Robust target tracking using distributed unmanned aerial vehicle networks

Abstract

In this article, a target tracking scheme for operation in distributed unmanned aerial vehicle (UAV) networks in which sensors may read the target position incorrectly is proposed. The proposed scheme operates two algorithms concurrently: semi-decentralized dynamic data fusion and fault detection. The semi-decentralized dynamic data fusion algorithm employs a median-consensus algorithm using extended non-faulty neighbours (whose sensor readings for the target position are within a prescribed tolerance level) and subsequently makes local estimates of the target position converge to nearly the actual target position. Meanwhile, the fault detection algorithm first asks each UAV to find the global median of the local target position through extended neighbours, and then diffuses the determined global median to all the UAVs in the network. As a result, the fault detection algorithm allows UAVs to detect and isolate faulty sensors quickly and to carry on target tracking in the semi-decentralized dynamic data fusion mode. As opposed to existing target tracking schemes, the proposed scheme is deterministic and guarantees the complete detection and isolation of faulty sensors on UAVs and thus successful target tracking. Numerical examples as well as experimental results are provided to support the developed ideas.

Keywords

fault-tolerant control unmanned aerial vehicle multi-agent network median consensus extended neighbours

Get full access to this article

View all access options for this article.

References

Smith

Singh

‘Approaches to multisensor data fusion in target tracking: A survey’

IEEE Trans. Knowl. Data Engng 1812 (2006): 1696–1710

Ren

Beard

R. W.

Kingston

D. B.

‘Multi-agent Kalman consensus with relative uncertainty.’ In Proceedings of the American Control Conference, June 2005, pp. 1865–1870.

Speranzon

Fischione

Johansson

K. H.

‘Distributed and collaborative estimation over wireless sensor networks.’ In Proceedings of the 45th IEEE Conference on Decision and Control, December 2006, pp. 1025–1030.

Olfati-Saber

Shamma

J. S.

‘Consensus filters for sensor networks and distributed sensor fusion.’ In Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference, December 2005, pp. 6698–6703.

Spanos

Olfati-Saber

Murray

R. M.

‘Dynamic consensus on mobile networks.’ In Proceedings of the 16th IFACWorld Congress, Prague, Czech Republic, 2005.

Chen

Kher

Somani

‘Distributed fault detection of wireless sensor networks.’ In Proceedings of the 2006 Workshop on Dependability Issue in Wireless ad hoc Networks and Sensor Networks, Los Angeles, September 2006.

Ding

Chen

Xing

Cheng

‘Localized fault-tolerant event boundary detection in sensor networks.’ In Proceedings of the IEEE INFOCOM, Miami, USA, March 2005.

Franco

Olfati-Sabor

Parisini

Polycarpou

M. M.

‘Distributed fault diagnosis using sensor networks and consensus-based filters’. In Proceedings of the 45th IEEE Conference on Decision and Control, December 2006, pp. 386–391.

Kim

Mesbahi

‘On maximizing the second smallest eigenvalue of a state-dependent Laplacian’

IEEE Trans. Autom. Control 511 (2006): 116–120

10.

Luo

Dong

Huang

‘On distributed fault-tolerant detection in wireless sensor networks’

IEEE Trans. Comput. 551 (2006): 58–70

11.

Ruiz

L. B.

Siqueira

I. G.

Oliveira

L. B.

Wong

H. C.

Nogueira

J. M. S.

Loureiro

A. A. F.

‘Fault management in event-driven wireless sensor networks.’ In Proceedings of the MSWiM, Venezia, Italy, October 2004.

12.

Toregas

Swain

Revelle

Bergman

‘The location of emergency service facilities’

Oper. Res. 191 (1971): 1363–1373