Halit Ergezer

Path Planning for UAVs for Maximum Information Collection

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators.

Path planning for multiple unmanned aerial vehicles

Path planning is a problem of designing the path the vehicle is supposed to follow in such a way that a certain objective is optimized. In our case, the objective is to maximize collected amount of information from Desired Regions (DR), meanwhile flying over the Forbidden Regions is avoided. In this study the path planning problem for multiple unmanned air vehicles (UAV) is studied. Path of each UAV is designed by using our algorithm for single UAV. The part of our algorithm for determining the visiting sequence of DRs has been altered to find the visiting sequence of DRs for each UAV. The visiting sequence of DRs for each UAV has been determined by solving fixed destination multiple-Traveling Salesman Problem (mTSP). After finding the visiting sequence for each UAV, the problem will be considered as multiple single-UAV-Path-Planning-Problem. The Genetic Algorithm has been used to solve of mTSP. The algorithm has been tested using different scenarios and obtained results are presented.

Planning unmanned aerial vehicle's path for maximum information collection using evolutionary algorithms

Abstract Path planning is a problem of designing the path the vehicle is supposed to follow in such a way that a certain objective is optimized. In our study the objective is to maximize collected amount of information from Desired Regions (DR), meanwhile flying over the Forbidden Regions is avoided. In this paper, the path planning problem for single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators; Pull-to-Desired-Region (PTDR), Push-From-Forbidden-Region (PFFR), Pull-to-Finish-Point (PTFP). Besides these newly proposed operators, mutation and crossover operators have been used. The algorithm has been tested using two different scenarios and obtained results are presented in section 5. The 6 Degree-of-Freedom equation of motion has been used. The equations of motion of 12 state equations and the autopilot have been simulated in MATLAB/Simulink. Unlike previous studies in this field, we try to maximize collected information, instead of minimizing total mission time.

Refining the progressive multiple sequence alignment score using genetic algorithms

Given a set of N (N>2) sequences, the Multiple Sequence Alignment (MSA) problem is to align these N sequences, possibly with gaps, that bring out the best score due to a given scoring criterion between characters. Multiple sequence alignment is one of the basic tools for interpreting the information obtained from bioinformatics studies. Dynamic Programming (DP) gives the optimal alignment of the two sequences for the given scoring scheme. But, in the case of multiple sequence alignment it requires enormous time and space to obtain the optimal alignment. The time and space requirement increases exponentially with the number of sequences. There are two basic classes of solutions except the DP method: progressive methods and iterative methods. In this study, we try to refine the alignment score obtained by using the progressive method due to given scoring criterion by using an iterative method. As an iterative method genetic algorithm (GA) has been used. The sum-of-pairs (SP) scoring system is used as our target of optimization. There are fifteen operators defined to refine the alignment quality by combining and mutating the alignments in the alignment population. The results show that the novel operators, sliding-window, local-alignment, which have not been used up to now, increase the score of the progressive alignment by amount of % 2.

Real-Time Radar, Target, and Environment Simulator

In this work, a real-time radar target and environment simulator (RTSim) is presented. RTSim provides a hardware-in-the-loop (HIL) test system for radar signal processing units (RSPU). RTSim provides repeatable experiments for radar developers in digitally controlled but complex environments. Moreover, it reduces the development costs by limiting expensive field tests. RTSim consists of three main components; a control computer that provides the user interface and scenario generation software, embedded processors for environment calculations, and field programmable gate arrays (FPGAs) for baseband radar signal generation. In hardware-in-the-loop operation scenario RTSim and RSPUs work in synchronization. RSPU sends the parameters of current pulse burst to RTSim and it generates baseband IQ signals using these parameters and user programmed environment parameters obtained from scenario generation software. RTSim can generate return signals for targets, jammers, clutter, and system noise. The generated baseband signals are sent to RSPU over fiberoptic lines.

Hardware-in-the-loop radar test simulator

In this work, a real-time hardware-in-the-loop (HIL) radar target and environment simulator (RTSim) is presented. RTSim is developed to test the radar systems starting from the initial algorithm development until the final field testing stages. In this way, it is possible to avoid the costly field tests in constantly changing conditions and test the radar systems in a controlled but highly complex environments. In the real-time operation scenario, Radar Signal Processing Unit (RSPU) sends the parameters of the radar signal to the RTSim. For each receive channel, RTSim generates baseband IQ (16-bit I, 16-bit Q) signals using these parameters and user programmed environment including targets, jammers, atmospheric effects, clutter, and radar related system noise. The generated baseband signals are sent to RSPU over fiberoptic lines.

Real-time FPGA implementation of range and velocity deception techniques

In this paper, a method that is developed for the simulation of range and velocity deception techniques on FPGAs is presented. The signal that will be received from environment by the radar, is modeled in baseband with the effects of the deception techniques. Range deception is modeled by changing the delay and Pulse Repetatition Intervals (PRI) of the radar signal according to the profile of the deception technique. Velocity deception technique is modeled by adjusting the phase of the baseband signal according to the doppler effect of the velocity difference that is calculated using the profile of the deception technique. Coordinated range and velocity deception is applied by synchronizing the profiles of both techniques. The method that is developed allows the generation of baseband radar signals on a Xilinx Virtex-5 FPGA at 200 MHz sampling frequency.

Modeling electromagnetic wave propogation in 3D environments

In this paper a method is developed to model two-way propagation of eletromagnetic waves. A recursive algorithm based on the Split Step Parabolic Equation Method (SSPE) is introduced. This algorithm both simplifies the implementation of the two-way propagation algorithm and also reduces the computational complexity. The terrain is modeled using a staircase approximation and also the targets are modeled using simple geometric shapes to investigate their effects on propagation. The proposed method is also used to model three-dimensional propagation on real terrain data using multi-core processors.

Evaluation of appropriateness of the score functions used in Multiple Sequence Alignments problem

In this study, the effects of the scoring functions, which are used in multiple sequence alignment problem, to find the biologically meaningful alignment has been investigated. BALiBASE version 3 has been used as benchmark set. Results have been obtained by calculating score values for alignments in benchmark set and investigating whether the higher score is possible for benchmark alignment or not.