Game theoretic distributed waveform design for multistatic radar networks

We examine the interaction of multiple-input multiple-output (MIMO) based clusters of radars within a game theoretic framework, using potential games. The objective is to maximise the signal-to-disturbance ratio (SDR) of the clusters of radars, by selecting most appropriate waveforms. We prove that the proposed game theoretic algorithm converges to a unique Nash equilibrium using discrete concavity and the larger midpoint property. As a result, each cluster can determine the best waveform for illumination (equilibrium) by strategising the actions of the other clusters

Game-theoretic power allocation and the Nash equilibrium analysis for a multistatic MIMO radar network

CCBY We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output (MIMO) radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal to interference plus noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game (GNG) belongs to the framework of standard functions. The standard function property together with the proof of the existence of solution for the game guarantees the uniqueness of the Nash equilibrium. The mathematical analysis based on Karush-Kuhn-Tucker conditions reveal some interesting results in terms of number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system