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降维处理法在多维信号检测中虽然减少了计算量,简化了检测过程,但是将会导致系统检测性能的下降,这一点是实际应用所不允许的。在序列图像情况下,为提高算法的实时性,研究了一种基于时空分集理论的降维处理及实时检测技术:时间分集实现了从时空三维图像向二维空间的投影,从而目标运动轨迹的搜索空间维数从时空三维变成了二维;空间分集完成了目标能量的积累,从而提高了信噪比。为提高系统检测性能,对决策统计量的分布函数进行了变换,改造了分布形状,获得了有利于提高检测概率的分布。给出了具体实施方案,相应的理论分析和实验结果。
Dimensionality reduction method in multi-dimensional signal detection reduces the amount of computation and simplifies the detection process, but will lead to the decline of the system detection performance, which is not allowed by the practical application. In the case of sequence images, in order to improve the real-time performance of the algorithm, a technique of dimensionality reduction and real-time detection based on spatio-temporal diversity theory is studied: Time-diversity realizes the projection from spacetime 3D image to 2D space, The dimension of the search space has changed from three-dimensional space-time to two-dimensional; spatial diversity has completed the accumulation of target energy, thus improving the signal-to-noise ratio. In order to improve the system detection performance, the distribution function of decision statistics is transformed, the distribution shape is modified, and the distribution which is helpful to improve the detection probability is obtained. Given the specific implementation plan, the corresponding theoretical analysis and experimental results.