This paper propose a cognitive FDA-MIMO radar network (CFDA-MIMON) target discrimination and tracking algorithm under main-lobe deceptive trajectory interference. This algorithm can be divided into two stages: search-discrimination-initialization and cognitive target tracking. The first stage mainly uses the distribution characteristics of target and main-lobe deceptive trajectory interference on different radars to discriminate the target and interference in the radar network. A target state initialization strategy driven by G-pair measurements with large time interval is proposed for target tracking. In the second stage, a cognitive target tracking algorithm of single FDA-MIMO radar system is designed based on the criteria of maximum Capon power spectrum (CMCP). In addition, in order to avoid the divergence problem of the EKF filter, a target state correction method (TSC) based on auxiliary particles is proposed. Finally, the fusion strategy of multiple radar trajectories is used to alleviate the estimation error of the single radar. Numerical experiments verify that the proposed CFDA-MIMON target discrimination and tracking algorithm can distinguish the target from main-lobe deceptive trajectory interference without prior information of the target, and output the target's trajectory.

In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is rendered ineffective due to occurrences of frequency spectrum interference and main-lobe deceptive interference with arbitrary time delays. Therefore, a cognitive FDA-MIMO radar network (CFDA-MIMORN) transmit element selection algorithm is introduced. At first, the target is discriminated from the false targets. The Kalman filter is used to track the target, then available information is used to infer the target’s position in the next time step. The finite transmit elements of the radar network are organized to enhance tracking performance, especially in the presence of frequency spectrum interferences. The numerical simulations demonstrate that the proposed CFDA-MIMORN can effectively discriminate the true target from false targets, and optimize the allocation of transmit elements to avoid interferences, resulting in improved tracking accuracy.

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).