Doo-Hyun Cho

HRRPs-based target length estimation using a FMCW radar

This paper proposes a filtering method to estimate the length of a moving target tracked by a frequency modulated continuous wave (FMCW) radar. The method generates high resolution range profiles (HRRPs) from the radar echo signal and then integrates them into an extended Kalman filter (EKF) to simultaneously estimate the target motion and geometry. Numerical simulations on tracking a ballistic target represented by one-dimensional scattering centers verify the effectiveness of the proposed length estimation methodology.

Informative Path Planning and Mapping with Multiple UAVs in Wind Fields

Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents an systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.

Optimal Continuous-Time Job Scheduling for Multiple Low Earth Orbit Satellites

This paper proposes a mixed-integer linear program (MILP) formulation for job scheduling of a constellation of low earth orbit satellites and investigates the applicability/scalability of a standard MILP solver that produces the optimal solution. The goal of the satellite constellation job scheduling is to allocate each job for satellites and to determine the job starting times in order to maximize the overall mission performance measure. The scheduling problem is formulated by first selecting and timetabling the job (observation activities) to acquire the user-requested data of the Earth surface, with incorporating satellite operational constraints such as visibility time windows, transition time between consecutive jobs, maximum attitude angle, energy capacity, memory capacity. The proposed formulation relaxes some of these constraints, which would not have impacts on real instances, but additionally includes precedence condition between jobs and job-agent compatibility constraints. An off-the-shelf MILP solver is used to obtain the optimal solution for this scheduling formulation; numerical experiments designed for investigating the applicability of the optimal solver in terms of problem size indicates that the optimal solution can be obtained in a tractable manner up to the problem size with three satellites and hundreds of jobs.