The ability to quickly, accurately, and autonomously identify and track objects in digital images in real-time has been an area of investigation for quite some time. Research in this area falls under the broader category of computer vision. Only in recent decades, with advances in computing power and commercial optical hardware, has this capability become a possibility. There are many different methods of identifying and tracking objects of interest, and best practices are still being developed, varying based on application. This thesis examines background subtraction methods as they apply to the tracking of small unmanned aerial systems (sUAS). A system combining commercial off-the-shelf (COTS) cameras and a pan-tilt unit (PTU), along with custom developed code, is developed for the purpose of continuously pointing at and tracking the motion of a sUAS in flight. Mixtures of Gaussians Background Modeling (MOGBM) is used to track the motion of the sUAS in frame and determine when to command the PTU. When the camera is moving, background subtraction methods are unusable, so additional methods are explored for filling this performance gap. The stereo vision capabilities of the system, enabled by the use of two cameras simultaneously, allow for estimation of the three-dimensional position and trajectory of the sUAS. This system can be used as a supplement or replacement to traditional tracking methods such as GPS and RADAR as part of a larger unmanned aerial systems traffic control (UTC) infrastructure. / Master of Science / The ability to quickly, accurately, and automatically identify and track targets in digital images has been of interest for some time now. Research in this area falls under the broader category of computer vision. Only in recent decades, with advances in computing power and commercial optical hardware, has this ability become a possibility. There are many different methods of identifying and tracking targets of interest, and best practices are still being developed, varying based on application. This thesis examines background subtraction methods as they apply to the tracking of small unmanned aerial systems (sUAS), commonly referred to as drones. A system combining cameras and a moving platform, along with custom developed code, is developed for the purpose of continuously pointing at and tracking the motion of an sUAS in flight. The system is able to map out the three-dimensional position of a flying sUAS over time.

In recent years previously restricted radio-frequency spectrum has been opened to civilian and industrial access in the United States. Because of this, high priority users such as the military and government need to develop systems that can adapt to the surrounding spectral environment which will suddenly be filled with new users. This thesis considers an environment with one tracking radar, a single target, and a communications system that can passively interfere with the radar system. Three separate agents, Sense and Avoid, Machine Learning, and "Optimal", are tasked with the channel selection problem for radar communications coexistence. Each agent is evaluated based on their ability to detect and avoid the interferer while also tracking a target accurately. In particular, in this thesis, we are interested in the value that machine learning algorithms can provide over and above simple approaches. This value is assessed based on the conflicting requirements of avoiding interference yet using as much of the spectrum for tracking as possible. / Master of Science / With a newfound dependence on wireless transmission, the demand for electromagnetic spectrum allocations has vastly increased. In recent years the Federal Communications Commission has auctioned some previously restricted access frequency bands to public and commercial applications. While this enables the growth of faster and more widespread civilian communications, military radar systems which had been the priority users of those bands are now at risk of interference from new users. Current radar systems typically occupy fixed bands and are not yet well adjusted to sharing their allocated spectrum with other users. Cognitive radar systems have been proposed to monitor airwaves for potential interferences and autonomously manage band allocation to avoid the interferers. In this thesis, we study a learning algorithm that enables a radar system to actively monitor and select its bandwidth to ensure proper target tracking. In particular, we are interested in the value this learning algorithm can provide over and above simple approaches. This value is assessed based on the conflicting requirements of avoiding interference yet using as much of the spectrum for tracking as possible.

The problem of reorienting a spacecraft to acquire a moving target is investigated. The spacecraft is modeled as a rigid body with N axisymmetric wheels controlled by axial torques, and the kinematics are represented by Modified Rodriques Parameters. The trajectory, denoted the reference trajectory, is one generated by a virtual spacecraft that is identical to the actual spacecraft. The open-loop reference attitude, angular velocity, and angular acceleration tracking commands are constructed so that the solar panel vector is perpendicular to the sun vector during the tracking maneuver. We develop a nonlinear feedback tracking control law, derived from Lyapunov stability and control theory, to provide the control torques for target tracking. The controller makes the body frame asymptotically track the reference motion when there are initial errors in the attitude and angular velocity. A spacecraft model, based on the X-ray Timing Explorer spacecraft, is used to demonstrate the effectiveness of the Lyapunov controller in tracking a given target. / Master of Science

In conventional tracking algorithms the targets are usually considered as point source objects. However, in realistic scenarios the point source assumption is often not suitable and estimating the states of an object extension characterized by a collectively moving ballistic object group (cluster) becomes a very critical and relevant problem which has applications in the defense area. Recently, a Bayesian approach to extended object tracking using random matrices has been proposed. Within this approach, ellipsoidal object extensions are modeled by random matrices and treated as additional state variables to be estimated. In this work we propose to use a slightly modified version of this new approach that simultaneously estimates the ellipsoidal shape and the kinematics of a group of ballistic targets. Target group that is tracked consists of subsequent projectiles. We use JPDAF framework together with the new approach to emphasize the pros and cons of both approaches. The methods are demonstrated and evaluated in detail by making various simulations.

International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada / Military aerospace test ranges are increasingly being called upon to conduct missions utilizing large numbers of participating units, or targets. Precision, position and trajectory data must be recorded on all participants. In addition, weapon/target engagements must be scored and real-time range safety considerations must be accommodated. This requires precision metric data be available in real-time on all participating targets. One solution to these problems, is utilization of multiple target tracking radars which incorporate electronic beam steering to quickly move from one target to another in sequence. This paper briefly recounts the history of range instrumentation radars, points out some of the advantages of using multi-target radars, and highlights the specifications and design of a multiple target instrumentation radar now being acquired by the U.S. Army for use at White Sands Missile Range and the Kwajalein Missile Range.

