Consensus based target tracking against deception jamming in distributed radar networks

Abstract

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.

Parameter Estimation of Communication Radar Fusion System Based on Orthogonal Frequency Division Multiplexing Technology

“…On the ground of the differences in false target collaboration methods, two recognition schemes were designed on the ground of the nearest neighbor algorithm and consensus algorithm. The results indicate that this algorithm can effectively achieve physical target tracking and has good accuracy [10]. Cenkeramaddi L R scholar combines machine learning estimation with field of view enhancement technology to improve radar field of view performance.…”

“…Considering the constraint of factor vectors in the decomposition problem, this study conducts parameter correction from two aspects: parameter estimation and factor vector reconstruction. It is mathematical expression is shown in equation (10).…”

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

“…Location information association and velocity vector association (VVA) are performed successively to enhance the ability to discriminate the range-velocity joint deception false targets for active radar networks in [19]. Besides the above dot fusion methods for centralized fusion structure, many ECCMs are also studied for distributed fusion architecture within the target tracking process against false targets in [20][21][22]. Compared with the track fusion methods, the dot fusion methods make use of more raw measurement information.…”

“…The data-level fusion methods can be the preprocessing of the signal-level fusion methods, providing and lessening the discrimination scope of the latter. Compared with signal-level fusion methods, the data-level fusion methods are generally more concise and intuitive and easy to engineer implementation with [21,22]. Therefore, a data-level fusion method is proposed to counter the active deception jamming in this paper.…”

Range Deception Jamming Performance Evaluation for Moving Targets in a Ground-Based Radar Network

“…The deceptively active jamming receives the radar transmission signal and then changes the range, angle, or velocity information through jamming modulation. Then, the jammer transmits the jamming signal in order to degrade the radar detection performance [9]. For a single radar system, range deception jamming may easily disturb the radar system via reasonably designing the range deception parameters.…”