In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.
ChenQWangWYinC (2016) Distributed state estimations based on cubature Kalman filtering. In: proceedings of the 28th Chinese control and decision conference, Yinchuan, China, 28–30 May 2016, pp.3384–3389. IEEE.
3.
ChenQYinCZhouJ, et al. (2018) Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks. IEEE Sensors Journal18(11): 4561–4569.
4.
CuiRChenLYangC, et al. (2019) Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities. IEEE Transactions on Industrial Electronics66(10): 8279-8280.
5.
DingZLiuYLiuJ, et al. (2018) Unscented information consensus filter for maneuvering target tracking based on interacting multiple model. In: 2018 21st international conference on information fusion, Cambridge, England, 10–13 July 2018, pp.1535–1540. IEEE.
6.
GaoZ (2003) Scaling and bandwidth-parameterization based controller tuning. In: proceedings of the 2003 American control conference, Denver, CO, USA, 4–6 June 2003, pp.4989–4996. IEEE.
7.
GeorgeJ (2019) Distributed tracking of maneuvering target: A finite-time algorithm. In: 2019 IEEE international conference on acoustics, speech and signal processing, Brighton, United Kingdom,12–17 May 2019. pp.4420–4424. IEEE.
8.
GuoB-ZZhaoZ-L (2011) On the convergence of an extended state observer for nonlinear systems with uncertainty. Systems & Control Letters60(6): 420–430.
9.
HanJ (1995) A class of extended state observers for uncertain systems. Control and Decision10(1): 85-88.
10.
HanJ (2009) From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics56(3): 900-906.
11.
HlinkaOHlawatschFDjuricPM (2013) Distributed particle filtering in agent networks. IEEE Signal Processing Magazine30(1): 61-81.
12.
Keshavarz-MohammadiyanAKhaloozadehH (2018) Consensus-based distributed unscented target tracking in wireless sensor networks with state-dependent noise. Signal Processing144: 283-295.
13.
LiSYangJChenW-H, et al. (2012) Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Transactions on Industrial Electronics59(12): 4792–4802.
14.
LiWJiaY (2012) Location of mobile station with maneuvers using an IMM-based cubature Kalman filter. IEEE Transactions on Industrial Electronics59(11): 4338–4348.
15.
LiWWeiGHanF, et al. (2016) Weighted average consensus-based unscented Kalman filtering. IEEE Transactions on Cybernetics46(2): 558-567.
16.
LiXRJilkovVP (2003) Survey of maneuvering target tracking. Part I: Dynamic models. IEEE Transactions on Aerospace and Electronic Systems39(4): 1333–1364.
17.
LiuYHeYWangH (2014) Squared-root cubature information consensus filter for non-linear decentralised state estimation in sensor networks. IET Radar Sonar and Navigation8(8): 931-938.
18.
Olfati-SaberR (2007) Distributed Kalman filtering for sensor networks. In: proceedings of the 46th IEEE conference on decision and control, New Orleans, LA, USA,12–14 December 2007, pp.1814–1820. IEEE.
19.
PakkiKChandraBGuD-W, et al. (2011) Cubature information filter and its applications. In: 2011 American control conference, San Francisco, CA, USA, 29 June–1 July 2011, pp.3609-3614. IEEE.
20.
RenWBeardRWAtkinsEM (2007) Information consensus in multivehicle cooperative control. IEEE Control Systems Magazine27(2): 71-82.
21.
WangDWangZLiG, et al. (2016) Distributed filtering for switched nonlinear positive systems with missing measurements over sensor networks. IEEE Sensors Journal16(12): 4940–4948.
22.
YuY (2017) Consensus-based distributed linear filter for target tracking with uncertain noise statistics. IEEEs Sensors Journal17(15): 4875–4885.
23.
ZhangDYuLZhangW-A (2015) Energy efficient distributed filtering for a class of nonlinear systems in sensor networks. IEEE Sensors Journal15(5): 3026-3036.
24.
ZhangHZhouXWangZ, et al. (2019a) Maneuvering target tracking with event-based mixture Kalman filter in mobile sensor networks. IEEE Transactions on Cybernetics1–12. DOI: 10.1109/TCYB.2019.2901515.
25.
ZhangHZhouXWangZ, et al. (2019b) Adaptive consensus-based distributed target tracking with dynamic cluster in sensor networks. IEEE Transactions on Cybernetics49(5): 1580–1591.
26.
ZhangZHuangCDingD, et al. (2019c) Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking. Nonlinear Dynamics97(2): 1227–1243.
