Consensus based target tracking against deception jamming in distributed radar networks

In this study, an anti-deception jamming target tracking algorithm based on the data-level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non-collaborative and collaborative false targets respectively. For non-collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti-deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

Automated summary

In this study, an anti-deception jamming target tracking algorithm based on the data-level fusion is proposed in a distributed radar network. The algorithm improves tracking performance by introducing the consensus theory to covariance intersection fusion and utilizes unscented Kalman filter for distributed filtering. Two distributed target discrimination schemes are developed for non-collaborative and collaborative false targets respectively.