Adaptive Resource Optimization Management Of Phased Array Radar

Phased array radar has the beam without inertial scanning capability,which can achieve a high-speed change of parameters and direction of the beam.It is the basis for flexibly performing various tasks and fulfilling the requirements of complex battlefield environment.With more tasks to perform,the optimization of allocating resources for each task under the constraints of limited time and energy resources has become the fundamental question to exploit the full performance of phased radar systems.The radar processing flow of the target in the airspace is as follows: discover and confirm the target through scanning and search,establish the tracking of the target,and transmit the detected sampling of the target with other relevant task requests to the scheduling module,determine the scheduling properties and execution time of the task,and each task will be performed in accordance with the scheduled sequence.This thesis will focus on the flow to study each module's adaptive resource optimization management.First,the resource optimization management under the search model is studied,including the method of beam position arrangement,the search parameter adjustment strategy without priori information,and the search strategy under the guidance of priori information.The thesis will introduce and compare the effects of detection distance change,airspace revisit interval change,and both changes on radar detection performance as the proportion of search resources alters.Then the thesis introduces a search strategy under the guidance of targets' priori information: in conformity with different search data rates established on each beam and the corresponding probability value of the target,the average time of radar on identifying the target will be reduced.Moreover,the thesis assesses the resource optimization management problem under the single-target tracking and the multi-target tracking modes.Three adaptive update interval methods will be reviewed and compared with the simulation based on the typical tracking algorithm introduced.Regarding resource management of the multi-target tracking mode,the paper will be based on the concept of covariance control and developments from this point will be proposed.The sampling interval control will be added to the control value selection process,and the expected covariance will be changed by tracking residual adaptive.Through simulation verification,the improved covariance algorithm effectively prevents the imbalance of control values,and further optimizes the employment of time resources.Finally,the thesis studies the adaptive task scheduling algorithm.To clarify the effect of priority allocation criteria on scheduling results,based on the traditional adaptive scheduling algorithm,task scheduling will be simulated and compared under three priority allocation criteria.In order to compare the effect of different scheduling benefit models on scheduling results,a genetic algorithm will be adopted to determine the optimal scheduling sequence.Accordingly,the thesis simulates and compares the scheduling performance indexes under four fitness functions,and therefore analyze the optimal fitness function under different scenarios.Considering the insufficient utilization problem of time resources in uneven task assignment under the traditional time pointer algorithm and the high time-shifting rate problem of the improved time pointer algorithm,an improved time pointer algorithm based on the limitation of typical parameters is proposed.The simulation results show that the algorithm can effectively reduce the execution time-shifting rate,particularly for high-priority tasks.