多机协同的机载雷达组网联合目标探测可有效提高复杂电磁干扰环境下对隐身弱目标的探测能力。文中针对机载雷达组网探测时空间配准误差大、协同探测难以实现的难题,提出了一种基于轨迹空间配准的协同目标检测算法,通过雷达间少量距离-多普勒域数据及低检测门限下目标轨迹域数据的交互,采用极大似然估计广义似然比检测器对目标进行联合恒虚警检测(CFAR),并通过轨迹域空间配准与CFAR 的迭代计算,实现配准精度和目标联合检测性能的双提升。数值仿真实验的结果表明,在四部雷达组网工作时,在相参积累后信噪比9 dB、虚警概率10-4的典型场景下,经过迭代处理,空间配准精度可达到一个距离-多普勒分辨单元;对目标的检测概率由单部雷达的28. 5%提高到四部雷达协同下的83. 67%。

Joint target detection with networked radars can effectively improve the detection ability to stealth weak targets in complex electromagnetic jamming environment. However, it is especially hard to be netted for airborne radars due to the problems of large spatial registration error. In this paper, a cooperative target detection algorithm based on trajectory spatial registration is proposed for airborne networked radars systems. The maximum likelihood estimation generalized likelihood ratio detector is adopted for joint constant false-alarm rate(CFAR)detection of targets with the utilization of a small amount of range-Doppler domain data and target trajectory domain data under low detection threshold from radars nearby. Through the iterative calculation of spatial registration and joint CFAR detection, both of registration accuracy and joint target detection performance is improved. The results of numerical simulation experiments show that the spatial registration accuracy can be limited in a range-Doppler resolution cell. The detection probability is increased from 28. 5% for a single radar to 83. 67% for the four networked radars after iterative processing under the typical scenario with a signal-to-noise ratio of 9 dB after coherent integration and a false alarm probability of 10-4 when four radars are networked.

TN953

国家自然科学基金资助项目(61971224)