Cagatay Tanil

Collaborative mission planning for UAV cluster to optimize relay distance

Unmanned Aerial Vehicles (UAVs) coordinated path planning and intercommunication for visual exploration of a geographical region has recently become crucial. Multiple UAVs cover larger area than a single UAV and eliminate blind spots. To improve the surveillance, survivability and quality of the communication, we propose two algorithms for the route planning of UAV cluster operated in obstacle rich environment: (i) Multiple Population Genetic Algorithm (MPGA) (ii) Relay Selection Criteria (RSC). The main objective of MPGA is to minimize the total mission time while maintaining an optimal distance for communication between the neighboring nodes. MPGA utilizes evolutionary speciation techniques with a novel Feasible Population Creation Method (FPCM) and enhanced Inter-species Crossover Mechanism (ISCM) to obtain diversified routes in remarkably short time. In obtaining collision-free optimum paths, UAVs are subjected to constraints such as limited communication range, maximum maneuverability and fuel capacity. In addition to the path planning, RSC is developed for selection of UAVs relay nodes that is based on the location of the relay relative to source and destination. It is crucial since the Bit Error Rate (BER) performance of the link significantly depends on the location of the selected relay. In this paper, path planning and relay allocation algorithms are combined to have a seamless high quality monitoring of the region and to provide superior Quality of Service (QoS) for audio-video applications. Also, simulations in different operation zones with a cluster of up to six UAVs are performed to verify the feasibility of the proposed algorithms both in optimality and computation time.

Cooperative Path Planning for Multiple Missiles - An Evolutionary Speciation Approach

Saturation attack is a crucial military tactic that overwhelms the enemy side in a complicated battle zone. The tactic relies on routing greater number of missiles that are aimed to be on target simultaneously to increase the target probability of acquisition. In accordance with the concept of coordinated attack in naval zone, we present an offline multiple path planning approach to generate cooperative and mission adaptable routes in an accurate and efficient manner. The algorithm utilizes evolutionary speciation to find suboptimal paths in an obstacle rich environment while considering the physical limitation of missiles. This multiple-population genetic algorithm has variable-length chromosomes and a novel population creation method that generates diversified feasible paths in a considerably short time. The computation load of clustering is also reduced drastically by starting with the feasible paths. By means of enhanced inter-species crossover mechanism, new species can be produced in the next generations in case there are missing routes in the initial population. Simulations are performed in two-dimensional complex scenarios and the results demonstrate that the proposed approach is able to plan multiple paths simultaneously in a remarkably short time while maintaining the optimality.

External Configuration Optimization of Missiles in Conceptual Design

The main area of emphasis in this paper is to investigate the methods and technology for aerodynamic configuration sizing of missiles and to develop a software platform in MATLAB® environment as a design tool which has an ability of optimizing the external configuration of missiles for a set of flight requirements specified by the user through a graphical user interface. A genetic algorithm based optimization tool is prepared by MATLAB® that helps the designer to find out the best external geometry candidates in the conceptual design stage. Missile DATCOM is employed as the aerodynamic coefficient prediction package to determine aerodynamic coefficients of each external geometry candidate in finding their performance merits by integrating their dynamic equations of motion. Numerous external geometry candidates are rapidly eliminated according to objectives and constraints specified by designers, which provide necessary information in preliminary design. In this elimination, the external geometry candidates are graded according to their flight performances in order to discover an optimum solution. All of the flight performance data are obtained by running a two degree-of-freedom simulation in the vertical plane. In order to solve the resulting multi-objective optimization problem with a set of constraint of linear and nonlinear nature and in equality and inequality forms, geneticalgorithm-based methods are applied. Hybrid encoding methods in which the integer configuration variables (i.e., nose shape and control type) and real-valued geometrical dimension (i.e., diameter, length) parameters are encoded in the same individual chromosome. An external configuration design tool (EXCON) is developed as a synthesis and external sizing tool for the subsonic cruise missiles. A numerical example, the reconfiguration problem of an anti-ship cruise missile, is presented to demonstrate the accuracy and feasibility of the conceptual design tool.

An INS Monitor to Detect GNSS Spoofers Capable of Tracking Vehicle Position

In this paper, we describe and evaluate a new monitor that uses inertial navigation system (INS) measurements to detect spoofing attacks on global navigation satellite system (GNSS) receivers. Spoofing detection is accomplished by monitoring the Kalman filter innovations in a tightly coupled INS/GNSS mechanization. Monitor performance is evaluated against worst case spoofing attacks, including spoofers capable of tracking vehicle position. There are two main contributions of this paper. The first is a mathematical framework to quantify postmonitor spoofing integrity risk. The second is an analytical expression of the worst case sequence of spoofed GNSS signals. We then apply these to an example spoofing attack on a Boeing 747 on final approach. The results show that GNSS spoofing is easily detected, with high integrity, unless the spoofers position-tracking devices have unrealistic, near-perfect accuracy, and no delays.

Improved heuristic and evolutionary methods for tactical missile mission planning

In this paper, improved heuristic and evolutionary methods are presented for pre-launch trajectory optimization of a tactical missile. Computation time and trajectory length are minimized as objectives whereas maneuverability of the missile, total amount of fuel, obstacles, no-fly zones, islands, shorelines are considered to be constraints. There are two main methods developed for this purpose. One method is based on heuristic search by adaptive-based A* algorithm. In this method, sub-optimal path is obtained by constructing a network in which node distance (leg length) and node intensity can be changed adaptively with respect to mission environment in the search time. This enhancement in conventional A* method leads to closer optimal trajectories in less computation load especially in complex mission scenarios such as shorelines having narrow pass, too many unintended targets or friends etc. The other proposed path planning method is improved genetic algorithm. This algorithm has a variable-length chromosome and a real-valued encoding as well as an intelligent population creation method that produces feasible individuals only. By means of starting with a feasible population, it is observed that convergence time is far less than using random creation of initial population. Furthermore, for rapid analysis and comparison of the two proposed methods in different environments, a generic graphical user interface (MPT-The Mission Planning Tool) is developed.

Detecting Global Navigation Satellite System Spoofing Using Inertial Sensing of Aircraft Disturbance

Vulnerability of Global Navigation Satellite System users to signal spoofing is a critical threat to positioning integrity, especially in aviation applications where the consequences are potentiall...

Kalman filter-based INS monitor to detect GNSS spoofers capable of tracking aircraft position

In this work, we propose an innovation-based INS spoofing monitor that utilizes a tightly-coupled INS-GNSS integration in a Kalman filter. The performance of the monitor is evaluated when a spoofer tracks and estimates the aircraft position. To create the worst case spoofing conditions, we analytically derive a Kalman filter-based worst-case sequence of spoofed GNSS measurements. Utilizing this worst-case spoofing attack scenario during a Boeing 747 (B747) final approach, we prove that unless the spoofers position-tracking devices have unrealistic accuracy and no-delay, the proposed INS monitor performance is highly effective in detecting spoofing attacks.