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SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition

Abstract: This work presents a simulation framework to generate human micro-Dopplers in WiFi based passive radar scenarios, wherein we simulate IEEE 802.11g complaint WiFi transmissions using MATLAB's WLAN toolbox and human animation models derived from a marker-based motion capture system. We integrate WiFi transmission signals with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics, and the sensor parameters. In this paper, we consider five human activities. We uniformly benchmark the classification performance of multiple machine learning and deep learning models against a common dataset. Further, we validate the classification performance using the real radar data captured simultaneously with the motion capture system. We present experimental results using simulations and measurements demonstrating good classification accuracy of $\geq$ 95\% and $\approx$ 90\%, respectively.

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Topics: Passive radar (53%), Motion capture (53%), Radar (53%)
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7 results found


Open accessProceedings ArticleDOI: 10.1109/ICCWORKSHOPS50388.2021.9473900
Shelly Vishwakarma1, Chong Tang1, Wenda Li1, Karl Woodbridge1  +2 moreInstitutions (2)
14 Jun 2021-
Abstract: This work considers the use of a passive WiFi radar (PWR) to monitor human activities. Real-time uncooperative monitoring of people has numerous applications ranging from smart cities and transport to IoT and security. In e-healthcare, PWR technology could be used for ambient assisted living and early detection of chronic health conditions. Large training datasets could drive forward machine-learning-focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Therefore, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data at multiple IEEE WiFi standards(IEEE 802.11g, n, and ad). We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured signatures. To create a more realistic training dataset, we artificially add noise to our clean simulated spectrograms. A noise distribution is directly learned from real radar measurements using a Generative Adversarial Network (GAN). We observe improvements in the classification performances between 3 to 8%. Our results suggest that simulation data can be used to make adequate training data when the available measurement training support is low.

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Topics: Radar (58%), Noise measurement (52%), Noise (52%)

3 Citations


Open accessPosted Content
Chong Tang, Wenda Li, Shelly Vishwakarma, Karl Woodbridge1  +2 moreInstitutions (1)
Abstract: Micro-Doppler analysis has become increasingly popular in recent years owning to the ability of the technique to enhance classification strategies. Applications include recognising everyday human activities, distinguishing drone from birds, and identifying different types of vehicles. However, noisy time-frequency spectrograms can significantly affect the performance of the classifier and must be tackled using appropriate denoising algorithms. In recent years, deep learning algorithms have spawned many deep neural network-based denoising algorithms. For these methods, noise modelling is the most important part and is used to assist in training. In this paper, we decompose the problem and propose a novel denoising scheme: first, a Generative Adversarial Network (GAN) is used to learn the noise distribution and correlation from the real-world environment; then, a simulator is used to generate clean Micro-Doppler spectrograms; finally, the generated noise and clean simulation data are combined as the training data to train a Convolutional Neural Network (CNN) denoiser. In experiments, we qualitatively and quantitatively analyzed this procedure on both simulation and measurement data. Besides, the idea of learning from natural noise can be applied well to other existing frameworks and demonstrate greater performance than other noise models.

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Topics: Noise (58%), Deep learning (56%), Noise reduction (54%) ... show more

3 Citations


Journal ArticleDOI: 10.1109/JSEN.2021.3057592
Shuoguang Wang1, Qiang An2, Shiyong Li1, Guoqiang Zhao1  +1 moreInstitutions (2)
Abstract: Through-wall detection and recognition of human motions via radar is of great benefit to public security and emergency service applications The micro-Doppler signatures extracted from the targets of interest in motion typically contain distinct inner-individual motion features, which is the key to human identification and motion classification However, no research so far considered a very common application scenario, where the conductive wires buried in the wall are in a powering on mode, let alone study its potential effect on the collected signatures of motion behind wall As it should be anticipated, strong interference components would be brought in the obtained micro-Doppler signatures, and the subsequent motion recognition would be severely affected In this paper, we, for the first time, report the effect of the buried live wire on the micro-Doppler signatures Specifically, a micro-Doppler signature enhancement method, named range-max time-frequency representation (R-max TFR) is utilized to obtain feature enhanced micro-Doppler signatures of behind wall human motions And to mitigate the clutter components introduced by the buried live wire, the effect is first modeled as an impulse response with its center located at a fixed frequency instance in the R-max TFR map Then, a novel technique based on conditional Generative Adversarial Network (cGAN), is proposed to fulfill the goal Both numerical and experimental results, as well as comparisons with other classical de-clutter methods, demonstrate the effectiveness and superiority of the proposed de-wiring cGAN framework in suppressing the wiring effect in behind wall micro-Doppler signatures

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1 Citations


Open accessPosted Content
Abstract: This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications.

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Open accessProceedings ArticleDOI: 10.1109/RADARCONF2147009.2021.9455278
07 May 2021-
Abstract: This work investigates the degradation effects of four distinct jamming signal styles on human micro-Doppler signatures by examining the ability of a linear discriminant classifier to accurately distinguish signatures collected using a simulated frequency modulated continuous wave (FMCW) radar which have been injected with jamming. Misclassification dependence on jamming signal power for each jamming style is presented along with the nature of misclassifications.

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Topics: Jamming (62%)

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36 results found


Open accessPosted Content
Abstract: Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). The SqueezeNet architecture is available for download here: this https URL

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4,584 Citations


Journal ArticleDOI: 10.1109/TAES.2006.1603402
Abstract: When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

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Topics: Doppler radar (71%), Continuous-wave radar (66%), Pulse-Doppler radar (65%) ... show more

1,115 Citations


Journal ArticleDOI: 10.1007/BF01901021
Abstract: Presents a human walking model built from experimental data based on a wide range of normalized velocities. The model is structured on two levels. On the first level, global spatial and temporal characteristics are generated. On the second level, a set of parameterized trajectories produce both the position of the body in space and the internal body configuration. This is performed for a standard structure and an average configuration of the human body. The experimental context corresponding to the model is extended by allowing a continuous variation of global spatial and temporal parameters according to the motion rendition expected by the animator. The model is based on a simple kinematic approach designed to keep the intrinsic dynamic characteristics of the experimental model. Such an approach also allows a personification of the walking action in an interactive real-time context in most cases. A correction automata of such motion is then proposed

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Topics: Inverse kinematics (53%), Kinematics (53%), Context (language use) (50%)

476 Citations


Open accessJournal ArticleDOI: 10.1109/72.822516
Abstract: In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive.

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Topics: Sequential minimal optimization (60%), Support vector machine (60%), Iterative method (54%) ... show more

382 Citations


Open accessBook
30 Dec 2010-
Abstract: This highly practical resource provides you with thorough working knowledge of the micro-Doppler effect in radar, including its principles, applications and implementation with MATLAB codes. The book presents code for simulating radar backscattering from targets with various motions, generating micro-Doppler signatures, and analyzing the characteristics of targets. You find detailed descriptions of the physics and mathematics of the Doppler and micro-Doppler effect. Moreover, you learn how to derive rigid and non-rigid body motion induced micro-Doppler effect in radar scattering. The book provides a wide range of clear examples, including an oscillating pendulum, a spinning and precession heavy top, rotating rotor blades of a helicopter, rotating wind-turbine blades, a person walking with swinging arms and legs, a flying bird, and movements of quadruped animals. DVD Included: Contains time-saving MATLAB source codes for simulation, radar data processing, and micro-Doppler signature analysis to help you with your challenging projects in the field.

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Topics: Radar (60%)

341 Citations


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20217