A Fast and Compact Deep Gabor Network for Micro-Doppler Signal Processing and Human Motion Classification

Le, Hoang Thanh; Phung, Son Lam; Bouzerdoum, A.

doi:10.1109/jsen.2021.3106300

Cited by 12 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…for 0 ≤ k < N s , 0 ≤ l < N c , and 0 ≤ i < N R . Then, we can observe that for the given values of k and l, z k,l [i] is a finite-duration discrete-time complex exponential signal with the discrete-time angular frequency ω θ defined in (37). Similar to the discussions for the range-DFT and Doppler-DFT, a third DFT can now be applied to the finite-duration discrete-time sequence z k,l [i] to extract the AOA.…”

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

“…For unambiguous measurement of the angle θ, the angular frequency is limited to the range −π ≤ ω < π, or equivalently, −N/2 ≤ η < N/2, assuming N is an even number as in the case where FFT is used. Therefore, based on the angular frequency-angle relation in (37), the index axis of Z k,l [η] can be converted to the angle unit with…”

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

“…With zero padding, the angular frequency resolution of the DFT Z k,l [η] is still determined by N R , that is, ω res = 2π/N R , since zero padding does not improve the frequency resolution of DFT [19]. Because of the nonlinear relation between ω θ and θ in (37), it is more complicated to derive the angular resolution than the velocity resolution in (29). To derive the angular resolution, we define…”

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

“…in addition to that in (35), where ω θ is defined (37) and N R is the number of the receiver antennas. For the ease of presentation, define a row vector based on (39) for the 2-Tx-4-Rx TDM MIMO configuration (i.e., N T = 2 and N R = 4) as shown in Fig.…”

Section: Tdm Mimo Radar For Improved Angular Resolution a Tdm Mimo Radarmentioning

confidence: 99%

See 3 more Smart Citations

Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

Li¹,

Wang

Yang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this tutorial paper, we systematically present the fundamental operating principles and analytical details of the discrete Fourier transform based signal processing techniques for the TDM MIMO FMCW millimeter-wave (mmWave) automotive radars. The mmWave radars provide a key sensing capability to support safety features of the conventional and autonomous vehicles. Automotive radar sensors are used to detect presence and location of the objects of interest to derive comprehensive and accurate knowledge of road conditions and surrounding environments. Automotive radars are subjected to an increasing demand to provide high-resolution measurements in the range-Doppler-azimuth-elevation domains. Therefore, the current state-of-the-art automotive radars commonly employ MIMO technologies, resulting in a large block of multidimensional data to process in real time through a long chain of signal processing algorithms. Detailed coverage on the fundamental radar signal processing techniques are scattered in a large body of literature for classical radar systems. Currently, there is no tutorial available that covers the technical details of the latest TDM MIMO FMCW radar technology, making it extremely hard for new researchers in the field of automotive mmWave radars. This paper contains sufficient technical details to serve as a tutorial. It can help lay a solid analytical foundation to facilitate exploration and development of advanced radar signal processing techniques for automotive applications. The algorithmic details and analytical results presented in this paper can be readily applied to both real-time implementation and postprocessing. Simulation and experimental results are presented in this paper to validate analytical derivations and to demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations.

show abstract

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

Section: Angle Processing a Angle-dftmentioning

confidence: 99%

Section: Tdm Mimo Radar For Improved Angular Resolution a Tdm Mimo Radarmentioning

confidence: 99%

See 2 more Smart Citations

Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

Li¹,

Wang

Yang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition, detailed gait analysis methods based on biomechanical approach using MDR have recently been developed [24,25]. In recent years, machine-learning-based approaches that use MDR data have been employed to recognize various types of motion [26,27]. In particular, MDR-based fall detection systems have been developed, and their effectiveness has been demonstrated in realistic situations [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults

Saho

Fujimoto

Kobayashi

et al. 2022

Sensors

View full text Add to dashboard Cite

In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data.

show abstract

An Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis

Li,

Wang,

Wiltos

et al. 2024

Mobile Netw Appl

View full text Add to dashboard Cite

The current proofreading algorithms for action regulation mainly recover the 3D structure and action information of non-rigid objects from image sequences by factorization. Most of algorithms assume that the camera model is an affine model. This assumption only holds if the size and depth of the object change very little relative to the distance from the object to the camera, which is in the case of fixed-shape basis. When the object is very close to the camera, this assumption causes a large reconstruction error. This paper solves this problem by the intelligent proofreading algorithms for remote skiing teaching actions based on variable shape basis. Firstly, the improved Retinex algorithm is used to enhance the multi-frame video images of skiing actions to make the action details more prominent. Then, measurement matrix is calculated after eliminating the translation vector by coordinate transformation. Under the condition of rank constraint, the measurement matrix is decomposed by singular value decomposition algorithm, and the correct shape basis structure of 3D action features can be obtained by using the variable shape basis. Finally, by randomly initializing a parameter, the optimized parameter and the least square algorithm are used to optimize the randomly initialized parameter further. The iteration until the convergence of the objective function can be used to calculate the deformation degree of the actions. The test results show that this algorithm improves the proofreading accuracy of action regulation in skiing teaching, and the proofreading results of various uploaded sliding actions are correct, which can be applied to remote skiing teaching and community learning.

show abstract

A Fast and Compact Deep Gabor Network for Micro-Doppler Signal Processing and Human Motion Classification

Cited by 12 publications

References 51 publications

Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults

An Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis

Contact Info

Product

Resources

About