Below is a select list of published paper by researchers using Ancortek SDR products

 

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2019 –

 

 

“Radar Data Cube Processing For Human Activity Recognition Using Multi Subspace Learning”

Baris Erol and Moeness G. Amin

IEEE Transactions on Aerospace and Electronic Systems, DOI 10.1109/TAES.2019.2910980, 2019

 

Abstract: In recent years, radar has been employed as a fall detector due to its effective sensing capabilities and penetration through walls. In this paper, we introduce a multilinear subspace human activity recognition scheme that exploits the three radar signal variables: slow-time, fast-time, and Doppler frequency. The proposed approach attempts to find the optimum subspaces that minimize the reconstruction error for different modes of the radar data cube. A comprehensive analysis of the optimization considerations is performed, such as initialization, number of projections, and convergence of the algorithms. Finally, a boost-ing scheme is proposed combining the unsupervised multilinear principal component analysis (MPCA) with the supervised methods of linear discriminant analysis (LDA) and shallow neural networks (SNN). Experimental results based on real radar data obtained from multiple subjects, different locations, and aspect angles (0◦, 30◦, 45◦, 60◦, 90◦) demonstrate that the proposed algorithm yields the highest overall classification accuracy among spectrogram-based methods including pre-defined physical features, one and two dimensional PCA and convolutional neural networks (CNNs).

 

“Interferometric Angular Velocity Measurement of Rotating Blades: theoretical analysis, modeling and simulation study”

Xiangrong Wang, Pengcheng Wang, Xianbin Cao and Victor C. Chen

IET Radar, Sonar & Navigation, Vol.13, No. 3, pp. 438-444, 2019

 

Abstract:  Doppler radar can only measure the radial velocity of a moving object. If an object is moving along a curved path, when its radial velocity decreases, the angular velocity must increase. Thus, if the angular velocity can be measured, the problem caused by little or no radial velocity can be solved. In this paper, we provide detailed theoretical analysis and establish the mathematical model of the interferometric frequency shifts of rotating blades. We first analyze the instantaneous frequency of a SINC function, which comprises a pair of sinusoidals and a train of strong spectrum lines. Then, we utilize the convolution theory in time-frequency domain to calculate the interferometric frequency shifts of rotating blades. Simulation results manifest that some important parameters and features of rotating blades, such as blade length, rotating rate and blade number, can be accurately estimated from the time-varying interferometric frequency signatures.

 

“Detection of Gait Asymmetry Using Indoor Doppler Radar”

Ann-Kathrin Seifert, Abdelhak M. Zoubir and Moeness G. Amin

2019 IEEE Radar Conference, paper no. 5240, April 2019

 

Abstract:  Doppler radar systems enable unobtrusive and privacy-preserving long-term monitoring of human motions indoors. In particular, a person’s gait can provide important information about their state of health. Utilizing micro-Doppler signatures, we show that radar is capable of detecting small differences between the step motions of the two legs, which results in asymmetric gait. Image-based and physical features are extracted from the radar return signals of several individuals, including four persons with different diagnosed gait disorders. It is shown that gait asymmetry is correctly detected with high probability, irrespective of the underlying pathology, for at least one motion direction.

 

“Generalized PCA Fusion for Improved Radar Human Motion Recognition”

Baris Erol and Moeness Amin

2019 IEEE Radar Conference, paper no. 5233, April 2019

 

Abstract:  Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis. Different human motion articulations present themselves more vividly in certain joint-variables data domains, most notably, time-frequency (TF) and range vs slow time. In this paper, we present a human motion data-driven classifier that utilizes both domains through a feature fusion approach. With data in each domain considered as an image, the features are extracted from lower dimension projections. These projections recognize the correlations across each image dimension, and are pursued using the generalized principal component analysis (GPCA). It is shown, through the confusion matrices, that feature fusion provides improved classification performance of human daily activities over the case where only the features of either domain are considered.

 

“Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes”

Moeness Amin, Zhengxin Zeng and Tao Shan

2019 IEEE Radar Conference, paper no. 5323, April 2019

 

Abstract:  We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.

