Design of Hypervelocity-Impact Damage Evaluation Technique Based on Bayesian Classifier of Transient Temperature Attributes

With the rapid increasement of space debris on earth orbit, the hypervelocity-impact (HVI) of space debris can cause some serious damages to the spacecraft, which can affect the operation security and reliability of spacecraft. Therefore, the damage detection of the spacecrafts has become an urgent problem to be solved. In this paper, a method is proposed to detect the damage of spacecraft. Firstly, a variable-interval method is proposed to extract the effective information from the infrared image sequence. Secondly, in order to mine the physical meaning of the thermal image sequence, five attributes are used to construct a feature space. After that, a Naive Bayesian classifier is established to mine the information of different damaged areas. Then, a maximum interclass distance function is used choose the representative of each class. Finally, in order to visualize damaged areas, the Canny operator is used to extract the edge of the damage. In the experiment, ground tests are used to simulate hypervelocity impacts in space. Historical data of natural damaged material and artificial damaged material are used to build different classifiers. After that, the effective of classifiers is illustrated by accuracy, F-score and AUC. Then, two different types of materials are detected by proposed method, Independent Component Analysis (ICA) and Fuzzy C-means (FCM). The results show that the proposed method is more accurate than other methods

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

Air Force Institute of Technology Research Report 1999

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 and Engineering Management, Operational Sciences, and Engineering Physics

Air Force Institute of Technology Research Report 2004

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 and Engineering Management, Operational Sciences, and Engineering Physics

Detecting and Tracking Vulnerable Road Users\u27 Trajectories Using Different Types of Sensors Fusion

Vulnerable road user (VRU) detection and tracking has been a key challenge in transportation research. Different types of sensors such as the camera, LiDAR, and inertial measurement units (IMUs) have been used for this purpose. For detection and tracking with the camera, it is necessary to perform calibration to obtain correct GPS trajectories. This method is often tedious and necessitates accurate ground truth data. Moreover, if the camera performs any pan-tilt-zoom function, it is usually necessary to recalibrate the camera. In this thesis, we propose camera calibration using an auxiliary sensor: ultra-wideband (UWB). USBs are capable of tracking a road user with ten-centimeter-level accuracy. Once a VRU with a UWB traverses in the camera view, the UWB GPS data is fused with the camera to perform real-time calibration. As the experimental results in this thesis have shown, the camera is able to output better trajectories after calibration. It is expected that the use of UWB is needed only once to fuse the data and determine the correct trajectories at the same intersection and location of the camera. All other trajectories collected by the camera can be corrected using the same adjustment. In addition, data analysis was conducted to evaluate the performance of the UWB sensors. This study also predicted pedestrian trajectories using data fused by the UWB and smartphone sensors. UWB GPS coordinates are very accurate although it lacks other sensor parameters such as accelerometer, gyroscope, etc. The smartphone data have been used in this scenario to augment the UWB data. The two datasets were merged on the basis of the closest timestamp. The resulting dataset has precise latitude and longitude from UWB as well as the accelerometer, gyroscope, and speed data from smartphones making the fused dataset accurate and rich in terms of parameters. The fused dataset was then used to predict the GPS coordinates of pedestrians and scooters using LSTM

Novel robust computer vision algorithms for micro autonomous systems

People detection and tracking are an essential component of many autonomous platforms, interactive systems and intelligent vehicles used in various search and rescues operations and similar humanitarian applications. Currently, researchers are focusing on the use of vision sensors such as cameras due to their advantages over other sensor types. Cameras are information rich, relatively inexpensive and easily available. Additionally, 3D information is obtained from stereo vision, or by triangulating over several frames in monocular configurations. Another method to obtain 3D data is by using RGB-D sensors (e.g. Kinect) that provide both image and depth data. This method is becoming more attractive over the past few years due to its affordable price and availability for researchers. The aim of this research was to find robust multi-target detection and tracking algorithms for Micro Autonomous Systems (MAS) that incorporate the use of the RGB-D sensor. Contributions include the discovery of novel robust computer vision algorithms. It proposed a new framework for human body detection, from video file, to detect a single person adapted from Viola and Jones framework. The 2D Multi Targets Detection and Tracking (MTDT) algorithm applied the Gaussian Mixture Model (GMM) to reduce noise in the pre-processing stage. Blob analysis was used to detect targets, and Kalman filter was used to track targets. The 3D MTDT extends beyond 2D with the use of depth data from the RGB-D sensor in the pre-processing stage. Bayesian model was employed to provide multiple cues. It includes detection of the upper body, face, skin colour, motion and shape. Kalman filter proved for speed and robustness of the track management. Simultaneous Localisation and Mapping (SLAM) fusing with 3D information was investigated. The new framework introduced front end and back end processing. The front end consists of localisation steps, post refinement and loop closing system. The back-end focus on the post-graph optimisation to eliminate errors.The proposed computer vision algorithms proved for better speed and robustness. The frameworks produced impressive results. New algorithms can be used to improve performances in real time applications including surveillance, vision navigation, environmental perception and vision-based control system on MAS

Air Force Institute of Technology Research Report 2012

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 and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

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

APPLYING MACHINE LEARNING FOR COP/CTP DATA FILTERING

Student Thesis (NPS NRP Project Related)Accurate tracks and targeting are key to providing decision-makers with the confidence to execute their missions. Increasingly, multiple intelligence, surveillance, and reconnaissance (ISR) assets across different intelligence sources are being used to increase the accuracy of track location, resulting in the need to develop methods to exploit heterogeneous sensor data streams for better target state estimation. One of the algorithms commonly used for target state estimation is the Kalman Filter (KF) algorithm. This algorithm performs well if its covariance matrices are accurate approximations of the uncertainty in sensor measurements. Our research complements the artificial intelligence/machine learning (AI/ML) efforts the U.S. Navy is conducting by quantitatively assessing the potential of using an ML model to predict sensor measurement noise for KF state estimation. We used a computer simulation to generate sensor tracks of a single target and trained a neural network to predict sensor error. The hybrid model (ML-KF) was able to outperform our baseline KF model that uses normalized sensor errors by approximately 20% in target position estimation. Further research in enhancing the ML model with external environment variables as inputs could potentially create an adaptive state estimation system that is capable of operating in varied environment settings.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Outstanding ThesisCaptain, Singapore ArmyApproved for public release. Distribution is unlimited