Situation awareness for UAV operating in terminal areas using bearing-only observations and circuit flight rules

Situation awareness is required for an Unmanned Aerial Vehicle (UAV) when it makes an arrival at an uncontrolled airfield. Since no air traffic control service is available, the UAV needs to detect and track other traffic aircraft by using its onboard sensors. General aviation pilots obtain enough situation awareness to operate in these environments, only using their vision and radio messages heard from other traffic aircraft. To improve the target tracking performance of a UAV, the circuit flight rules and standard radio messages are incorporated to provide extra knowledge about the target behaviour. This is achieved by using the multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to perform Bayesian filtering with bearing-only observations from a vision sensor

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction

This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning. In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors. The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness. The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches

Integrated target tracking and weapon guidance

The requirements of a modern guided weapon will be established based on the current and perceived threats at the time the design is commissioned. However the design of a modern guided weapon is a long and expensive process which can result in the weapon entering service only for the original threat to have changed or passed, inevitably inducing a capability gap. The defence budgets of the major military powers such as the UK and USA continue to shrink. As a result the emphasis of military research is being placed on adapting current legacy systems to bridge these capability gaps. One such gap is the requirement to be able to intercept small relocatable, highly manoeuvrable targets. It was demonstrated a number of years ago, that the performance of a legacy weapon against manoeuvering targets could be potentially increased by retrofitting a data link to the weapon. The data link allows commands to be sent to the weapon in flight. The commands will result in the weapon executing one or more manoeuvres which will change the shape of the trajectory. This has the potential to improve the performance of current Advanced Anti-Armour Weapons (AAAW) against manoeuvring targets. The issue which arises from data linking any weapon including an AAAW, is that the ability to shape the trajectory of the weapon will be limited due to the original design parameters of the non data linked system. Therefore in order to obtain the maximum performance increase, the trajectory shaping (retargetting) capability must be efficiently utilised over the duration of the weapon fly out. It was postulated in this thesis that this could be achieved using an integrated fire control system, which would seek to calculate an optimal shaped trajectory. The optimal trajectory should maximise the ability of the weapon to respond to target manoeuvres, thereby improving the probability of a successful intercept occurring. The potential effectiveness of an integrated fire control system was explored by considering the scenario of a generic data linked AAAW which is to intercept a small highly manoeuvrable surface vessel. A total of three integrated fire control systems were developed which calculated the optimal trajectory for different criteria. The first system optimised the weapon trajectory considering multiple predicted target trajectories. Each trajectory had an associated probability. For a given weapon trajectory, the seeker would be able to detect the target at one or more locations along certain predicted target trajectories. The sum of the probabilities associated with the detectable locations represented the total probability of intercept. The weapon trajectory was optimised by calculating the trajectory which achieved the maximum probability of intercept using simulated annealing and simple search optimisation algorithms. The second system optimised the weapon trajectory considering only the most probable trajectory (M.P.T) from a distribution of predicted target trajectories. Appropriate commands were calculated such that a location along this M.P.T trajectory was detectable at some instant in time. The third system presented in this thesis optimised the trajectory considering the maximum probability of intercept initially and then only the M.P.T trajectory later on in the engagement. The three integrated systems and a Fire and Forget system were tested against 80 random target trajectories. In each of the integrated fire control systems, the performance of the AAAW against manoeuvring targets was significantly improved when compared to the Fire and Forget results

Vision-based vehicle detection and tracking in intelligent transportation system

This thesis aims to realize vision-based vehicle detection and tracking in the Intelligent Transportation System. First, it introduces the methods for vehicle detection and tracking. Next, it establishes the sensor fusion framework of the system, including dynamic model and sensor model. Then, it simulates the traffic scene at a crossroad by a driving simulator, where the research target is one single car, and the traffic scene is ideal. YOLO Neural Network is applied to the image sequence for vehicle detection. Kalman filter method, extended Kalman filter method, and particle filter method are utilized and compared for vehicle tracking. The Following part is the practical experiment where there are multiple vehicles at the same time, and the traffic scene is in real life with various interference factors. YOLO Neural Network combined with OpenCV is adopted to realize real-time vehicle detection. Kalman filter and extended Kalman filter are applied for vehicle tracking; an identification algorithm is proposed to solve the occlusion of the vehicles. The effects of process noise as well as measurement noise are analysed using variable-controlling approach. Additionally, perspective transformation is illustrated and implemented to transfer the coordinates from the image plane to the ground plane. If the vision-based vehicle detection and tracking can be realized and popularized in daily lives, vehicle information can be shared among infrastructures, vehicles, and users, so as to build interactions inside the Intelligent Transportation System