Adaptive Estimation of Small-Size UAV Motion Parameters Based on Video Camera and FMCW Rangefinder Data
Keywords:UAV, FMCW rangefinder, video camera, motion parameters, types of maneuver, adaptive estimation, mixed Markov processes, posterior probability density
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.
Abs Plaza. Using Unmanned Aerial Vehicles. (2016). American Bureau of Shipping.
Tkachuk A. G., Koval A. V., Gumenyuk A. A., Bogdanovskyj M. V. (2021). Avtomatyzovana systema monitoryngu nayavnosti shkidlyvyx ta vybuxonebezpechnyx gaziv na osnovi mini bezpilotnyx litalnyx aparativ: monografiya [Automated system for monitoring the presence of harmful and explosive gases based on mini unmanned aerial vehicles: monograph]. Zhytomyr: Derzhavnyj universytet «Zhytomyrska politexnika», 141 p.
Dinesh Sathyamoorthy (2015). A Review of Security Threats of Unmanned Aerial Vehicles and Mitigation Steps. STRIDE, Ministry of Defence, Malaysia, 14 p.
Dudush A. S., Tyutyunnik V. A., Reznichenko A. A., Hohoniants S. U. (2018). Suchasnyj stan ta problemy protydiyi malovysotnym, nyzkoshvydkisnym ta malorozmirnym BPLA [State of the art and problems of defeat of low, slow and small unmanned aerial vehicles]. Suchasni informacijni texnologiyi u sferi bezpeky ta oborony [Modern Information Technologies in the Sphere of Security and Defence], Vol. 31, No. 1, pp. 121-131. doi:10.33099/2311-7249/2018-31-1-121-131.
Wallace, R. J., Loffi, J. M. (2015). Examining Unmanned Aerial System Threats & Defenses: A Conceptual Analysis. International Journal of Aviation, Aeronautics, and Aerospace, Vol. 2. Iss. 4. doi: 10.15394/ijaaa.2015.1084.
Kartashov V. M., Olejnykov V. Y., Shejko S. A., et al. (2018). Osobennosty obnaruzhenyya i raspoznavanyya malykh bespylotnykh letatelnykh apparatov [Features of detection and recognition small unmanned aircraft]. Radyotekhnyka. Vseukr. mezhved. nauch.-texn. sb., Vol. 195, pp. 235-243.
Kartashov V. M., Korytcev Y. V., Shejko S. A., Olejnykov V. N., Zubkov O. V., Babkyn S. Y. (2020). Optyko-elektronnie metody obnaruzhenyya vozdushnikh ob'ektov i yzmerenyya ykh koordynat [Optical and electronic methods of detection of aerial objects and measurement of their coordinates]. Radyotekhnyka: Vseukr. mezhved. nauch.-texn. sb., Vol. 202, pp. 153 – 159.
Automatic Anti-Drone Security.
He You, Xiu Jianjuan, Guan Xin (2016). Radar Data Processing with Applications. Wiley. DOI: 10.1002/9781118956878.
Bashynskyj V. G., Bzot V. B. ta in., vseogo 9 avt. (2014). Malogabarytnye bespylotnye avyacyonnye kompleksy: monografiya [MiniUVS]. Zaporizhzhya: «AO Motorsich», 262 p.
Zhuk S. Ya. (2008). Metody optimizacii diskretnyh dinamicheskih sistem so sluchajnoj strukturoj / Monografiya [Methods of optimization of discrete dynamic systems with a random structure / Monograph]. K.: NTUU «KPI», 232 p.
Kuzmyn S. Z. (2000). Cyfrovaya radyolokacyya. Vvedenye v teoryyu [Digital radar. Introduction to the theory]. Kyev: KViCz, 428 p.
Santos, P. Sebastião and N. Souto. (2019). Low-cost SDR based FMCW radar for UAV localization. 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC), pp. 1-6, doi: 10.1109/WPMC48795.2019.9096117.
Tovkach, I., Zhuk, S. (2020). Adaptive filtration of the UAV movement parameters based on the AOA-measurement sensor networks. International Journal of Aviation, Aeronautics, and Aerospace, Vol. 7, Iss. 3. doi:10.15394/ijaaa.2020.1497.
Farina A., Studer F. A. (1993). Cyfrovaya obrabotka radyolokacyonnoj ynformacyy. Soprovozhdenye celej [Radar Data Processing: Introduction and Tracking]; per. s angl. A. M. Bochkareva; pod red. A. N. Yureva. M.: Radyo y svyaz, 320 p.
Zhuk S. Y., Neuimin O. S., Tovkach I. O. and Chmelov V. O. (2020). Adaptive Algorithm For Tracking Maneuvering Targets In A Complex Jamming Environment For A Radar With Range Rate Measurement. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, pp. 249-254, doi: 10.1109/TCSET49122.2020.235433.
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