Research On Antenna Selection For Distributed MIMO Radar

Compared with the traditional radar,MIMO radar has obvious advantages in parameter estimation,anti stealth,anti interception and anti-jamming etc.But the high system complexity has become the bottleneck of its development and application.How to optimize the configuration of MIMO radar's receiving and transmitting antennas reasonably,and how to reduce the resource occupation and calculation cost as much as possible while ensuring the system performance,have important research value.In this paper,aiming at the optimization of antenna selection in distributed MIMO radar system,the establishment and solution of antenna selection optimization model are studied for different application scenarios such as single task,target tracking with the same priority in multitask and target tracking with different priority in multitask etc.The main work includes:1.In the single task and multi-target tracking scenario,aiming at the problem that the complexity of the existing antenna selection algorithm is high,which is not conducive to the fast tracking of the target,the finite antenna selection algorithms based on the attenuation sort increasing(ASI)and the attenuation sort decreasing(ASD)for multi-target are given respectively.The optimization model of 0-1 integer programming is established with the minimum number of antennas as the optimization objective and the position estimation accuracy of multi-target as the constraint.Both algorithms are based on the signal attenuation sorting criteria.The ASI algorithm increases antennas successively based on tthe lowest attenuation receiving and transmitting antenna set,the ASD algorithm reduces antennas successively based on the whole antennas set.The increase or decrease of antennas will be stopped when the system performance is just satisfied.The simulation results show that the proposed algorithms can ensure the system performance while effectively reducing the calculation amount of the system and can be applied to different application scenarios.Under simulation conditions,compared with the comparison algorithm,the ASI algorithm and the ASD algorithm reduce the computation by 79% and 57% respectively.2.Aiming at the problem of antenna selection in multitask of targets tracking with the same priority,an antenna selection algorithm based on indicator change rate(ICR)is proposed.Two application scenarios are discussed.One is to minimize the number of antennas and build the least antenna optimization model under the given system tracking and detection performance requirements.The other is to improve the performance of the system as much as possible and build the optimal performance optimization model under the given number of antennas.In the solution of the two scenarios,the dimensional differences between detection probability and tracking accuracy indicators are eliminated.Based on all the antennas,the relative change rate of different indicators is linearly weighted to comprehensively measure the contribution of the antennas to the system performance,and the antenna with the least contribution to the system performance is successively eliminated until the constraint conditions are approached or reached.The simulation results show that the proposed algorithm can guarantee the overall performance of the system and reduce the amount of computation.Under simulation conditions,compared with the comparison algorithm,the reduction can reach more than 86%.3.Aiming at the problem of antenna selection in multitask of targets tracking with multi priority,the algorithm of antenna selection based on modified fair multi-start local search(MFMLS)and the algorithm of antenna selection based on modified greedy multi-start local search of multitask(MT_MGMLS)are proposed respectively.The optimization model is established by taking the minimum number of antennas as the objective function.After parameter preprocessing,in antenna selection,the MFMLS algorithm traverses each initial antenna set in the antenna selection,while the MT_MGMLS algorithm searches for the optimal initial antenna set through traversal,and then increases the optimal antenna step by step to complete the element selection.The simulation results show that the two algorithms can both guarantee the overall performance of the system and reduce the amount of computation,to meet different application requirements.