Enhanced Detection of Doppler-Spread Targets for FMCW Radar

Zhang, Wei; Li, Huiyong; Sun, Guohao; He, Zishu

doi:10.1109/taes.2019.2925433

Cited by 27 publications

(8 citation statements)

References 26 publications

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“…Γ 1 is hyperbolic tangent and Γ sigmoid function. Both activation functions are used to improve non-linearity of the network and can each be expressed by (7) and (8).…”

Section: Pedestrian Detection Using Lstm Neural Networkmentioning

confidence: 99%

“…On the other hand, Pp is described as the probability of that following a positive detection, the subject will be within the line of sight of the vehicle. Each of these parameters is expressed by (7)(8)(9), where TP is true positive, TN is true negative, FP is false positives and FN is false negative classification [25].…”

Section:   X Xmentioning

confidence: 99%

“…These ensure that the vehicle would detect pedestrians in blind spots and perform evasive maneuvres when required; whether through warning systems [3] or emergency braking mechanisms [4]. Thus far, numerous sensing approaches have been tested which include computer vision [5], laser scanner [6] and automotive radar [7] technologies. Each of these methods has unique capabilities that enable the vehicle to detect pending collisions with pedestrians [8].…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian detection using Doppler radar and LSTM neural network

Azizi

Noh

Pasya

et al. 2020

IJ-AI

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<span lang="EN-US">Integration of radar systems as primary sensor with deep learning algorithms in driver assist systems is still limited. Its implementation would greatly help in continuous monitoring of visual blind spots from incoming pedestrians. Hence, this study proposes a single-input single-output based Doppler radar and long short-term memory (LSTM) neural network for pedestrian detection. The radar is placed in monostatic configuration at an angle of 45 degree from line of sight. Continuous wave with frequency of 1.9 GHz are continuously transmitted from the antenna. The returning signal from the approaching subjects is characterized by the branching peaks higher than the transmitted frequency. A total of 1108 spectrum traces with Doppler shifts characteristics is acquired from eight volunteers. Another 1108 spectrum traces without Doppler shifts are used for control purposes. The traces are then fed to LSTM neural network for training, validation and testing. Generally, the proposed method was able to detect pedestrian with 88.9% accuracy for training and 87.3% accuracy for testing.</span>

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“…Γ 1 is hyperbolic tangent and Γ sigmoid function. Both activation functions are used to improve non-linearity of the network and can each be expressed by (7) and (8).…”

Section: Pedestrian Detection Using Lstm Neural Networkmentioning

confidence: 99%

Section:   X Xmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pedestrian detection using Doppler radar and LSTM neural network

Azizi

Noh

Pasya

et al. 2020

IJ-AI

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“…However, this method is not legally permitted outside of the exhibition situation and specific time points. The radar method [2,3] detects the object by extracting information from the received signal by radiating radio waves, but the resolution is not better than other methods. The radio wave signal detection [4,5] finds both the drone and the manipulator by receiving the radio wave used by the drone without emitting radio waves like the radar and acquiring the frequency used for communication with the drone controller.…”

Section: Introductionmentioning

confidence: 99%

A Study on Distance Measurement Module for Driving Vehicle Velocity Estimation in Multi-Lanes Using Drones

Lee

2021

Applied Sciences

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A method of estimating driving vehicle information usually uses a speed gun and a fixed speed camera. Estimating vehicle information using the speed gun has a high risk of traffic accidents by the operator and the fixed speed camera is not efficient in terms of installation cost and maintenance. The existing driving vehicle information estimation method can only measure each lane’s driving vehicle information, so it is impossible to measure multi-lanes simultaneously with a single measuring device. This study develops a distance measurement module that can acquire driving vehicle information in multi-lanes simultaneously with a single system using a drone. The distance measurement module is composed of two LiDAR sensors to detect the driving vehicle in one lane. The drone is located above the edge of the road and each LiDAR sensor emits the front/rear point of the road measuring point to detect the driving vehicle. The driving vehicle velocity is estimated by detecting the driving vehicle’s detection distance and transit time through radiation, with the drone LiDAR sensor placed at two measurement points on the road. The drone LiDAR sensor radiates two measuring points on the road and estimates the velocity based on driving vehicle’s detection distance and driving time. As an experiment, the velocity accuracy of the drone driving vehicle is compared with the speed gun measurement. The vehicle velocity RMSE for the first and second lanes using drones is 0.75 km/h and 1.3 km/h, respectively. The drone and the speed gun’s average error probabilities are 1.2% and 2.05% in the first and second lanes, respectively. The developed drone is more efficient than existing driving vehicle measurement equipment because it can acquire information on the driving vehicle in a dark environment and a person’s safety.

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“…The sample matrix is processed by a 2D fast Fourier transform, and finally, a 2D range-Doppler matrix (RDM) is obtained for each receive antenna. Based on the RDM, a 2D constant false alarm rate procedure is implemented [6].…”

mentioning

confidence: 99%

Approach of 2D direction of arrival estimation of FMCW traffic radar by utilising 1D array

Zhang

et al. 2020

Electron. lett.

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The frequency-modulated continuous wave (FMCW) radar will play a more important role in future intelligent traffic systems due to low cost and high adaptability to complex environments. Generally, a 2D array is utilised to measure range, speed and azimuth and elevation angles (4D) for multiple vehicles simultaneously. However, a traffic radar utilising a 2D array will enhance its system complexity; therefore, the increased cost would make it less superior to LiDAR. In this Letter, an azimuth and elevation angles estimation method of FMCW radar utilising only 1D array is proposed. Thus, it can provide 4D parameters to multiple vehicles simultaneously. The feasibility of the proposed approach is verified by on-road measurement data.

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Enhanced Detection of Doppler-Spread Targets for FMCW Radar

Cited by 27 publications

References 26 publications

Pedestrian detection using Doppler radar and LSTM neural network

Pedestrian detection using Doppler radar and LSTM neural network

A Study on Distance Measurement Module for Driving Vehicle Velocity Estimation in Multi-Lanes Using Drones

Approach of 2D direction of arrival estimation of FMCW traffic radar by utilising 1D array

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