Deflection characterisation of rotary systems using ground‐based radar

Ochieng, Francis Xavier; Jiang, Haoyang; Hancock, Craig M.; Roberts, Gethin Wyn; Kernec, Julien Le; Tang, Xu

doi:10.1049/joe.2019.0503

Cited by 2 publications

(3 citation statements)

References 18 publications

(23 reference statements)

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“…In this paper, in simulation we have used 2cm and 37.5cm in experiments obtaining over 99% accuracy in both cases. In [31], preliminary results on a novel implementation of the radar return simulations for Wind turbines shows richer information in the resulting spectrograms using a simulation that emulates radar pulses to obtain multidomain information. Furthermore, in [32], two radar systems a continuous wave (CW) radar operating in K-band and an FMCW radar operating in C-band are used for the classification of ten indoor human activities.…”

Section: Discussionmentioning

confidence: 99%

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

et al. 2018

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The accurate classification of activity patterns based on radar signatures is still an open problem and is key to detect anomalous behavior for security and health applications. This paper presents a novel iterative convolutional neural networks strategy with an autocorrelation pre-processing instead of the traditional micro-Doppler image pre-processing to classify activities or subjects accurately. The proposed strategy uses an iterative deep learning framework for the automatic definition and extraction of features. This is followed by a traditional supervised learning classifier to label the different activities. Using three human subjects and their real motion captured data, twelve thousand radar signatures were simulated by varying additive white Gaussian noise. Additionally, 6720 experimental radar signatures were captured with a frequency-modulated continuous radar at 5.8GHz with 400M Hz of instantaneous bandwidth from seven activities using one subject and 4800 signatures from five subjects while walking. The simulated and experimental data were both used to validate our proposed method. With SNR varying from −20 to 20dB with 88.74% average accuracy at −10dB and 100% peak accuracy at 15dB. The proposed Iterative Convolutional Neural Networks followed with Random Forests (ICNNRF) does not only outperform the feature-based methods using micro-Doppler images but also the classification methods using other types of supervised classifiers after our proposed iterative convolutional neural network.

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Section: Discussionmentioning

confidence: 99%

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

et al. 2018

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“…It was shown in [4] that the algorithm modelling full waves was more computationally intensive than the algorithm in [3]. However, it gave more details in simulated mD signatures, especially in acceleration and deceleration phases [4]. In [5], the authors also used mD signatures and benchmarked the classification performance as a function of aspect angle by comparing eight radar systems.…”

Section: Introductionmentioning

confidence: 99%

“…This is a ‘shortcut’ to reduce the computational load as opposed to simulating the full wave, but this means that the raw data and the range information cannot be exploited as they are not representative of the radar operation. It was shown in [4] that the algorithm modelling full waves was more computationally intensive than the algorithm in [3]. However, it gave more details in simulated mD signatures, especially in acceleration and deceleration phases [4].…”

Section: Introductionmentioning

confidence: 99%

4D radar simulator for human activity recognition

Zhou,

Le Kernec

2023

IET Radar Sonar & Navi

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Millimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances.

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Deflection characterisation of rotary systems using ground‐based radar

Cited by 2 publications

References 18 publications

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

4D radar simulator for human activity recognition

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