雷达带宽增加带来的回波信息增量,能够更准确地辅助环境感知并获取目标信息。但是,雷达带宽的提高通常会造成目标回波在距离维的扩展,进而造成目标回波能量的分散,使单个距离单元的回波信噪比下降,不利于目标的远距离探测。此时,通过融合连续多个距离单元的回波进行检测能够提升目标探测性能。为了更充分地利用目标回波信息设计融合检测器,文中结合认知探测的思想,利用跟踪状态下的目标先验信息设计了跟踪信息辅助的扩展目标检测算法。本算法首先基于扩展目标的跟踪信息预测目标位置及其回波在各距离单元的分布,再基于预测信息设计融合检测器,以此实现从跟踪到检测的闭环,更充分地挖掘和利用了历史目标回波信息。实验表明:所设计的跟踪信息辅助的扩展目标检测算法相较于传统扩展目标检测算法,能够提升目标检测性能,推远雷达对目标的有效跟踪距离。

Environmental awareness and acquisition of target information can be more accurately aided by the incremental echo information from increased radar bandwidth. However, the expansion of target echoes in the distance dimension is usually caused by the increase in radar bandwidth. Then the dispersion of target echo energy is caused and the signal-to-noise ratio of echoes in a single distance unit is degraded, which is not conducive to the long-range target detection. In this case, the target detection performance can be improved by fusing the echoes of multiple consecutive distance units. In order to design a fusion detector by making full use of the target echo information, the idea of cognitive detection is combined by this paper and a tracking-aided detection for extended targets algorithm using the information in the tracking state is designed. Firstly, based on the tracking information of the extended target, the target position and its echo distribution at each distance unit are predicted by the algorithm. Then, based on the predicted information, the fusion detector is designed. In this way, a closed loop from tracking to detection is achieved, and the historical target echo information is more fully exploited and utilized. Experimental results show that compared with the traditional extended target detection algorithm, the target detection performance can be improved by the algorithm designed in this paper and the target tracking distance can be farther.

TN957.51

国家自然科学基金资助项目