高速弱小目标检测的并行处理方法与性能分析

Parallel Processing Technique for High Speed Small Target Detection and Performance Analysis

长时间相干积累是一种提高高速弱小目标检测性能的有效手段。针对传统的长时间相干积累方法因为引入跨距离单元与跨多普勒单元因素面临的计算量巨大的问题,我们提出了在距离维以跨距离单元数目为索引的并行处理方法。首先,基于高速目标在一个相干处理间隔内的运动特性,本文建立了匀加速直线目标运动模型。然后,依据跨距离单元数,我们对回波数据进行并行重组,并沿慢时间维进行快速傅里叶变换处理,输出幅度超过第一门限的参数组合通过粒子群算法再在(距离,速度,加速度)维上对重组后的回波数据进行小范围搜索。算法输出对应的峰值与第二门限比较从而做出目标判决。该方法通过两次门限设置以及粒子群算法大大简化了搜索的复杂度与运算量,使算法更具有硬件可实现性。最后,我们给出了实验仿真,分析了积累损失上限,验证了该算法的有效性。

Long-time coherent integration is an effective way to improve high speed weak target detection performacne. In order to resolve the problems of large calculation load caused by across range units（ ARU） and across Doppler unit（ ADU）,a parallel processing technique is proposed in this paper. At first,the uniformly accelerated rectilinear motion model is established based on the motion feature of high speed target in a coherent processing interval. Then,the echoes are reorganized into multiple groups in parallel by the number of range units that the interested targets may walk across. Subsequently,Fast Fourier transform（ FFT） is executed for each groups along the slow time dimension and the first threshold is set to reduce the parameter searching range. Moreover,the particle swarm optimization（ PSO） is utilized to search the optimum parameters of the targets along（ range,velocity,acceration） dimensions and the second threshold is set to declare the existence of the target. By two-threshold and PSO methods,the proposed technique simplifies the search complexity,reduce the calculation load effectively,showing good hardware feasibility. Finally,the upper bound of integration loss is analyzed and the simulation results show its cauculation effectiveness.