 

“Incremental L1-Norm Linear Discriminant Analysis for Indoor Human Activity Classification”

Sivan Zlotnikov, Panos P. Markopoulos and Fauzia Ahmad

2019 IEEE Radar Conference, paper no. 5492, April 2019

 

Abstract:  In this paper, we present an incremental version of L1-norm Linear Discriminant Analysis (L1-LDA) for radar-based indoor human activity classification. Incremental L1-LDA enables refinement of the discriminant basis as more training samples become available during operation. At the same time, it permits adaptation to the specific activity patterns of the human subject of interest, different than the ones on which the original discriminant basis was trained. The incremental version retains the robustness of L1-LDA to outliers among the training data. Using Doppler signatures of various indoor human activities, we demonstrate that the proposed method exhibits enhanced performance over the incremental counterpart of standard linear discriminant analysis when the training data are corrupted and similar performance under nominal training data.

 

“Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features”

Matthew Ritchie and Aaron M. Jones

2019 IEEE Radar Conference, paper no. 5299, April 2019

 

Abstract:  This paper presents micro-Doppler analysis and classification results from radar measurements of various hand gestures. A new database of 6 individuals completing 4 separate gestures with over 3,000 repetitions was recorded using a 24 GHz Ancortek radar system. The micro-Doppler signatures from these gestures were generated, features extracted and multiple different classifiers applied to this gesture data. A typical micro-Doppler classification process aims to use either a single range bin of data, average over a series of range bins or align all the target signal to a single bin. Different to previous techniques, the paper presents a method that uses multiple ranges bins to produce a spectrogram per range bin in order to represent the observed gesture over all four dimensions of time, Doppler, space and polarization. A comparison of the traditional and the newly proposed technique is shown and the improvements demonstrated are observed to be significant.

 

“GAN-based Synthetic Radar Micro-Doppler Augmentations for Improved Human Activity Recognition”

Baris Erol, Sevgi Z. Gurbuz, and Moeness G. Amin

2019 IEEE Radar Conference, paper no. 5325, April 2019

 

Abstract:  Deep neural networks (DNNs), and, in particular, convolutional neural networks (CNNs), have recently received much attention in a wide range of research areas, including radar-based human activity recognition. However, obtaining a large training dataset still remains a challenging task due to the costs and resources required for data collections. In this paper, we present a method for extending adversarial learning to the generation of synthetic radar time-frequency (TF) domain signatures which provides the ability to adapt to different operational environments. The classification results achieved with a deep CNN trained on generated images prove the efficiency of proposed algorithm over the state of the art methods.

 

“Dynamic Hand Gesture Classification Based on Multistatic Radar Micro-Doppler Signatures Using Convolutional Neural Network”

Zhaoxi Chen, Gang Li, Francesco Fioranelli and Hugh Griffiths

2019 IEEE Radar Conference, paper no. 5178, April 2019

 

Abstract:  We propose a novel convolutional neural network (CNN) for dynamic hand gesture classification based on multistatic radar micro-Doppler signatures. The time-frequency spectrograms of micro-Doppler signatures at all the receiver antennas are adopted as the input to CNN, where data fusion of different receivers is carried out at an adjustable position. The optimal fusion position that achieves the highest classification accuracy is determined by a series of experiments. Experimental results on measured data show that 1) the accuracy of classification using multistatic radar is significantly higher than monostatic radar, and that 2) fusion at the middle of CNN achieves the best classification accuracy.

