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.

Considering a new type of netted radar system, the receiver can process the reflected echoes transmitted by all transmitters. Compared with networked radar system, power and beam resources of netted radar system are still limited, and more importantly, the number of transmitting and receiving (TR) beams has increased dramatically. In this paper, a joint transmitter/receiver beam selection and power allocation (JTRBS-PA) strategy is developed for multiple targets tracking in netted radar system. The JTRBS-PA strategy utilizes the target prior information and predicted accuracy to optimize the TR beam selection and the power of each transmitting beam, which aims to maximize target tracking performance under limited radar system resources. A two-step solution technique is proposed to deal with the coupled bivariate constrained optimization problem. Simulation results demonstrate the effectiveness and superiority of the proposed strategy.

In order to enhance the resource utilisation of the netted radar system, a receiving resource allocation (RRA) strategy for target localisation is developed in this article. Receiving resource refers to the receiving node and the processing approach adopted. The aim of the proposed RAA strategy is to select the receiver node and corresponding processing approach to minimise the resource cost while satisfying the desired target localisation accuracies. The resource cost includes the implementation cost of the processing approaches and the distance cost of data transmission. The Cramer‐Rao bound (CRB) incorporating the above controllable parameters is derived and used as the target localisation performance metric in the netted radar system. Whereafter, the RRA problem is modelled as a non‐convex constrained optimisation problem with the discrete variables. A scheme based on the modified depth‐first search is proposed to solve this problem. Simulation results demonstrate that the proposed scheme can effectively reduce resource consumption under the desired target localisation performance constraints.