DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar

Cao, Zhihui; Fang, Wenwei; Song, Yuying; He, Lai; Song, Chunyi; Xu, Zhiwei

doi:10.1109/tgrs.2021.3113302

Cited by 14 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OS-CFAR has advantages in distinguishing close targets, but introduces a slightly increased false alarm rate and additional computational costs. More sophisticated CFAR variants are summarised in [128], but are rarely used in automotive applications. Deep learning methods can be used to improve noise estimation [129] and peak classification [128] in CFAR.…”

Section: Classical Detection Pipelinementioning

confidence: 99%

“…More sophisticated CFAR variants are summarised in [128], but are rarely used in automotive applications. Deep learning methods can be used to improve noise estimation [129] and peak classification [128] in CFAR. Clustering is the most important stage in the radar detection pipeline, especially for the next-generation high-resolution radar [130].…”

Section: Classical Detection Pipelinementioning

confidence: 99%

See 1 more Smart Citation

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

show abstract

Section: Classical Detection Pipelinementioning

confidence: 99%

Section: Classical Detection Pipelinementioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, convolutional neural networks (CNNs) [ 7 , 8 ] or U-shaped neural networks (i.e., U-nets) [ 9 , 10 ] were used to detect targets on the range–velocity plane. Recently, deep learning techniques to replace the CFAR algorithm in the automotive MIMO FMCW radar system were also introduced in [ 11 , 12 ]. A U-net-based target detector was proposed in [ 11 ] for detecting a vulnerable road user on the range-angle (RA) map.…”

Section: Introductionmentioning

confidence: 99%

“…A U-net-based target detector was proposed in [ 11 ] for detecting a vulnerable road user on the range-angle (RA) map. In addition, the authors in [ 12 ] compensated for the disadvantages of the conventional CFAR algorithm by replacing the peak detection step of the CFAR algorithm with the deep neural network.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System

Kang

Jang

Lee

2022

Sensors

View full text Add to dashboard Cite

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.

show abstract

“…Under the condition of strong interference, it is difficult for traditional radar of detect the target. In this case, the target can only be detected by utilizing better target detection techniques such as constant false alarm rate (CFAR) [1,2] technology. Compared with traditional radars, multiple-input multiple-output (MIMO) radar [3,4] have stronger antijamming ability, higher measurement accuracy and better resolution [5].…”

Section: Introductionmentioning

confidence: 99%

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

Wang

Shi³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments.

show abstract

DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar

Cited by 14 publications

References 54 publications

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

Contact Info

Product

Resources

About