Increasing fast estimation of assessment of navigation parameters for the UAV based on a linear Kalman filter

Abstract

Navigation of the short-range unmanned aerial vehicles (UAV) requires the evaluation of parameters such as the velocity vector and position in real-time using an onboard computer. The use of the complex algorithms capable of working with nonlinear systems reduces the efficiency of calculations; therefore, the urgent task is to reduce the computational complexity of the existing algorithms while maintaining the required accuracy, which allows you to release CPU resources for solving other tasks that are needed for the UAV control system. The use of a flat Earth model for solving a navigation problem allows using the linear differential equations to describe movements in UAV space and using the linear Kalman filter as a basis for an algorithm for estimating navigation parameters. The rejection of iterative recalculation of the Kalman filter gain factors significantly reduces the number of arithmetic operations but at the same time retains the required accuracy of the estimation of navigation parameters, and the optimization of the multiplication of sparse zeros of the matrices further reduces the computational complexity. In this regard, the use of an algorithm for solving a navigation problem, which has a minimum number of arithmetic operations, increases the efficiency of processing data from the control system as a whole and allows for better controllability of the UAVs.