Sensor Path Planning for Emitter Localization

The localization of a radio frequency (RF) emitter is relevant in many military and civilian applications. The recent decade has seen a rapid progress in the development of small and mobile unmanned aerial vehicles (UAVs), which offer a way to perform emitter localization autonomously. The path a UAV travels influences the localization significantly, making path planning an important part of a mobile emitter localization system. The topic of this thesis is path planning for a UAV that uses bearing measurements to localize a stationary emitter. Using a directional antenna, the direction towards the target can be determined by the UAV rotating around its own vertical axis. During this rotation the UAV is required to remain at the same position, which induces a trade-off between movement and measurement that influences the optimal trajectories. This thesis derives a novel path planning algorithm for localizing an emitter with a UAV. It improves the current state of the art by providing a localization with defined accuracy in a shorter amount of time compared to other algorithms in simulations. The algorithm uses the policy rollout principle to perform a nonmyopic planning and to incorporate the uncertainty of the estimation process into its decision. The concept of an action selection algorithm for policy rollout is introduced, which allows the use of existing optimization algorithms to effectively search the action space. Multiple action selection algorithms are compared to optimize the speed of the path planning algorithm. Similarly, to reduce computational demand, an adaptive grid-based localizer has been developed. To evaluate the algorithm an experimental system has been built and the algorithm was tested on this system. Based on initial experiments, the path planning algorithm has been modified, including a minimal distance to the emitter and an outlier detection step. The resulting algorithm shows promising results in experimental flights

A new Measure for Optimization of Field Sensor Network with Application to LiDAR

This thesis proposes a solution to the problem of modeling and optimizing the field sensor network in terms of the coverage performance. The term field sensor is referred to a class of sensors which can detect the regions in 2D/3D spaces through non-contact measurements. The most widely used field sensors include cameras, LiDAR, ultrasonic sensor, and RADAR, etc. The key challenge in the applications of field sensor networks, such as area coverage, is to develop an effective performance measure, which has to involve both sensor and environment parameters. The nature of space distribution in the case of the field sensor incurs a great deal of difficulties for such development and, hence, poses it as a very interesting research problem. Therefore, to tackle this problem, several attempts have been made in the literature. However, they have failed to address a comprehensive and applicable approach to distinctive types of field sensors (in 3D), as only coverage of a particular sensor is usually addressed at the time. In addition, no coverage model has been proposed yet for some types of field sensors such as LiDAR sensors. In this dissertation, a coverage model is obtained for the field sensors based on the transformation of sensor and task parameters into the sensor geometric model. By providing a mathematical description of the sensor’s sensing region, a performance measure is introduced which characterizes the closeness between a single sensor and target configurations. In this regard, the first contribution is developing an Infinity norm based measure which describes the target distance to the closure of the sensing region expressed by an area-based approach. The second contribution can be geometrically interpreted as mapping the sensor’s sensing region to an n-ball using a homeomorphism map and developing a performance measure. The third contribution is introducing the measurement principle and establishing the coverage model for the class of solid-state (flash) LiDAR sensors. The fourth contribution is point density analysis and developing the coverage model for the class of mechanical (prism rotating mechanism) LiDAR sensors. Finally, the effectiveness of the proposed coverage model is illustrated by simulations, experiments, and comparisons is carried out throughout the dissertation. This coverage model is a powerful tool as it applies to the variety of field sensors

Localization in Spatially Correlated Shadow-Fading Environment

Στην διπλωματική αυτή εργασία ενδιαφέρομαστε για Received Signal Strength (RSS) localization λόγω της ενγενης απλοτιτας του οπου ο καθε δεκτης μετρα ισχυ. Η διατριβή παρουσιάζει τόσο θεωρητικα όσο και πειραματικά αποτελέσματα. Στην αρχη κατασκευαζεται και παρουσιάζεται ένα νέο θεωρητικό όριο για το πρόβλημα εντοπισμού μίας πηγής σε χωρικα συσχέτισομενο περιβαλον, με χρησει conditional measurments και στη συνέχεια να χρησιμοποιείτε για την αξιολόγηση των επιδόσεων. Επιπλέον, παρουσιάζονται ορισμένα θεωρητικά αποτελέσματα στο πιο δύσκολο πρόβλημα του εντοπισμού πολλαπλών πηγών και πάλι για την περίπτωση του χωτικα συσχετιζομενου shadow fading περιβαλοντος. Αυτά τα αποτελέσματα δεν χρεισιμοποιουν contitionla measurments, αλλά δείχνουν πώς η απόδοση σχετιζετε με τον αριθμό των δεκτων, των αριθμό των άγνωστων πηγων, και ο συντελεστής συσχέτισης του περιβάλλοντος. Επιπλέον, δύο πειραματικές εκστρατειες εσωτερικου χωρου περιλαμβάνονται στην παρούσα διατριβή, και στις δύο χρησιμοποιηθηκε η OpenAirInterface (OAI) πλατφόρμα. Ο κύριος στόχος των εκστρατειών ήταν 1) να εξακριβώσει η ύπαρξη χωρικης συσχετισεις του shadow fading για εσοτερικο χωρο και 2) να χρησιμοποιούν ad-hoc τεχνικές και αλγόριθμοι, προκειμένου να επιτευχθεί κάποιο όφελος από την χωρική συσχέτιση υποθέτοντας γνώση contitional measurments.In this thesis we are interested on Received Signal Strength (RSS) localization due to its simplicity as every radio measures power. Thesis presents both theoretical as well as experimental results. We derive and present a new theoretical bound for the single-source localization problem that takes spatial- correlation as well as conditional measurements into account and then uses it to assess performance. Furthermore, presents some theoretical results in the more challenging multi-source localization problem again for the case of correlated shadow-fading environments. These results did not assume prior knowledge of conditional measurements, but show how the localization performance scales with respect to the number of sensors, the number of unknown sources, and the correlation coefficient of the environment. Additionally, two indoor experimental campaigns are included in this thesis, both of them used the OpenAirInterface (OAI) platform. The main target of the campaigns was 1) to verify the existence of shadow-fading in the indoor environment and 2) to use ad-hoc techniques in our localization algorithms in order achieve some gain from spatial correlation assuming knowledge of conditional measurements.

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties