99%

Shadow competition: its definition, prevalence, causes and measurement

Scharf

Ruxton

2023

Oikos

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Competition is a fundamental ecological process and an important mediating mechanism to natural selection and evolution. One form of competition, shadow competition, is evident when an approaching moving prey item is captured by a competing predator, earlier in the prey's trajectory, preventing it from reaching the attack range of a focal predator. The necessary definitional involvements of space and movement direction differentiate shadow competition from the more classical competition types of interference and exploitation, even though competition, in general, intensifies with spatial proximity. Compared to the latter two, shadow competition is understudied. Differentiating distinct competition types is important, because they may distinctively affect animal behaviour and higher levels of organisation. Although shadow competition is probably common in nature, there are only sporadic reports applying this terminology in systems of ambush predators, such as web-building spiders, pit-building antlions and sit-andwait predatory fish, and their moving prey. Here, we summarise clear cases of shadow competition in the published literature, cases in which we believe it to be present but not explicitly described as such, and potential scenarios in which shadow competition seems likely to be present but is currently unreported. We end with potential research directions for enhancing our still-fledgling understanding of shadow competition.

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Section: Future Research On Shadow Competitionmentioning

confidence: 99%

Shadow competition: its definition, prevalence, causes and measurement

Scharf

Ruxton

2023

Oikos

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“…However, conventional ghost target removal methods require adjusting parameters to achieve optimal results in varying experimental environments. Thus, there has been an increasing focus on using deep learning to detect ghost targets in recent years [12][13][14]. For example, the authors of [12] proposed a method for removing ghost targets using U-Net and the authors of [13] identified targets in a cluttered environment using convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there has been an increasing focus on using deep learning to detect ghost targets in recent years [12][13][14]. For example, the authors of [12] proposed a method for removing ghost targets using U-Net and the authors of [13] identified targets in a cluttered environment using convolutional neural networks. Additionally, in [14], the authors proposed a deep neural network-based classifier that uses features such as distance, angle, and signal strength of the targets to distinguish ghost targets.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Extraction of Indoor Environment from Radar Sensor-Based Mapping

Cho,

Kwak,

Lee

2024

Remote Sensing

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In this paper, we propose a method for extracting the structure of an indoor environment using radar. When using the radar in an indoor environment, ghost targets are observed through the multipath propagation of radio waves. The presence of these ghost targets obstructs accurate mapping in the indoor environment, consequently hindering the extraction of the indoor environment. Therefore, we propose a deep learning-based method that uses image-to-image translation to extract the structure of the indoor environment by removing ghost targets from the indoor environment map. In this paper, the proposed method employs a conditional generative adversarial network (CGAN), which includes a U-Net-based generator and a patch-generative adversarial network-based discriminator. By repeating the process of determining whether the structure of the generated indoor environment is real or fake, CGAN ultimately returns a structure similar to the real environment. First, we generate a map of the indoor environment using radar, which includes ghost targets. Next, the structure of the indoor environment is extracted from the map using the proposed method. Then, we compare the proposed method, which is based on the structural similarity index and structural content, with the k-nearest neighbors algorithm, Hough transform, and density-based spatial clustering of applications with noise-based environment extraction method. When comparing the methods, our proposed method offers the advantage of extracting a more accurate environment without requiring parameter adjustments, even when the environment is changed.

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“…Many papers in the literature address the moving target recognition through FMCW radars [9], [10], [25]- [28] supporting machine learning (ML) algorithms, such as support vector machines (SVMs) or Deep Neural Networks (DNNs) [26], [29], [30]. The use of DNNs in FMCW radar target classification has also been reported in [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

On the Edge Recurrent Neural Network Approach for Ground Moving FMCW Radar Target Classification

Gianoglio,

Rizik,

Tavanti

et al. 2024

IEEE Trans. Consumer Electron.

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In this paper, an approach for ground-moving target classification with an FMCW radar is proposed. In particular, data are collected using a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi device for data acquisition and processing. An FFT-based processing scheme is then applied to obtain a sequence of range-Doppler maps, which are provided in input to different convolutional neural network (CNN) architectures for classifying the targets (cars, motorcycles, or pedestrians) eventually passing in front of the radar. Specifically, two approaches have been followed and compared. In the first one, single range-Doppler maps are processed alone using a convolutional neural network, and then a voting mechanism is applied to select the target classes. In the second approach, a sequence of range-Doppler maps is processed using a time-distributed layer feeding a recurrent neural network. The CNNs are deployed on the Raspberry Pi providing the target classification on a low-cost embedded device. The obtained results show that the proposed approaches allow for effectively detecting the different types of targets running on an embedded device in less than one second.

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A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Cited by 4 publications

References 37 publications

Shadow competition: its definition, prevalence, causes and measurement

Shadow competition: its definition, prevalence, causes and measurement

Instantaneous Extraction of Indoor Environment from Radar Sensor-Based Mapping

On the Edge Recurrent Neural Network Approach for Ground Moving FMCW Radar Target Classification

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