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A Radar Multi-target Tracking Algorithm Based on Gaussian Mixture PHD Filter under Doppler Blind Zone
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摘要: 在多普勒雷达目标跟踪过程中，由于多普勒盲区(DBZ)的存在使得跟踪问题更为复杂。针对该问题，该文基于高斯混合概率假设密度(GM-PHD)提出了一种适用于多普勒盲区的多目标跟踪算法。该算法在常规检测概率模型中引入最小可检测速度(MDV)信息，并将该检测概率模型应用于传统GM-PHD更新方程中，推导出多普勒盲区下的GM-PHD更新方程。蒙特卡罗仿真实验结果表明：与只有多普勒量测信息的传统GM-PHD算法相比，新算法在较小的MDV条件下能够明显提高雷达对运动目标的跟踪性能。
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关键词:
- 多普勒盲区 /
- 最小可检测速度 /
- 多普勒信息 /
- 高斯混合概率假设密度
Abstract: Due to the Doppler Blind Zone (DBZ), the target tracking of Doppler radar becomes more and more complicated. In this paper, a multi-target tracking algorithm based on Gaussian Mixture Probability Hypothesis Density (GM-PHD) for DBZ is proposed. The algorithm introduces the Minimum Detectable Velocity (MDV) information to the traditional detection probability model to update the GM-PHD and the updated equation of the GM-PHD is deduced. The simulation results show that, compared to the traditional GM-PHD with the only Doppler measurement, the proposed algorithm improves greatly the radar tracking performance of moving target under the condition of minor MDV. -
图 1 传感器/目标几何杂波率为12.6×10–6时杂波分布
Figure 1. Sensor/target clutter distribution when the geometric clutter rate is 12.6×10–6
图 2 两目标的多普勒与不同的MDVs的时间关系
Figure 2. The relationship of two goals Doppler with diffierent MDVs
图 3 真实航迹与不同算法的估计(MDV=1)
Figure 3. Real track and estimation of different algorithms (MDV=1)
图 4 不同滤波器跟踪性能比较(MDV=1)
Figure 4. Comparison of tracking performance of different fliters (MDV=1)
图 5 不同滤波器跟踪性能比较(MDV=3)
Figure 5. Comparison of tracking performance of different fliters (MDV=3)
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