组网雷达多帧检测前跟踪算法研究

组网雷达系统(NRS)由于其稳健的性能优势，在近年来受到了广泛关注。目前，组网雷达系统在进行目标探测时常采用先检测后跟踪(DBT)算法，即在每个时刻先对接收到的回波数据进行单帧门限检测，得到疑似目标的点迹集合，然后上传这些点迹或由这些点迹跟踪得到的航迹估计到融合中心做进一步处理，最终得到全局估计结果。然而，当信噪比(SNR)比较低时，目标往往很难通过单帧门限检测，最终导致目标漏检、航迹起批难，无法有效发挥组网雷达系统优势。针对这一问题，该文提出了一种组网雷达多帧检测前跟踪(MF-TBD)算法。该方法首先在本地节点进行多帧检测前跟踪，然后传递检测得到的点迹序列到融合中心进行融合。该方法一方面利用了组网雷达系统平台优势；另一方面不同于常规先检测后跟踪技术，多帧检测前跟踪能够利用目标空时相关性积累目标能量，改善弱小目标检测性能；因此其可以有效提高系统对目标的检测性能。但是，多帧检测前跟踪输出结果和先检测后跟踪算法不同，导致现有融合方法不适用。针对这一问题，该文首先理论推导了点迹序列的融合方法，然后结合实际雷达模型给出了算法实现流程，最后提出了算法的粒子滤波实现方式并通过仿真实验验证了算法的性能。仿真结果证明该文提出的方法相比于先检测后跟踪算法，有4～6 dB的检测性能增益；相比于常规单传感器多帧检测前跟踪算法，航迹跟踪精度有50%左右的提升。 

Multi-frame Track Before Detect Method for the Netted Radar System

Recently, Netted Radar System (NRS) has received much attention due to its robust performance gain. Usually, the NRS Detect Before Track (DBT) method detects the received data at each time, acquiring a set of alarm plots, and then transmits these plots or the trajectories obtained based on them to the fusion center, thus generating a global estimated result. However, when the Signal-to-Noise Ratio (SNR) is low, the performance becomes highly degraded because the targets cannot pass the single-frame detection threshold of DBT. To solve this problem, a netted radar Multi-Frame Track Before Detect (MF-TBD) method is proposed in this paper. First, MF-TBD is performed in local radar nodes, and then it acquires estimated target plot sequences and transmits them to the center for further fusion. MF-TBD can take advantage of NRS, and also can utilize target space time correlation through MF-TBD processing and enhance the target SNR. Thus, it can improve detection performance. However, the outputs of MF-TBD are different from that of DBT. Therefore, the current fusion methods for DBT are not suitable for MF-TBD. To solve this problem, this paper first derives a fusion method for plot sequences, then reports its processing steps in radar system, and finally proposes an implementation method based on the particle filter. The simulation results show that the proposed method has a detection performance gain of 4 to 6 dB than the traditional method based on DBT, and a 50% gain on estimation accuracy than single-sensor MF-TBD.