Unmanned aerial vehicle trajectory data fusion based on an active and passive feedback system

Abstract

The surveillance data obtained from an unmanned aerial vehicle during real-time operation are very important for unmanned aerial vehicle supervision. In this study, in order to obtain more reliable and accurate unmanned aerial vehicle flight paths, we have proposed a trajectory fusion method of unmanned aerial vehicle data based on an active and passive feedback system. The active surveillance data are obtained from automatic dependent surveillance – broadcast and the passive surveillance data are derived from the unmanned aerial vehicle ground control station. The convex combination fusion algorithm is employed to fuse the two sets of data to obtain the accurate flight state and a continuous stable running track for the unmanned aerial vehicle. The simulation results showed that the fusion trajectory obtained using the convex combination fusion algorithm was smoother than the local trajectory obtained by the interacting multiple model algorithm.

Keywords

Active and passive feedback convex combination fusion algorithm unmanned aerial vehicle unmanned aerial vehicle supervision unmanned aerial vehicle track fusion interacting multiple model

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References

Light and small UAV operation regulations (trial operation). www.caac.gov.cn (accessed 29 December 2015).

Niu

Varshney

. Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J Wireless Commun Netw 2005; 2005: 462–472.

Brenner T, Hardejewicz J, Rupniewski M, et al. Signals and data fusion in a two-band radar. In: 2012 13th international radar symposium (IRS), vol. 49, Warsaw, Poland, 23–25 May 2012, pp.15–18.

Ding

Liu

, et al. Adaptive interacting multiple model algorithm based on information-weighted consensus for maneuvering target tracking. Sensors 2018; 18: 2012.

Elenchezhiyan

Prakash

. State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm. ISA Trans 2015; 58: 520–532.

Hwang

Seah

Lee

. A study on stability of the interacting multiple model algorithm. IEEE Trans Autom Control 2017; 62: 901–906.

Govaers

Koch

. An exact solution to track-to-track-fusion at arbitrary communication rates. IEEE Trans Aerosp Electron Syst 2012; 48: 2718–2729.

Battistelli G, Chisci L, Fantacci C, et al. Distributed peer-to-peer multitarget tracking with association-based track fusion. In: International conference on information fusion, 2014, pp.1–7. New York: IEEE.

Quan

Zhang

. A distributed coefficient fusion method based on filter coefficients. Comput Eng Appl 2011; 47: 141–143.

10.

Liu

. Distributed multi-sensor trajectory fusion algorithm based on dynamic weighting. Comput Appl Res 2006; 23: 59–61.

11.

Miranda

Baker

Woodbridge

, et al. Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization. Signal Process Mag IEEE 2006; 23: 66–76.

12.

Nayak

Karaya

. Target tracking by a quadrotor using proximity sensor fusion based on a sigmoid function. IFAC-PapersOnLine 2018; 51: 154–159.

13.

Chen ZH, Wang C, et al. Heterogeneous sensor information fusion based on kernel adaptive filtering for UAVs' localization. In: 2017 IEEE international conference on information and automation (IEEE ICIA 2017), 2017, pp.171–176.