This paper proposes a multi-dimensional resource management (MRM) scheme for multifunctional radar systems that aims to improve multiple target tracking accuracy under dynamic jamming environments. The proposed scheme constructs an optimization model to coordinate the radar's power and frequency resources. To combat dynamic jammers, we present a Reinforcement Learning based active anti-jamming Markov Decision Process model. The resulting signal to interference plus noise ratio and the Bayesian Cramer-Rao lower bound under jamming environments are calculated, and a multiple target tracking performance optimization model is built for the MRM scheme. We show that the MRM problem can be decoupled as two subproblems, one for combating dynamic jammer and the other for resource allocation. A two-step solution technique is developed to solve the resulting optimization subproblem based on decoupling analysis, and the obtained solution is proven to maximize target tracking performance. Simulation results indicate that the proposed method can evidently beat the dynamic jamming and improve the target tracking accuracy within a given power budget.