2017 IEEE Radar Conference

Around-the-Corner Radar: Detection and Localization of a Target in Non-Line of Sight

Khac-Phuc-Hung Thai, Olivier Rabaste, Jonathan Bosse, Dominique Poullin, Israel Hinostroza, Thierry Letertre and Thierry Chonavel

Abstract—This paper examines the problem of detecting and locating an NLOS target in an urban environment by exploiting multipath radar returns. We propose a detection-localization algorithm based on a matched subspace filter approach that works in the target state space directly. A real experimentation was carried out to show that a portable radar can provide images of multipath returns in NLOS cases that can be clearly interpreted thanks to a simple ray tracing model. The application of the detection-localization algorithm on experimental radar measurements shows that the target can be detected and located but that the mitigation of strong ambiguities inherent to the multipath detection-localization problem remains a challenging problem for the application at hand.

Bistatic human micro-Doppler signatures for classification of indoor activities

Francesco Fioranelli, Matthew Ritchie, Hugh Griffiths

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.

Experimental Study on Radar Micro-Doppler Signatures Of Unmanned Aerial Vehicles

Michael Jian, Zhenzhong Lu, and Victor C. Chen

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

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.

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

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 %.

Microwave Interferometric and Doppler Radar Measurements of a UAV

Jeffrey A. Nanzer and Victor C. Chen

Abstract—The first dual-mode measurements of the time- varying radial and angular velocity signatures of a UAV quad- copter are presented. Measured with a compact 24 GHz interfer- ometric 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

Abstract—Radar has been successfully employed for classify- ing 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 tradition- ally 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, Moeness G. Amin

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

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 24 GHz ultra- wide 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.

Real-Time Capable Micro-Doppler Signature Decomposition of Walking Human Limbs

Sherif Abdulatif, Fady Aziz, Bernhard Kleiner, Urs Schneider

Abstract—Unique micro-Doppler signature (μ-D) of a human body motion can be analyzed as the superposition of different body parts μ-D signatures. Extraction of human limbs μ-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate μ-D signatures of a walking human. Furthermore, a novel limbs μ-D signature time independent decomposition feasibility study is presented based on features as μ-D signatures and range profiles also known as micro- Range (μ-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose μ-D signatures of limbs from a walking human signature on real- time basis.

Sparsity-based Dynamic Hand Gesture Recognition Using Micro-Doppler Signatures

Gang Li, Rui Zhang, Matthew Ritchie, Hugh Griffiths

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%.

2016 IEEE Digital Avionics Systems Conference

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

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 form- factor 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.

IET Radar, Sonar & Navigation

Multi-aspect micro-Doppler signatures for attitude-independent L/N quotient estimation and its application to helicopter classification

Rui Zhang, Gang Li1, Carmine Clemente, John J. Soraghan

Abstract: Micro-Doppler signals returned from the main rotor of a helicopter can be used for feature extraction and helicopter classification. An intrinsic feature of a helicopter that may be extracted from the micro-Doppler signatures is the L/N quotient, where N denotes the number of rotor blades and L is the blade length. However, in monostatic radar, the L/N quotient cannot be accurately estimated due to the unknown attitude angles of non-cooperative helicopters. To solve this problem, an attitude- independent L/N quotient estimation method based on multi-aspect micro-Doppler signatures is proposed in this study. The helicopter is observed from different aspect angles, and the multi-aspect micro-Doppler signatures are jointly processed to solve the attitude angles of the helicopter and estimate the L/N quotient unambiguously. Experiments with both simulated and real data demonstrate that, the proposed method is robust with respect to the attitude of the helicopter and, therefore, significantly improves the accuracy of L/N quotient estimation compared with only using the signature observed from single-aspect angle. This implies that the proposed method has the potential to increase the success rate of helicopter classification.