To address the issue of poor tracking accuracy of the complex maneuvering target under limited bandwidth, an event-based hybrid consensus interactive multi-model (IMM) information filter (EHC-IMMIF) is proposed for air maneuvering target tracking in this paper. By combining the approaches of consensus on estimated information and unbiased converted measurement consensus, distributed maneuvering target estimation is achieved, balancing the improvement of estimation accuracy against the reduction of calculation load from consensus iteration steps. Furthermore, to alleviate network communication burden, a distributed event-triggering mechanism based on neighbor node information is designed for IMM estimation. This mechanism adaptively determines that communication data should be transmitted under pre-defined triggering conditions. Finally, simulation results of four UAVs cooperative tracking a maneuvering target are exhibited, verifying the proposed algorithm can achieve accurate and effective tracking performance under the limited communication resources.
GuoQTengL. Maneuvering target tracking with multi-model based on the adaptive structure. IEEJ T Electr Electr2022; 17(6): 865–871.
2.
LiBYangZPChenDQ, et al.Maneuvering target tracking of UAV based on MN-DDPG and transfer learning. Def Technol2021; 17(2): 1–10.
3.
HuangFJLiXXWuPL. Multi-missile cooperative control based on ESO for simultaneous. In: International conference on guidance, navigation and control, Tianjin, China, 30 October 2021, pp. 1375–1378.
4.
ChoMHTahkMJ. Decorrelated debiased converted measurement Kalman filter for phased array radar tracking system. IEEE T Aero Elec Sys2021; 58(1): 581–595.
5.
WeiYHongTKhelloufiA, et al.An application research of Kalman filter based algorithms in ECEF coordinate system for motion models of sensors. Procedia Comput Sci2019; 147: 574–580.
6.
LiSChengYMGouB, et al.Sequential unbiased converted measurement non-linear filter with range rate in ECEF coordinates. IET Radar Sonar Nav2020; 14(7): 1113–1124.
7.
ModalavalasaNRaoGSBPrasadKS, et al.A new method of target tracking by EKF using bearing and elevation measurements for underwater environment. Robot. Auton Syst2015; 74: 221–228.
8.
JulierSJUhlmannJK. Unscented filtering and nonlinear estimation. P IEEE2004; 92(3): 401–422.
9.
ArasaratnamIHaykinS. Cubature Kalman filters. IEEE T Automat Contr2009; 54(6): 1254–1269.
10.
BelfadelDBar-ShalomYWillettP. Single space-based sensor bias estimation using a single target of opportunity. IEEE Trans Aero Electron Syst2020; 56(3): 1676–1684.
11.
SuchomskiP. Explicit expressions for debiased statistics of 3D converted measurements. IEEE Trans Aero Electron Syst1999; 35(1): 368–370.
12.
BordonaroSWillettPBar-ShalomY. Decorrelated unbiased converted measurement Kalman filter. IEEE Trans Aero Electron Syst2014; 50(2): 1431–1444.
13.
LerroDBar-ShalomY. Tracking with debiased consistent con verted measurements versus EKF. IEEE T Aero Elec Sys1993; 29(3): 1015–1022.
14.
HeSWuPLLiXX, et al.Adaptive modified unbiased minimum-variance estimation for highly maneuvering target tracking with model mismatch. IEEE T Instrum Meas2023; 72(1): 1–16.
15.
ElsonbatyAREl-SayedAMA. Further nonlinear dynamical analysis of simple jerk system with multiple attractors. Nonlinear Dyn2017; 87(2): 1169–1186.
16.
MehrotraKMahapatraPR. A Jerk model for tracking highly maneuvering targets. IEEE T Aero Elec Sys1997; 3(4): 1094–1105.
17.
LiXBar-ShalomY. Performance prediction of the interacting multiple model algorithm. IEEE T Aero Elec Sys1993; 29: 755–771.
18.
ShanGLXuGGQiaoCL. A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control. Def Technol2019; 16(1): 242–250.
19.
BattistelliGChisciL. Kullback-Leibler average consensus on probability densities and distributed state estimation with guaranteed stability. Automatica2014; 50(3): 707–718.
20.
BattistelliGChisciLMugnaiG, et al.Consensus-based linear and nonlinear filtering. IEEE T Automat Contr2015; 60(5): 1410–1415.
21.
ZhaoY. Cubature-extended hybrid Kalman filtering method and its application in PPP/IMU tightly coupled navigation systems. IEEE Sens J2015; 15(12): 6973–6985.
22.
HuangZHChenSJHaoCP, et al.Bearings-only target tracking with an unbiased pseudo-linear Kalman filter. Remote Sens2021; 13: 2915–2933.
23.
WangYWangXGCuiNG. Hybrid consensus-based distributed pseudomeasurement information filter for small UAVs tracking in wireless sensor network. IET Radar, Sonar Navig2020; 14(4): 556–563.
24.
BattistelliGChisciL. Stability of consensus extended Kalman filter for distributed state estimation. Automatica2016; 68: 169–178.
25.
ChenQYinCZhouJ, et al.Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks. IEEE Sens J2018; 18(1): 4561–4569.
26.
LiWDuJJiaY. Event-triggered Kalman consensus filter over sensor networks. IET Control Theory & Appl2016; 10(1): 103–110.
27.
Martinez-ReyMEspinosaFGardelA, et al.On-board event-based state estimation for trajectory approaching and tracking of a vehicle. Sensors2015; 15: 14569–14590.
28.
LinHLiSLiuG. Cubature Kalman filter based on event-triggered mechanism. Chin Space Sci Technol2018; 38(4): 11–19.
29.
JenabzadehASafarinejadianB. A Lyapunov-based distributed consensus filter for a class of nonlinear stochastic systems. Automatica2017; 86: 53–62.
30.
AssimakisNAdamMDouladirisA. Information filter and Kalman filter comparison: selection of the faster filter. Int J Inf Technol We2012; 2(1): 1–5.
31.
AstromKBernhardssonB. Comparison of periodic and event-based sampling for first order stochastic systems. In: Proceedings of IFAC world congress, USA, 2002, pp. 2011–2016.
