The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

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

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels