EE Seminar: Single Sensor Trajectory Optimization for Best Emitter Localization
Speaker: Elad Tzoreff
Ph.D. student under the supervision of Prof. Anthony J. Weiss
Monday, March 4th, 2019 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering
Single Sensor Trajectory Optimization for Best Emitter Localization
Abstract
Passive emitter localization has many civilian, commercial and military applications. The rapidly increasing utilization of smartphones and, therefore mobile applications, has created a high demand for location based services, both in commercial applications and social networking, for multiple and varied uses. Location based services are also critical to many businesses and government organizations to derive real insight from data tied to specific locations where activities take place. The spatial patterns that location-related data and services can provide is one of the most powerful and useful aspects when location is a common denominator in all of these activities and can be leveraged to better understand patterns and relationships. Accordingly, precise, and personalized localization solutions become a fundamental requirement of any commercial/social service.
In this presentation I will address the problem of a single platform trajectory optimization, aims to provide a targeted localization solution for a given emitter based on TOA measurements (i.e., minimizing the localization error of the emitter). The problem of trajectory optimization is a constraint non-convex optimization problem. Constraints arise due to physical limitation of the platforms, and geographical constrains such as restricted areas and safety zones in which the receiver is not allowed to travel. I will discuss two use-cases, a pre-mission design in which the entire trajectory is optimized based on prior knowledge on the emitter location. The second use-case is a real-time path design, in which the receiver begins with a coarse estimation of the emitter location, and searches for the next best way-point to travel to. In this case, the uncertainty in the estimation is incorporated into the optimization problem, in order to avoid over-optimistic steps in preliminary stages of the process. For both use-cases, we propose convex relaxation solutions based on Semi-definite relaxation methods and demonstrate their impressive results in terms of performance and robustness. Next, I will discuss the trajectory optimization of a pair of sensors which cooperate to localize an emitter based on TDOA observations. The presence of more than a single sensor imposes additional constraints on the pairwise distances between the sensors. We derive a solution based on the alternating direction of multipliers (ADMM) with intermediate steps carried out using the majorization minimization (MM) and SDR methods. The algorithm is demonstrated to outperform global optimizers such as genetic and the basin-hoping algorithms, both in terms of performance (better localization error) and speed of convergence.
As a final step, in order to provide an algorithmic solution that is capable of operating in real time environments, we introduce a differential dynamic programming (DDP) solution that is demonstrated to converge quadratically to good local optima, exploiting the desired properties of Newton method.
