Distributed Jammer-Equipped Target Tracking with Hybrid Extended Kalman and Particle Filter in Sensor Network
محورهای موضوعی : Majlesi Journal of Telecommunication DevicesIman Maghsudlu 1 , Meysam Raees Danaee 2 , Hamid Arezumand 3
1 - Phd Candidate of Electrical Engineering, Faculty of Electrical Engineering, Imam Hossein University, Tehran, Iran
2 - Assistant Professor, Faculty of Electrical Engineering, Imam Hossein University, Tehran, Iran
3 - Researcher, Faculty of Electrical Engineering, Imam Hossein University, Tehran, Iran
کلید واژه:
چکیده مقاله :
In this paper, we propose a distributed tracking algorithm for a jammer-equipped target that passes through the nodes of a wireless sensor network (WSN). The jammer that the target carries is of the deceptive type, which means that it can mimic the signal of the target and confuse the sensors. Unlike other existing methods, our proposed algorithm does not require any additional hardware installation on each WSN node. It only relies on signal processing and solving the average consensus problem to detect the presence of jamming effects in the observations of the contaminated nodes and exclude them from the distributed tracking problem. For the distributed tracking problem, we use the hybrid extended Kalman filter (EKF) and particle filter to reduce the number of parameters needed for solving the average consensus problem and to decrease the communication overhead. The simulation results show that our algorithm improves the tracking performance compared to the case where the nodes with jammed observations are used.
In this paper, we propose a distributed tracking algorithm for a jammer-equipped target that passes through the nodes of a wireless sensor network (WSN). The jammer that the target carries is of the deceptive type, which means that it can mimic the signal of the target and confuse the sensors. Unlike other existing methods, our proposed algorithm does not require any additional hardware installation on each WSN node. It only relies on signal processing and solving the average consensus problem to detect the presence of jamming effects in the observations of the contaminated nodes and exclude them from the distributed tracking problem. For the distributed tracking problem, we use the hybrid extended Kalman filter (EKF) and particle filter to reduce the number of parameters needed for solving the average consensus problem and to decrease the communication overhead. The simulation results show that our algorithm improves the tracking performance compared to the case where the nodes with jammed observations are used.
