Adaptive Estimation of Small-Size UAV Motion Parameters Based on Video Camera and FMCW Rangefinder Data
DOI:
https://doi.org/10.20535/RADAP.2023.91.46-52Keywords:
UAV, FMCW rangefinder, video camera, motion parameters, types of maneuver, adaptive estimation, mixed Markov processes, posterior probability densityAbstract
Nowday, the development of small-size UAV (SUAV) technology has led to its widespread use in various industries and to meet the needs of commercial and private consumers. At the same time, the wide access to this technology and its proliferation also creates a new class of threats. This necessitates the development of surveillance capabilities that detect and track SUAVs in air space. A combined surveillance system including a video camera and an FMCW rangefinder is capable of determining the coordinates of SUAV location in air space. Modern SUAVs are intensely mobile targets that can perform intense maneuvers, hover, and stay stationary in air space. This requires the use in the development of trajectory processing algorithms of adequate methods of estimating parameters of SUAV movement, which take into account its possible maneuvers. A stochastic dynamical system with a random structure in discrete time is used to describe the SUAV motion in the local rectangular coordinate system, which takes into account three main types of motion: hovering, almost uniform motion, motion with maneuver. The paper derived a measurement equation in the local rectangular coordinate system of the combined SUAV surveillance system, which includes a video camera and FMCW rangefinder. The mathematical apparatus of mixed Markov processes in discrete time is used for synthesis of the adaptive estimation algorithm of SUAV motion parameters, which allows obtaining recurrent estimation algorithms. In the developed algorithm the first and second moments of conditional posterior probability densities of SUAV motion parameters are calculated, and also the posterior probabilities of different types of its motion are extrapolated. At the same time it provides poligausible approximation of unconditional posterior probability density of SUAV movement parameters when passing to the next estimation step. The adaptive filter is multichannel and belongs to the class of devices with feedbacks between channels. The analysis of the developed algorithm of adaptive estimation of SUAV motion parameters from video camera data and FMCW-rangefinder is carried out by statistical modeling.
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