一种降维空时自适应处理子阵划分方法

A Subarray Partition Approach for Reduced-Dimension Space-Time Adaptive Processing

针对机载预警雷达降维空时自适应处理的子阵划分问题,提出了一种基于蚁群算法的雷达阵面子阵划分方法。该方法在子阵数给定的情况下,以最大化子阵级空时自适应处理的改善因子为优化准则,利用蚁群算法搜索子阵间的分隔点,从而获取最优的子阵划分方式。子阵划分是一种组合优化问题,蚁群算法非常适合于解决这种问题。仿真结果表明,利用该方法划分子阵并进行子阵级空时自适应处理,其改善因子总体上仅比阵元级的约低2dB,空时自适应方向图无明显的栅瓣,副瓣也较低。

For the subarray partition problem of the reduced-dimension space-time adaptive processing in airborne early warning radar,a subarray partition approach based on ant colony algorithm（ACA）is presented in this paper.Under the condition that the number of subarrays is given,this approach,with its optimization criterion that the improvement factor（IF）at subarray level is maximized,employs the ACA to search for the proper dividing points between the subarrays and therefore yields the optimum subarray partition form.The subarray partition is a kind of combinational optimization problem,and the ACA is very suitable for solving this kind of problem.The simulation results show that the IF based on the above approach is totally only lower than that of the element level adaptive processing by about 2 dB,the grating lobes are not found and the sidelobes are also lower in the space-time adaptive pattern.