Secure multitarget tracking over decentralized sensor networks with malicious cyber attacks

This paper is concerned with the multitarget tracking over decentralized sensor networks where the network can potentially be compromised by malicious cyber attacks. We consider the hybrid cyber attacks, including denial of service (DoS), false data injection (FDI), and extra packet injection (EPI) attack. We first establish the feature model of DoS, FDI and EPI attacks for decentralized multitarget tracking. Then, we propose a decentralized multitarget tracking algorithm against DoS, FDI and EPI attacks, which consists of three phases: prediction, adaptation and combination. The adaptation phase is to update the estimate of each node with its own measurements and all its neighbors' measurements. The combination phase is to fuse the estimate of each node with all its neighbors' estimates. By incorporation of the neighbors' measurements and fusing the neighbors' estimates, it can dramatically reduce the adverse effect of cyber attacks and provide reliable tracking performance. Numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.