 

“Simultaneous Measurement of Radial and Transversal Velocities Using a Dual-Frequency Interferometric Radar”

Xiangrong Wang, Pengcheng Wang and Victor C. Chen

2019 IEEE Radar Conference, paper no. 5203, April 2019

 

Abstract:  The linear velocity of an arbitrary moving object comprises two orthogonal components with reference to the observing radar, those are radial velocity and transversal velocity. The interferometric radar is capable of providing the complete 2-Dvelocityinformationofanarbitrarymovingobjectregardless of the trajectory. However, the radial velocities and transversal velocities are coupled in the interferometric measurement when there are multiple moving objects in the radar field of view, thus the conventional interferometric radar fails to measure the transversal velocities. In this paper, we propose a new method based on the dual-frequency interferometric radar, which can transmit high-frequency waveforms for radial velocity measurement and low-frequency waveforms for suppressing the inter-correlation terms among different objects, thus providing the transversal velocity measurement for each separate object. Simulation results are provided to validate the effectiveness of the proposed method.

 

“Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar”

Martins Ezuma, Ozdemir, Chethan Kumar Anjinappa, Wahab Ali Gulzar and Ismail Guvenc

2019 IEEE Radio and Wireless Symposium (RWS), DOI: 10.1109/RWS.2019.8714203

 

Abstract:  Millimeter wave radars are popularly used in last-mile radar-based defense systems. Detection of low altitude airborne target using these radars at low-grazing angles is an important problem in the field of electronic warfare, which becomes challenging due to the significant effects of clutters in the terrain. This paper provides both experimental and analytical investigation of micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low grazing angle, surface-sited 24 GHz dual polarized frequency modulated continuous wave (FMCW) radar. The radar backscatter signal from the UAV is polluted by land clutters which is modeled using a uniform Weibull distribution. A constant false alarm rate (CFAR) detector which employs adaptive thresholding is designed to detect the UAV in the rich clutter background. In order to further enhance the discrimination of the UAV from the clutter, the micro-Doppler signature of the rotating propellers and bulk trajectory of the UAV are extracted and plotted in the time-frequency domain.

 

“Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures”

Ann-Kathrin Seifert and Moeness G. Amin

IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2019.2893528, 2019

 

Abstract:  In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this work is concerned with solving the intra motion category classification problem of gait recognition.  Methods: New gait classification approaches utilizing physical features, subspace features and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.

 

2018 –

 

“Fall Detection Using Deep Learning in Range-Doppler Radars”

Jokanovic and M. Amin

IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 1, pp. 180–189, 2018

 

Abstract: In this paper, we propose an approach that uses deep learning to detect a human fall. The proposed approach automatically captures the intricate properties of the radar returns. In order to minimize false alarms, we fuse information from both the time-frequency and range domains. Experimental data is used to demonstrate the superiority of the deep learning-based approach in comparison with the principal component analysis method and those methods incorporating predefined physically interpreted features.

 

 

“A New Railyard Safety Approach for Detection and Tracking of Personnel and Dynamic Objects Using Software-Defined Radar”

Subharthi Banerjee, Jose Santos, Michael Hempel and Hamid Sharif

Proceedings of 2018 Joint Rail Conference (JRC2018-6239)

 

Abstract: In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. 

 

“Radar Classification of Human Gait Abnormality Based on Sum-of-Harmonics Analysis”

A.-K. Seifert, A. M. Zoubir, and M. G. Amin

In Radar Conference (RadarConf18), Oklahoma, OK, April 2018

 

Abstract:  Radar technology for monitoring of human gait has recently gained interest in the fields of home security, medical diagnosis, assisted living and rehabilitation. Due to its remote, reliable and privacy-preserving sensing, radar is promising to become an effective tool for medical gait analysis. We show the influences of gait abnormalities and assistive walking devices on the joint-variable representations of the back-scattered radar signals. Using both, parametric and non-parametric techniques, we extract gait features to classify normal, abnormal and cane-assisted gait. In particular, the fundamental frequency of the time-frequency behavior is estimated using sum-of-harmonics modeling in order to characterize different gaits. Results obtained using experimental K-band radar data are presented for the person-specific and person-generic case.

