Extraction of Human Limbs Based on Micro-Doppler-Range Trajectories Using Wideband Interferometric Radar

He, Xianxian; Zhang, Yunhua; Dong, Xiao

doi:10.3390/s23177544

Cited by 3 publications

(3 citation statements)

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“…For the precession frequency extraction of the micro-motion cone, the empirical mode decomposition (EMD) method already exists for detection and estimation [ 6 ]. Therefore, the precession frequency ω and LOS angle

can be regarded as known quantities.…”

Section: Simulation Verification Of Conical Micro-motion Parameter Ex...mentioning

confidence: 99%

“…The structure parameters of the precession cone, the micro-motion parameters, and the lidar parameters set in the simulation verification are shown in Table 2. For the precession frequency extraction of the micro-motion cone, the empirical mode decomposition (EMD) method already exists for detection and estimation [6]. Therefore, the precession frequency ω and LOS angle α can be regarded as known quantities.…”

Section: Simulation Verification Of Conical Micro-motion Parameter Ex...mentioning

confidence: 99%

“…At present, high-resolution lidar measurement technology plays an important role in fields such as ocean exploration [ 1 ] and atmospheric environment monitoring [ 2 ]. Laser micro-Doppler technology, as one of the high-resolution measurement methods, has been widely used in various fields such as vital sign detection [ 3 , 4 , 5 , 6 ], drone detection [ 7 , 8 ], automotive radar detection [ 9 ], vibration measurement [ 10 ], and bird recognition [ 11 ]. When a radar emits electromagnetic waves onto the surface of a moving object, the echo frequency will undergo a Doppler shift compared to the emitting signal.…”

Section: Introductionmentioning

confidence: 99%

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Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar

Yang,

Liu

et al. 2024

Sensors

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Micro-Doppler time–frequency analysis has been regarded as an important parameter extraction method for conical micro-motion objects. However, the micro-Doppler effect caused by micro-motion can modulate the frequency of lidar echo, leading to coupling between structure and micro-motion parameters. Therefore, it is difficult to extract parameters for micro-motion cones. We propose a new method for parameter extraction by combining the range profile of a micro-motion cone and the micro-Doppler time–frequency spectrum. This method can effectively decouple and accurately extract the structure and the micro-motion parameters of cones. Compared with traditional time–frequency analysis methods, the accuracy of parameter extraction is higher, and the information is richer. Firstly, the range profile of the micro-motion cone was obtained by using an FMCW (Frequency Modulated Continuous Wave) lidar based on simulation. Secondly, quantitative analysis was conducted on the edge features of the range profile and the micro-Doppler time–frequency spectrum. Finally, the parameters of the micro-motion cone were extracted based on the proposed decoupling parameter extraction method. The results show that our method can effectively extract the cone height, the base radius, the precession angle, the spin frequency, and the gravity center height within the range of a lidar LOS (line of sight) angle from 20° to 65°. The average absolute percentage error can reach below 10%. The method proposed in this paper not only enriches the detection information regarding micro-motion cones, but also improves the accuracy of parameter extraction and establishes a foundation for classification and recognition. It provides a new technical approach for laser micro-Doppler detection in accurate recognition.

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can be regarded as known quantities.…”

Section: Simulation Verification Of Conical Micro-motion Parameter Ex...mentioning

confidence: 99%

Section: Simulation Verification Of Conical Micro-motion Parameter Ex...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar

Yang,

Liu

et al. 2024

Sensors

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Gait‐based human recognition based on millimetre wave multiple input multiple output radar point cloud constructed using velocity‐depth‐time

He,

Zhang,

Dong

2024

IET Radar Sonar & Navi

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Gait recognition is to recognise different individuals based on their faint differences of gait characteristics, which is different from and more challengeable than the recognition of human activities based on relatively bigger differences between different motions. Existing millimetre‐wave Multiple Input Multiple Output radar point cloud data contains time‐varying three‐dimensional spatial positions, velocity, and intensity information. How to enhance the accuracy of gait recognition by effectively utilising the available radar point cloud data has become an attractive research topic in recent years. A velocity‐depth‐time (VDT) based point cloud construction method for millimetre‐wave Multiple Input Multiple Output radar is proposed for gait recognition application, which can not only alleviate the sparsity problem of mmWave point cloud but also make the constructed point cloud to exhibit temporal structural features of micro‐motions, and therefore enable the successful application of PointNet++ to mmWave‐MIMO point cloud gait recognition. New point clouds are constructed by the proposed method using public gait recognition datasets of 10 and 20 individuals from mmWave‐MIMO radar, which are used to conduct gait recognition experiments using PointNet++. The results show that the point clouds constructed based on VDT are more conducive to the gait recognition task. Even using the classic PointNet++ model, which is not specially designed for radar point clouds, high recognition accuracy can be achieved for gait recognition tasks. The recognition accuracies are improved by 11% and 12% in this work for datasets of 10 and 20 individuals, respectively, compared with the 84% and 80% achieved by the traditional method using the same dataset and the same PointNet++ model, while the accuracies are improved by 5% and 12%, respectively, compared with the 90% and 80% achieved by the original dataset thesis method corresponding to 10‐individual and 20‐individual datasets.

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Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

Viadero-Monasterio,

Alonso-Rentería,

Pérez-Oria

et al. 2024

Vehicles

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The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

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Extraction of Human Limbs Based on Micro-Doppler-Range Trajectories Using Wideband Interferometric Radar

Cited by 3 publications

References 42 publications

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar

Decoupling and Parameter Extraction Methods for Conical Micro-Motion Object Based on FMCW Lidar

Gait‐based human recognition based on millimetre wave multiple input multiple output radar point cloud constructed using velocity‐depth‐time

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

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