Gao Han, Li Jingwen
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2014 Jun 19;14(6):10829-45. doi: 10.3390/s140610829.
A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.
本文提出了一种利用合成孔径雷达（SAR）图像检测和跟踪移动目标的新方法。该方法通过基于粒子滤波器（PF）的检测前跟踪（TBD）算法实现，能够利用SAR系统检测和跟踪低信噪比（SNR）的移动目标，而传统的检测后跟踪（TAD）方法对此无能为力。通过将SAR移动目标的信号模型纳入算法，在跟踪过程中解决了目标方位位置和径向速度的模糊性，从而直接得到真实估计。用子区域代替整个区域来计算似然比，并适当选择粒子数量，在检测和跟踪性能损失很小的情况下提高了计算效率。通过蒙特卡罗试验验证了该方法的可行性并评估了其性能。结果表明，所提出的方法能够检测和跟踪SNR低至7 dB的移动目标，并且在SNR低于14 dB时优于传统的TAD方法。