 

“Subspace Classification of Human Gait using Radar Micro-Doppler Signatures”

A.-K. Seifert, L. Sch¨afer, M. G. Amin, and A. M. Zoubir

In 26th Eur. Signal Process. Conf. (EUSIPCO), Rome, Italy, September 2018

 

Abstract:  Radar-based monitoring of human gait has become of increased interest with applications to security, sports biomechanics, and assisted living. Radar sensing offers contactless monitoring of human gait. It protects privacy and preserves a person’s right to anonymity. Considering normal, pathological and assisted gait, we demonstrate the effectiveness of radar in discriminating different walking styles. By use of unsupervised feature extraction methods utilizing principal component analysis, we examine five gait classes using two different joint-variable signal representations, i.e., the spectrogram and the cadence-velocity diagram. Results obtained with experimental K-band radar data show that the choice of signal domain and adequate pre-processing are crucial for achieving high classification rates for all gait classes.

 

“Sparsity-Driven Micro Doppler Feature Extraction for Dynamic Hand Gesture Recognition”

Li, R. Zhang, M. Ritchie, and H. Griffiths

IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 2, pp. 655–665, 2018

 

Abstract: In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensors. First, sparse representations of the echoes reflected from dynamic hand gestures are achieved through the Gaussian-windowed Fourier dictionary. Second, the micro-Doppler features of dynamic hand gestures are extracted using the orthogonal matching pursuit algorithm. Finally, the nearest neighbor classifier is combined with the modified Hausdorff distance to recognize dynamic hand gestures based on the sparse micro-Doppler features. Experiments with real radar data show that the recognition accuracy produced by the proposed method exceeds 96% under moderate noise, and the proposed method.

 

 

“Effect of Sparsity-Aware Time–Frequency Analysis on Dynamic Hand Gesture Classification with Radar Micro-Doppler Signatures”

Gang Li, Shimeng Zhang, Francesco Fioranelli and Hugh Griffiths

IET Radar, Sonar & Navigation, Vol. 12, No. 8, pp. 815-820, 2018

 

Abstract:  Dynamic hand gesture recognition is of great importance in human–computer interaction. In this study, the authors investigate the effect of sparsity-driven time–frequency analysis on hand gesture classification. The time–frequency spectrogram is first obtained by sparsity-driven time–frequency analysis. Then three empirical micro-Doppler features are extracted from the time–frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time–frequency analysis techniques, sparsity-driven time–frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.

 

 

“Drone Detection and Tracking Based on Phase-Interferometric Doppler Radar”

Michael Jian, Zhenzhong Lu and Victor C. Chen

2019 IEEE Radar Conference, pp. 1146-1149, April 2018

 

Abstract:  In this paper, drone detection and tracking based on phase-interferometry are investigated. Data collected by a simple dual-channel Doppler radar is used for implementing the joint range-Doppler-azimuth processing. Experimental results show that micro drones can be detected and tracked by applying the joint range-Doppler-azimuth processing. Features extracted from the range-Doppler-azimuth domain can be used to identify drones.

 

2017 –

 

“Experimental Operation of Drone Micro-SAR with Efficient Time-Varying Velocity Compensation”

W.K. Lee and K.W. Lee

Electronics Letters, Vol. 53, No. 10, pp. 682-683, 2017

 

Abstract:  Drone platform for synthetic aperture radar (SAR) operation has been little publicised due to the technical constraint of the payload implementation. Multi-rotor drone based SAR is distinguished from the conventional airborne system by the increased sensitivity to turbulences and poor motion stability. Extremely limited power and mass budgets may prevent the use of extra hardware for motion compensation and the difficulty of SAR focusing is aggravated. Feasibility of micro-SAR drone operation is investigated through field tests, where experimental SAR images are acquired over ground targets. Attempts have been made to implement near-real-time compensation for non-uniform motion disturbance. Finally, experimental drone SAR operation is validated through calibrated SAR images.

