A novel adaptive sampling interval algorithm for multitarget tracking is presented.This algorithm which is based on interacting multiple models incorporates the grey relational grade(GRG) into the particle swarm optimization(PSO).Firstly,the desired tracking accuracy is set for each target.Secondly,sampling intervals are selected as particles,and then the advantage of the GRG is taken as the measurement function for resource management.Meanwhile,the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy.Finally,it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest.Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar. A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the gray relational grade (GRG) into the particle swarm optimization (PSO). Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally , it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.