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Abstract: We investigate cooperative target tracking of multiple unmanned aerial vehicles (UAVs) with a limited communication range. This is an integration of UAV motion control, target state estimation, and network topology control. We first present the communication topology and basic notations for network connectivity, and introduce the distributed kalman consensus filter. Then, convergence and boundedness of the estimation errors using the filter are analyzed, and potential functions are proposed for communication link maintenance and collision avoidance. By taking stable target tracking into account, a distributed potential function based UAV motion controller is discussed. Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation, it is clear that the UAV motion control and target state estimation are coupled. Finally, the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.

有界噪声下保通信连接的多无人机分布式协同目标跟踪

[1]Ajorlou A, Momeni A, Aghdam AG, 2010. A class of bounded distributed control strategies for connectivity preservation in multi-agent systems. IEEE Trans Autom Contr, 55(12):2828-2833.

[2]Casbeer DW, Beard R, 2009a. Distributed information filtering using consensus filters. American Control Conf, p.1882-1887.

[3]Casbeer DW, Beard R, 2009b. Multi-static radar target tracking using information consensus filters. AIAA Guidance, Navigation, and Control Conf, p.1-9.

[4]Godsil C, Royle G, 2001. Algebraic Graph Theory. Springer-Verlag, New York, USA.

[5]Ji M, Egerstedt M, 2007. Distributed coordination control of multiagent systems while preserving connectedness. IEEE Trans Robot, 23(4):693-703.

[6]Kan Z, Dani AP, Shea JM, et al., 2012. Network connectivity preserving formation stabilization and obstacle avoidance via a decentralized controller. IEEE Trans Autom Contr, 57(7):1827-1832.

[7]Kim Y, Mesbahi M, 2006. On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian. IEEE Trans Autom Contr, 51(1):116-120.

[8]Kim Y, Gu DW, Postlethwaite I, 2010. Robust target tracking using distributed unmanned aerial vehicle networks. Proc Inst Mech Eng Part G J Aerosp Eng, 224(4):417-426.

[9]Li TC, Fan HQ, García J, et al., 2019. Second-order statistics analysis and comparison between arithmetic and geometric average fusion: application to multi-sensor target tracking. Inform Fus, 51:233-243.

[10]Lim S, Kim Y, Lee D, et al., 2013. Standoff target tracking using a vector field for multiple unmanned aircrafts. J Intell Robot Syst, 69(1-4):347-360.

[11]Liu QY, Wang ZD, He X, et al., 2018. On Kalman-consensus filtering with random link failures over sensor networks. IEEE Trans Autom Contr, 63(8):2701-2708.

[12]Liu S, Zhang HM, 2011. Optimal Estimation Theory. Science Press, Beijing (in Chinese).

[13]Ma LL, Hovakimyan N, 2013. Cooperative target tracking in balanced circular formation: multiple UAVs tracking a ground vehicle. American Control Conf, p.5386-5391.

[14]Olfati-Saber R, 2006. Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Contr, 51(3):401-420.

[15]Olfati-Saber R, 2007a. Distributed Kalman filtering for sensor networks. Proc 46^th IEEE Conf on Decision and Control, p.12-14.

[16]Olfati-Saber R, 2007b. Distributed tracking for mobile sensor networks with information-driven mobility. American Control Conf, p.728-734.

[17]Olfati-Saber R, 2009. Kalman-consensus filter: optimality, stability, and performance. Proc 48^th IEEE Conf on Decision and Control held jointly with 28^th Chinese Control Conf, p.7036-7042.

[18]Olfati-Saber R, Jalalkamali P, 2012. Coupled distributed estimation and control for mobile sensor networks. IEEE Trans Autom Contr, 57(10):2609-2614.

[19]Oriolo G, de Luca A, Vendittelli M, 2002. WMR control via dynamic feedback linearization: design, implementation, and experimental validation. IEEE Trans Contr Syst Technol, 10(6):835-852.

[20]Stachura M, Frew EW, 2011. Cooperative target localization with a communication-aware unmanned aircraft system. J Guid Contr Dynam, 34(5):1352-1362.

[21]Tanner HG, Jadbabaie A, Pappas GJ, 2007. Flocking in fixed and switching networks. IEEE Trans Autom Contr, 52(5):863-868.

[22]Wang L, Wang XF, Hu XM, 2015. Connectivity maintenance and distributed tracking for double-integrator agents with bounded potential functions. Int J Robust Nonl Contr, 25(4):542-588.

[23]Wang YT, Li JB, Sun Q, 2013. Coordinated target tracking by distributed unscented information filter in sensor networks with measurement constraints. Math Probl Eng, 2013:402732.

[24]Wen G, Duan Z, Su H, et al., 2012. A connectivity-preserving flocking algorithm for multi-agent dynamical systems with bounded potential function. IET Contr Theory Appl, 6(6):813-821.

[25]Yan MD, Zhu X, Zhang XX, et al., 2017. Consensus-based three-dimensional multi-UAV formation control strategy with high precision. Front Inform Technol Electron Eng, 18(7):968-977.

[26]Yang P, Freeman RA, Gordon GJ, et al., 2010. Decentralized estimation and control of graph connectivity for mobile sensor networks. Automatica, 46(2):390-396.

[27]Yu ZQ, Liu ZX, Zhang YM, et al., 2019. Decentralized fault-tolerant cooperative control of multiple UAVs with prescribed attitude synchronization tracking performance under directed communication topology. Front Inform Technol Electron Eng, 20(5):685-700.

[28]Zavlanos MM, Pappas GJ, 2007. Potential fields for maintaining connectivity of mobile networks. IEEE Trans Robot, 23(4):812-816.

[29]Zavlanos MM, Pappas GJ, 2008. Distributed connectivity control of mobile networks. IEEE Trans Robot, 24(6):1416-1428.

[30]Zavlanos MM, Tanner HG, Jadbabaie A, et al., 2009. Hybrid control for connectivity preserving flocking. IEEE Trans Autom Contr, 54(12):2869-2875.

[31]Zavlanos MM, Egerstedt MB, Pappas GJ, 2011. Graph-theoretic connectivity control of mobile robot networks. Proc IEEE, 99(9):1525-1540.