Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Jiang, Wen; Ren, Yihui; Liu, Ying; Leng, Jiaxu

doi:10.3390/electronics11010156

Cited by 29 publications

(18 citation statements)

References 108 publications

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“…The authors in Ref. [4] described a review of the applications of deep learning in the field of radar target detection and summarised the possible limitations. The results obtained in Ref.…”

Section: Introductionmentioning

confidence: 99%

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An improved radar clutter suppression by simple neural network

Perďoch,

Gažovová,

Pacek

2023

IET Radar Sonar & Navi

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The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets.

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Section: Introductionmentioning

confidence: 99%

“…The results obtained in Ref. [4] have shown that deep learning-based detectors perform better than traditional processing methods to a certain degree in some specific cases. The study of deep learning in the field of radar target detection is at the initial stage and still faces some challenges.…”

Section: Introductionmentioning

confidence: 99%

An improved radar clutter suppression by simple neural network

Perďoch,

Gažovová,

Pacek

2023

IET Radar Sonar & Navi

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“…This paper focuses on the quality of the bounding boxes in annotated dataset for 2D object detection using camera data. Moreover the considerations and conclusions drawn in this paper can be easily expanded to 2D and 3D object detection based on different sensors, such as LiDAR [8] and RADAR [9]. To understand the importance of labels, one can imagine to work on a image classification task with vehicles.…”

Section: Introductionmentioning

confidence: 99%

Testing ground-truth errors in an automotive dataset for a DNN-based object detector

Baris

Chan

et al. 2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Given the promising advances in the field of Assisted and Automated Driving, it is expected that the roads of the future will be populated by vehicles driven by computers, partially or fully replacing human drivers. In this scenario, the first stage of the perception-decision-actuation pipeline will likely rely on Deep Neural Networks for understanding the scene around the vehicle. Typical tasks for Deep Neural Networks are object detection and instance segmentation, tasks relying on supervised learning and annotated datasets. As one can imagine, the quality of the labelled dataset strongly affects the performance of the network, and this aspect is investigated in this paper. Annotation quality should be a primary concern in safety-critical tasks, such as Assisted and Automated Driving. This work addresses and classifies some of the mistakes found in a popular automotive dataset. Moreover, some experiments with a Deep Neural Network model were performed to test the effect of these mistakes on network predictions. A set of criteria was established to support the relabelling of the testing dataset which was compared to the original dataset.

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“…An alternative approach for detection can be established via implementation of the algorithms based on artificial neural networks (ANN) in radar-processing units. Such algorithms as shown in [32,33] may potentially provide a higher level of overall performance. However, they require a training step to be properly conducted.…”

Section: Introductionmentioning

confidence: 99%

Stepped-Frequency Continuous-Wave Signal Processing Method for Human Detection Using Radars for Sensing Rooms through the Wall

et al. 2022

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The problem of detecting moving and stationary people in a room with a specialized radar system sensing through the wall is considered in the paper. The high-range resolution of the system is achieved by effective processing of reflected ultra-wideband stepped-frequency continuous-wave signals (SFCW). The paper presents a new method which is based on normalization of complex-valued samples of the received SFCW signals and extends traditional processing steps including quadrature-phase demodulation, sampling and inverse discrete Fourier transform. The proposed method is aimed at improving the performance of the interperiodic difference and variance of sample algorithms which are briefly described in relation to the SFCW radar system. The computer modeling showed that the introduced normalization mitigates the background noise and merely decreases the artifacts commonly appearing in radar images due to the non-uniform amplitude-frequency characteristics of the radar circuits. The described algorithms were implemented in a software part of the real-time working prototype of the radar system designed and assembled at the University research center. The results of field experiments confirmed the advantage of the proposed method in typical scenarios and showed the increase of the signal-to-noise ratio to 5 dB compared to traditional radar algorithm-processing SFCW signals.

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Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Cited by 29 publications

References 108 publications

An improved radar clutter suppression by simple neural network

An improved radar clutter suppression by simple neural network

Testing ground-truth errors in an automotive dataset for a DNN-based object detector

Stepped-Frequency Continuous-Wave Signal Processing Method for Human Detection Using Radars for Sensing Rooms through the Wall

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