 

 

“Feature Diversity for Fall Detection and Human Indoor Activities Classification Using Radar Systems”

A.Shrestha, J. Le Kernec, F. Fioranelli, E. Clippitelli, E. Gambi and S. Spinsante

2017 International Conference on Radar Systems, IET Publisher, Belfast, UK, 2017

 

Abstract:  This paper presents preliminary analysis of radar signatures for fall detection and classification of human indoor activities, to monitor the daily behaviour of individuals at risk of deteriorating physical or cognitive health. Two datasets of signatures in different environments have been collected, one of which included signatures generated from signals simultaneously collected from a radar and an RGB-D Kinect sensor, on a couple of older individuals. This preliminary analysis shows the potential effectiveness of different features and classifiers, and highlights the need of additional investigation to characterise and exploit the diversity of features and classification methods, in different experimental scenarios with different subjects.

 

“Gait Analysis of Horses for Lameness Detection with Radar Sensors”

A. Shrestha, J. Le Kernec, F. Fioranelli, J.F.Marshall and L. Voute

2017 International Conference on Radar Systems, IET Publisher, Belfast, UK, 2017

 

Abstract:  This paper presents the preliminary investigation of the use of radar signatures to detect and assess lameness of horses and its severity. Radar sensors in this context can provide attractive contactless sensing capabilities, as a complementary or alternative technology to the current techniques for lameness assessment using video-graphics and inertial sensors attached to the horses’ body. The paper presents several examples of experimental data collected at the Weipers Centre Equine Hospital at the University of Glasgow, showing the micro-Doppler signatures of horses and initial results of their analysis.

 

“Hand Gesture Classification Using 24GHz FMCW Dual Polarized Radar”

Ritchie, A. Jones, J. Brown, and H.D. Griffiths

2017 International Conference on Radar Systems, IET Publisher, Belfast, UK, 2017

 

Abstract:  This paper evaluates the classification performance of a dual polarised on receive, 24 GHz Frequency Modulated Continuous Wave (FMCW) radar system to autonomously identify micro-Doppler signatures of unique hand gestures. We employ an Eigen subspace feature selection technique on the calculated signal subspace in order to classify each gesture. Measurements using the dual polarised radar, permitting simultaneous recording of both the co-pol and cross-pol returns, are evaluated with this processing technique and results are reported herein. Our analysis displays the challenges presented by the high variance in individual gestures and the limited additional information the cross polarised returns have provided to the classifier. Classification performance comparisons are presented when co, cross and dual polarised data are provided to the classifier. With this technique we achieve autonomous classificationperformanceofupto84.6%whenEigenvaluederived features are used for classification.

 

“Joint Fall and Aspect Angle Recognition Using Fine-Grained Micro-Doppler Classification”

Qingchao Chen, Matthew Ritchie, Yang Liu, Kevin Chetty and Karl Woodbridge

2017 IEEE Radar Conference, pp. 0912-0916

 

Abstract:  Activity recognition and monitoring are finding important applications in ambient assisted living healthcare.  Among the various types of motions which researchers are attempting to detect and recognize, fall detection has gained significant interest. In this paper, we investigate the application of high-frequency (24 GHz) FMCW radar for multi-perspective micro-Doppler (µ-D) activity recognition. Data from two different types of motion; falling and picking-up an object, were collected from three aspect angles and put through a fine-grained classifier to not only differentiate the motions, but to also identify their aspect towards the radar receivers. A key novel component of this work is the application of the fine-grained classification task, where a label discriminate sparse representation classifier is proposed to improve recognition performance over very similar µ-D signatures. This is achieved by learning a discriminate dictionary constrained by the label information and meanwhile preventing the overfitting problem. The greatest increase in classification performance was found to be of the order of 8 %.

 

“Bistatic Human Micro-Doppler Signatures for Classification of Indoor Activities”

Francesco Fioranelli, Matthew Ritchie, and Hugh Griffiths

2017 IEEE Radar Conference, pp. 0610-0615

 

Abstract:  This paper presents the analysis of human micro-Doppler signatures collected by a bistatic radar system to classify different indoor activities. Tools for automatic classification of different activities will enable the implementation and deployment of systems for monitoring life patterns of people and identifying fall events or anomalies which may be related to early signs of deteriorating physical health or cognitive capabilities. The preliminary results presented here show that the information within the micro-Doppler signatures can be successfully exploited for automatic classification, with accuracy up to 98%, and that the multi-perspective view on the target provided by bistatic data can contribute to enhance the overall system performance.

