## 1 Introduction
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. For signal-level fusion methods, the targeted whole or partial echo signals are sent to the fusion center for fusion processing from each receiver. The signal-level fusion methods are mainly based on the difference of spatial scattering characteristics between physical and active false targets. For the physical target, its echoes are de-correlated in distributed radar stations due to its RCS fluctuating randomly with a different line of sight (LOS) [23; 24]. But for the active false target with the same source, although the transmission loss and received gain may vary with receivers, its jamming signals are still highly correlated in each receiver [25]. Based on the difference of spatial scattering characteristics, interference cancellation [26], jamming blocking [27] and correlation test [28; 29; 30] are proposed to suppress jamming signals or discriminate false targets. The cluster analysis method [31] and convolutional neural network [32] are also introduced to feature extraction and target discrimination. Although the signal-level fusion methods have better performance than the data-level fusion methods, the former puts forward higher requirements for system synchronization and information transmission. Moreover, they are not two completely separate parts. 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. The signal model and measurement model are first established for multistatic radar systems. According to the spatial correlation difference in physical target and active false target motion states, the statistical properties of the deception velocity are dealt with for the physical target and false target. It is found that the deception velocity obtained from the radial velocity of the physical target follows a Gaussian distribution with a zero mean. Conversely, for the active false target, the deception velocity conforms to a Gaussian distribution with a mean equivalent to its true deception velocity. Based on this fact, a data-level fusion method is proposed to discriminate the active false target and physical target by deception velocity testing. The proposed deception velocity-based method can keep a constant misjudgment probability for physical targets and discriminate targets effectively, especially in large deception velocity cases. The paper is organized as follows. Section 2 builds the signal model and measurement model for the multistatic radar system with joint range-velocity deception. The location information association method and the proposed deception velocity are dealt with in Section 3. The simulation results and analyses are provided in Section 4 to verify the validity and superiority of the proposed method. Section 5 draws the conclusions.