Proceedings Article | 5 May 2017
Proc. SPIE. 10195, Unmanned Systems Technology XIX
KEYWORDS: Mathematical modeling, Unmanned aerial vehicles, Detection and tracking algorithms, Sensors, Control systems, Linear filtering, Telecommunications, Artificial intelligence, Data conversion, Dynamical systems, Unmanned vehicles, Collision avoidance, Fuzzy logic, Fuzzy systems, Evolutionary algorithms
The necessity and occurrence of Unmanned Aerial Vehicles on the battlefield in near future is increasing day-by-day and that leads to the problem of sharing air space with manned air vehicles. However, avoiding collisions and/or deploying countermeasures during threat detection are a very crucial issue in most of the unmanned aerial vehicles [1] or similar. There is a need for a Sense, Avoid or React [2] system to track objects of potential collision risk or determine any action to avoid or mitigate a collision and, react with countermeasures after detection of hazardous situations i.e., during midair attack, collisions, flight path obstacles or dense clouds. The author of this paper present an algorithm for decision making system based on countermeasures during collision avoidance scenario or threat detection. A general framework to deal with non-linear dynamic systems and will developed consisting of a system of various collision and risks scenarios with moving and stationary threats that is based on straight future projection. The solution will include an algorithm that captures the path prediction of the threat. The proposed optimization problem resolution will aim to maintain minimum separation between two vehicles or threats and applying necessary countermeasures if the collision becomes unavoidable. The multi-sensor fusion system, will generate the object status signal, merges vehicle status, will release an assessment of collision in the form of warning level. A fuzzy controller for countermeasure of friendly maneuver will be presented to generate active and passive measure signals based on the response from assessment signal and vehicle sensor signals. The design implementations and simulations using FPGA will be included.