 

“Microwave Interferometric and Doppler Radar Measurements of a UAV”

Jeffrey A. Nanzer and Victor C. Chen

2017 IEEE Radar Conference, pp. 1628-1633

 

Abstract:  The first dual-mode measurements of the time varying radial and angular velocity signatures of a UAV quadcopter are presented. Measured with a compact 24 GHz interferometric radar, the signatures are measured at various observation angles relative to the UAV. It is shown that the signatures from the UAV at high grazing angles, when the radial velocity of the rotor blades relative to the radar is low, provide features related to the rotation rate of the rotor blades in certain instances. Using both Doppler and interferometric radar velocity measurements may therefore provide a method of detecting and classifying UAVs.

 

“Multiple Joint-Variable Domains Recognition of Human Motion”

Branka Jokanovic, Moeness Amin and Baris Erol

2017 IEEE Radar Conference, pp. 0948-0952

 

Abstract:  Radar has been successfully employed for classifying human motions in defense, security and civilian applications, and has emerged to potentially become a technology of choice in the healthcare industry, specifically in what pertains to assisted living. Due to the relationship between Doppler frequency and motion kinematics, the time-frequency domain has been traditionally used to analyze radar signals of human gross-motor activities. Towards improving motion classification, this paper incorporates three domains, namely, time-frequency, time-range, and range-Doppler domains. Features from each domain are extracted using deep neural network that is based on stacked auto-encoders. Final decision is made by combining the classification outcomes. Experimental results demonstrate that certain domains are more favorable than others in recognizing specific motion articulations, thus reinforcing the merits of multi-domain motion classifications.

 

“Radar-Based Human Gait Recognition in Cane-Assisted Walks”

Ann-Kathrin Seifert, Abdelhak M. Zoubir, and Moeness G. Amin

2017 IEEE Radar Conference, pp. 1428-1433

 

Abstract:  Radar technology for in-home gait assessment has recently become of increased interest due to its safety, reliability and privacy-preserving, non-wearable sensing mode. Radar-based micro-Doppler signatures of humans can be used to reveal key characteristics that capture changes in gait and enable detecting abnormalities. In elderly care, the influence of assistive walking devices on gait time-frequency signal characteristics has to be examined for proper diagnoses, assessment of rehabilitations, and fall risk predictions. In this paper, we demonstrate the effects of assistive walking devices, such as a cane, on the back-scattered radar signals. A K-band radar is used to discriminate between assisted and unassisted walks. Detection of walking aids and gait recognition are performed based on features obtained from the cadence-velocity diagram. Experimental data are presented for walking towards and away from radar, delineating different micro-Doppler signatures which are attributed to distinctions in upper and lower leg kinematics in both cases.

 

“Range-Doppler Radar Sensor Fusion for Fall Detection”

Baris Erol, Moeness G. Amin and Boualem Boashash

2017 IEEE Radar Conference, pp. 0819-0824

 

Abstract:  Falls are the major cause of accidents in the elderly population. Propelled by their non-intrusive sensing capabilities and robustness to heat and lighting conditions, radar-based automated fall detection systems have emerged as a candidate technology for reliable fall detection in assisted living. The use of a multiple radar system, in lieu of a single radar unit, for indoor monitoring combats occlusion and supported by the fact that motion articulations in the directions away from the line of sight generate weak Doppler signatures that are difficult to detect and classify. Fusion of the data from two radars is deemed to improve performance and reduce false alarms. Utilizing two 24GHz ultrawide band (UWB) radar sensing systems, we present different fusion architectures and sensor selection methods, demonstrating the merits of two-sensor platform for indoor motion monitoring and elderly care applications.