This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures.

Distributed target tracking (DTT) is desirable in wireless camera networks to achieve scalability and robustness to node or link failures. DTT estimates the target state via information exchange and fusion among cameras. This thesis proposes new DTT algorithms to handle five major challenges of DTT in wireless camera networks, namely non-linearity in the camera measurement model, temporary lack of measurements (benightedness) due to limited field of view, redundant information in the network, limited connectivity of the network due to limited communication ranges and asynchronous information caused by varying and unknown frame processing delays. The algorithms consist of two phases, namely estimation and fusion. In the estimation phase, the cameras process their captured frames, detect the target, and estimate the target state (location and velocity) and its uncertainty using the Extended Information Filter (EIF) that handles non-linearity. In the fusion phase, the cameras exchange their local target information with their communicative neighbours and fuse the information. The contributions of this thesis are as follows. The target states estimated by the EIFs undergo weighted fusion. The weights are chosen based on the estimated uncertainty (error covariance) and the number of nodes with redundant information such that the information of benighted nodes and the redundant information get lower weights. At each time step, only the cameras having the view of the target and the cameras that might have the view of the target in the next time step participate in the fusion (tracking). This reduces the energy consumption of the network. The algorithm selects the cameras dynamically by using a threshold on their shortest distances (in the communication graph) from the cameras having the view of the target. Before fusion, each camera predicts the target information of other cameras to temporally align its information with the (asynchronous) information received from other cameras. The algorithm predicts the target state using the latest estimated velocity of the target. The experimental results show that the proposed algorithms achieve higher tracking accuracy than the state of the art under the five DTT challenges.

Reliable track continuity is an important characteristic of multiple target tracking (MTT) algorithms. In the specific case of visually tracking multiple ground targets from an aerial platform, challenges arise due to realistic operating environments such as video compression artifacts, unmodeled camera vibration, and general imperfections in the target detection algorithm. Some popular visual detection techniques include Kanade-Lucas-Tomasi (KLT)-based motion detection, difference imaging, and object feature matching. Each of these algorithmic detectors has fundamental limitations in regard to providing consistent measurements. In this thesis we present a scalable detection framework that simultaneously leverages multiple measurement sources. We present the recursive random sample consensus (R-RANSAC) algorithm in a data fusion architecture that accommodates multiple measurement sources. Robust track continuity and real-time performance are demonstrated with post-processed flight data and a hardware demonstration in which the aircraft performs automated target following. Applications involving autonomous tracking of ground targets occasionally encounter situations where semantic information about targets would improve performance. This thesis also presents an autonomous target labeling framework that leverages cloud-based image classification services to classify targets that are tracked by the R-RANSAC MTT algorithm. The communication is managed by a Python robot operating system (ROS) node that accounts for latency and filters the results over time. This thesis articulates the feasibility of this approach and suggests hardware improvements that would yield reliable results. Finally, this thesis presents a framework for image-based target recognition to address the problem of tracking targets that become occluded for extended periods of time. This is done by collecting descriptors of targets tracked by R-RANSAC. Before new tracks are assigned an ID, an attempt to match visual information with historical tracks is triggered. The concept is demonstrated in a simulation environment with a single target, using template-based target descriptors. This contribution provides a framework for improving track reliability when faced with target occlusions.

<p>This thesis considers the problem of tracking multiple static or moving targets with one single pan/tilt-camera with a limited field of view. The objective is to minimize both the time needed to pan and tilt the camera's view between the targets and the total position uncertainty of all targets. To solve this problem, several planning methods have been developed and evaluated by Monte Carlo simulations and real world experiments. If the targets are moving and their true positions are unknown, both their current and future positions need to be estimated in order to calculate the best sensor trajectory. When dealing with static and known targets the problem is reduced to a deterministic optimization problem.</p><p>The planners have been tested through experiments using a real camera mounted above a car track using toy cars as targets. An algorithm has been developed to detect the cars and associate the detections with the correct target.</p><p>The Monte Carlo simulations show that, in the case of static targets, there is a huge advantage to arrange the targets into groups to be able to view more than one target at the time. In the case of moving targets with estimated positions it can be concluded that if the objective is to minimize the error in the position estimation the best planning choice is to always move to the target with the highest position uncertainty.</p>

<p>In angle-only target tracking the aim is to estimate the state of a target with use of measurement of elevation and azimuth. The state consists of relative position and velocity between the target and the platform. The platform is an Unmanned Aerial Vehicle (UAV) and the tracking system is meant to be a part of the platform’s anti-collision system. In the case where both the target and the platform travel with constant velocity the angle measurements do not provide any information of the range between the target and the platform. The platform has to maneuver to be able to estimate the range to the target.</p><p>Two filters are implemented and tested on simulated data. The first filter is based on a Extended Kalman Filter (EKF) and is designed for tracking nonmaneuvering targets. Different platform maneuvers are studied and the influence of initial errors and the geometry of the simulation scenario is investigated. The filter is able to estimate the position of the target if the platform maneuvers and the target travels with constant velocity. Maneuvering targets on the other hand can not be tracked by the filter.</p><p>The second filter is an interacting multiple model (IMM) filter, designed for tracking maneuvering targets. The filter performance is highly dependent of the geometry of the scenario. The filter has been tuned for a scenario where the target approaches the platform from the front. In this scenario the filter is able to track both maneuvering and non-maneuvering targets. If the target approaches the platform from the side on the other hand, the filter has problems with distinguish target maneuvers from measurement noise.</p>