基于抛物线随机Hough变换的机载脉冲多普勒雷达机动弱目标检测前跟踪方法

为解决机载脉冲多普勒（PD）雷达对机动弱目标进行检测与跟踪的问题，构造了一种基于抛物线随机Hough变换（RPHT）的检测前跟踪（TBD）算法。该算法通过模糊区间对目标的模糊量测进行距离多假设扩展，从而提取量测中的时空相关信息。利用RPHT方法将目标扩展量测转换到参数空间，并在参数空间对目标的机动航迹进行积累。在算法实现过程中，通过方位变换的方式对传统的RPHT方法进行了改进，以降低算法计算复杂度。该方法将机动弱目标解距离模糊问题转换为TBD框架下基于RPHT的航迹检测问题，避免了微弱目标的低信噪比造成的航迹漏检。仿真结果验证了该算法的有效性。

A RPHT-TBD Algorithm for Tracking and Detecting Maneuvering Weak Target via Airborne PD Radar

To address the problem of maneuvering weak target detection and tracking with airborne pulse Doppler(PD) radar in medium and high PRF modes, a novel randomized parabolic Hough transform algorithm（RPHT） is proposed within the framework of track-before-detection (TBD). The ambiguous measurements are mapped into all the possible range ambiguity intervals by a multiple hypothesis ranging procedure, where the space-time relevance can be derived. RPHT is used to integrate all the information along the target trajectory in the parameter space. And an azimuth transformation method is introduced to RPHT, which significantly reduces the computational complexity of this algorithm. In such a way, the new approach transforms the problem of range ambiguity resolution into the decision of the real track for weak targets. Simulation results illustrate its effectiveness.