Measurements and analysis of multistatic and multimodal micro-Doppler signatures for automatic target classification

The purpose of this paper is to present an experimental trial carried out at the Defence Academy of the United Kingdom to measure simultaneous multistatic and multimodal micro-Doppler signatures of various targets, including humans and flying UAVs. ewline Signatures were gathered using a network of sensors consisting of a CW monostatic radar operating at 10 GHz (X-band) and an ultrasound radar with a monostatic and a bistatic channel operating at 45 kHz and 35 kHz, respectively. A preliminary analysis of automatic target classification performance and a comparison with the radar monostatic case is also presented

Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

An algorithm for UWB radar-based human detection

This paper presents an algorithm for human presence detection in urban environments using an ultra-wide-band (UWB) impulse-based mono-static radar. A specular multi-path model (SMPM) is used to characterize human body scattered UWB waveforms. The SMPM parameters are used within a classical likelihood ratio detector framework to detect the presence of humans via gait, with the aid of a multi-target tracking technique (MTT). Experimental results on a simple human gait detection problem in an outdoor urban environment are presented to illustrate and validate the approach

Radar simulation of human activities in non line-of-sight environments

textThe capability to detect, track and monitor human activities behind building walls and other non-line-of-sight environments is an important component of security and surveillance operations. Over the years, both ultrawideband and Doppler based radar techniques have been researched and developed for tracking humans behind walls. In particular, Doppler radars capture some interesting features of the human radar returns called microDopplers that arise from the dynamic movements of the different body parts. All the current research efforts have focused on building hardware sensors with very specific capabilities. This dissertation focuses on developing a physics based Doppler radar simulator to generate the dynamic signatures of complex human motions in nonline-of-sight environments. The simulation model incorporates dynamic human motion, electromagnetic scattering mechanisms, channel propagation effects and radar sensor parameters. Detailed, feature-by-feature analyses of the resulting radar signatures are carried out to enhance our fundamental understanding of human sensing using radar. First, a methodology for simulating the radar returns from complex human motions in free space is presented. For this purpose, computer animation data from motion capture technologies are exploited to describe the human movements. Next, a fast, simple, primitive-based electromagnetic model is used to simulate the human body. The microDopplers of several human motions such as walking, running, crawling and jumping are generated by integrating the animation models of humans with the electromagnetic model of the human body. Next, a methodology for generating the microDoppler radar signatures of humans moving behind walls is presented. This involves combining wall propagation functions derived from the finite-difference time-domain (FDTD) simulation with the free space radar simulations of humans. The resulting hybrid simulator of the human and wall is used to investigate the effects of both homogeneous and inhomogeneous walls on human microDopplers. The results are further corroborated by basic point-scatterer analysis of different wall effects. The wall studies are followed by an analysis of the effects of flat grounds on human radar signatures. The ground effect is modeled using the method of images and a ground reflection coefficient. A suitable Doppler radar testbed is developed in the laboratory for simulation validation. Measured data of different human activities are collected in both line-of-sight and through-wall environments and the resulting microDoppler signatures are compared with the simulation results. The human microDopplers are best observed in the joint timefrequency space. Hence, suitable joint time-frequency transforms are investigated for improving the display and the readability of both simulated and measured spectrograms. Finally, two new Doppler radar paradigms are considered. First, a scenario is considered where multiple, spatially distributed Doppler radars are used to measure the microDopplers of a moving human from different viewing angles. The possibility of using these microDoppler data for estimating the positions of different point scatterers on the human body is investigated. Second, a scenario is considered where multiple Doppler radars are collocated in a two-dimensional (2-D) array configuration. The possibility of generating frontal images of human movements using joint Doppler and 2-D spatial beamforming is considered. The performance of this concept is compared with that of conventional 2-D array processing without Doppler processing.Electrical and Computer Engineerin

Human activity classification using micro-Doppler signatures and ranging techniques

PhD ThesisHuman activity recognition is emerging as a very import research area due to its potential applications in surveillance, assisted living, and military operations. Various sensors including accelerometers, RFID, and cameras, have been applied to achieve automatic human activity recognition. Wearable sensor-based techniques have been well explored. However, some studies have shown that many users are more disinclined to use wearable sensors and also may forget to carry them. Consequently, research in this area started to apply contactless sensing techniques to achieve human activity recognition unobtrusively. In this research, two methods were investigated for human activity recognition, one method is radar-based and the other is using LiDAR (Light Detection and Ranging). Compared to other techniques, Doppler radar and LiDAR have several advantages including all-weather and all-day capabilities, non-contact and nonintrusive features. Doppler radar also has strong penetration to walls, clothes, trees, etc. LiDAR can capture accurate (centimetre-level) locations of targets in real-time. These characteristics make methods based on Doppler radar and LiDAR superior to other techniques. Firstly, this research measured micro-Doppler signatures of different human activities indoors and outdoors using Doppler radars. Micro-Doppler signatures are presented in the frequency domain to reflect different frequency shifts resulted from different components of a moving target. One of the major differences of this research in relation to other relevant research is that a simple pulsed radar system of very low-power was used. The outdoor experiments were performed in places of heavy clutter (grass, trees, uneven terrains), and confusers including animals and drones, were also considered in the experiments. Novel usages of machine learning techniques were implemented to perform subject classification, human activity classification, people counting, and coarse-grained localisation by classifying the micro-Doppler signatures. For the feature extraction of the micro-Doppler signatures, this research proposed the use of a two-directional twodimensional principal component analysis (2D2PCA). The results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers were greatly improved. A Convolutional Neural Network (CNN) was built for the target classifications of type, number, activity, and coarse localisation. The CNN model obtained very high classification accuracies (97% to 100%) for the outdoor experiments, which were superior to the results obtained by SVM and kNN. The indoor experiments measured several daily activities with the focus on dietary activities (eating and drinking). An overall classification rate of 92.8% was obtained in activity recognition in a kitchen scenario using the CNN. Most importantly, in nearly real-time, the proposed approach successfully recognized human activities in more than 89% of the time. This research also investigated the effects on the classification performance of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used; providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications. Secondly, this research used a two dimensional (2D) LiDAR to perform human activity detection indoors. LiDAR is a popular surveying method that has been widely used in localisation, navigation, and mapping. This research proposed the use of a 2D LiDAR to perform multiple people activity recognition by classifying their trajectories. Points collected by the LiDAR were clustered and classified into human and non-human classes. For the human class, the Kalman filter was used to track their trajectories, and the trajectories were further segmented and labelled with their corresponding activities. Spatial transformation was used for trajectory augmentation in order to overcome the problem of unbalanced classes and boost the performance of human activity recognition. Finally, a Long Short-term Memory (LSTM) network and a (Temporal Convolutional Network) TCN was built to classify the trajectory samples into fifteen activity classes. The TCN achieved the best result of 99.49% overall accuracy. In comparison, the proposed TCN slightly outperforms the LSTM. Both of them outperform hidden Markov Model (HMM), dynamic time warping (DTW), and SVM with a wide margin