 

“Sparsity-Based Dynamic Hand Gesture Recognition Using Micro-Doppler Signatures”

Gang Li, Rui Zhang, Matthew Ritchie and Hugh Griffiths

2017 IEEE Radar Conference, pp. 0928-0931

 

Abstract:  In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensor. The sparse representation of the radar signal in the time-frequency domain is achieved through the Gabor dictionary, and then the micro-Doppler features are extracted by using the orthogonal matching pursuit (OMP) algorithm and fed into classifiers for dynamic hand gesture recognition. The proposed method is validated with real data measured with a K-band radar. Experiment results show that the proposed method outperforms the principal component analysis (PCA) algorithm, with the recognition accuracy higher than 90%.

 

“Experimental Study on Radar Micro-oppler Signatures of Unmanned Aerial Vehicles”

Michael Jian, Zhenzhong Lu and Victor C. Chen

2017 IEEE Radar Conference, pp. 0854-0857

 

Abstract:  In the paper, radar micro-Doppler signatures of rotating rotors are investigated for detection and identification of small UAVs. A 24 GHz dual-receiving channel interferometric radar is used to capture useful features of rotating rotors. Interferometric radar with two receiving channels can measure both radial velocity and angular velocity induced micro-Doppler modulations. The study found the angular micro-Doppler signature is a good complementary feature to the radial induced one for identifying small UAVs.

 

“Indoor Monitoring Human Movements Using Dual-Receiver Radar”

Baokun Liu, Michael Jian, Zhenzhong Lu and Rachel Chen

2017 IEEE Radar Conference, pp. 0520-0523

 

Abstract:  A K-band experimental dual-receiver radar is used for real-time indoor monitoring human movements. In this paper, we introduce the architecture of the dual-receiver radar and its specifications for indoor monitoring. We describe its design concept, advantage, and special considerations for best indoor monitoring human movements. The basic signal processing methods and algorithms, such as range-Doppler-angle-of-arrival processing, digital beamforming, range-velocity mapping, and moving target indication are discussed. Examples of interesting experimental results for monitoring multiple people are also presented and analyzed.

 

“Detection, Localization and Tracking of Unauthorized UAS and Jammers”

Ismail Guvenc, Ozgur Ozdemir, Yavuz Yapici, Hani Mehrpouyan and David Matolak

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

 

Abstract:  Small unmanned aircraft systems (UASs) are expected to take major roles in future smart cities, for example, by delivering goods and merchandise, potentially serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber attacks to infrastructure, private/public property, and people. Even for legitimate use-cases of small UASs, air traffic management (ATM) for UASs becomes of critical importance for maintaining safe and collusion-free operation. Therefore, various ways to detect, track, and interdict potentially unauthorized drones carries critical importance for surveillance and ATM applications. In this paper, we will review techniques that rely on ambient radio frequency signals (emitted from UASs), radars, acoustic sensors, and computer vision techniques for detection of malicious UASs. We will present some early experimental and simulation results on radar-based range estimation of UASs, and receding horizon tracking of UASs. Subsequently, we will overview common techniques that are considered for interdiction of UASs.

 

“Flight-Test Evaluation of Small Form-Factor LiDAR and Radar Sensors for sUAS Detect-and-Avoid Applications”

Maarten Uijt de Haag, Chris G. Bartone, Michael S. Braasch

2017 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)

 

Abstract:  Despite well over a decade of intensive research and development efforts, detect-and-avoid (DAA) technology remains in an immature state for medium and large unmanned aerial systems (UAS) and is in its very infancy for small UAS (sUAS).  Routine Beyond Visual Line-of-Sight (BVLOS) operations will not be achieved until this technological impasse has been surpassed.  Although a multi-system/multi-sensor approach is known to be the robust solution, sUAS platforms are challenged to host such an equipment suite in addition to their revenue-generating payload for commercial applications. Recent developments in small formfactor LiDAR and radar sensors may prove to be vital components in the overall DAA solution for sUAS.  These types of sensors are being developed primarily for the autonomous ground vehicle market, but may be adapted for UAS applications.  This paper documents a series of ground and flight tests conducted to evaluate the performance of both a small form-factor LiDAR and radar sensors.  Obstacle detection range versus obstacle size is determined for both sensors in static and dynamic flight modes.