Effective detection of strong maneuvers of object in noisy measurements conditions. Abstract

Effective detection of strong maneuvers of object in noisy measurements conditions

I. A. Kalinov, R. T. Agishev

Moscow Institute of Physics and Technology (State University), 9 Institutsky Per., Dolgoprudny, Moscow region 141700, Russia

Skolkovo Institute of Science and Technology, Nobel str. 3, Moscow 121205, Russia

The paper is received on May 18, 2019

Abstract. In this paper coordinate measurements of the object, which trajectory includes strong maneuvers, are used as input data. Strong maneuvers described here as fast turns of the object in sharp angles. Measurement data are very noisy, and standard filtration methods (Kalman filter) don’t give satisfactory results. In this paper an improved filtration method that is capable to detect strong maneuvers effectively was implemented. The filtration algorithm was applied to multirotor UAV’s tracking task.

Keywords: UAV, DSP, Kalman filter, trajectory analisys.

1. Z. Li,H. Wu. A survey of maneuvering target tracking using Kalman filter. Proc. 4th Int. Conf. Mechatronics, Mater., Chem. Comput. Eng. (ICMMCCE), 2015, Vol. 39. pp. 542–545.

2. M. Boutayeb, D. Aubry. A strong tracking extended Kalman observer for nonlinear discrete-time systems. IEEE Trans. Autom. Control, 1999, Vol. 44, No. 8, pp. 1550–1556.

3. X. Yun, E. R. Bachmann. ‘Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans. Robot., 2006, Vol. 22, No. 6, pp. 1216–1227.

4. Y. T. Chan, A. G. C. Hu, J. B. Plant. Kalman filter based tracking scheme with input estimatio. IEEE Trans. Aerosp. Electron. Syst., 1979, Vol. AES-15, No. 2, pp. 237–244.

5. C. Snyder, F. Q. Zhang. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Monthly Weather Rev., 2003, Vol. 131,Nno. 8, pp. 1663–1677.

6. F. E. Daum, R. J. Fitzgerald. Decoupled Kalman filters for phasedarray radar tracking. IEEE Trans. Autom. Control, 1983, Vol. AC-28, No. 3, pp. 269–283.

7. R. S. Scalero, N. Tepedelenlioglu. A fast new algorithm for training feedforward neural networks. IEEE Trans. Signal Process., 1992, Vol. 40, No. 1, pp. 202–210.

8. Y. Iiguni, H. Sakai, H. Tokumaru. A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter. IEEE Trans. Signal Process., 1992, Vol. 40, No. 4, pp. 959–966.

10.Gabriel F. Basso, Thulio Guilherme Silva De Amorim, Alisson V. Brito, Tiago P. Nascimento. Kalman Filter With Dynamical Setting of Optimal Process Noise Covariance. IEEE Access, 2017, Vol. 5, pp. 8385-8393.

I. A. Kalinov, R. T. Agishev. Effective detection of strong maneuvers of object in noisy measurements conditions. Zhurnal Radioelektroniki - Journal of Radio Electronics. 2019. No.6. Available at http://jre.cplire.ru/jre/jun19/3/text.pdf