A Multi-sensor Adaptive Observation Iteratively Updating GM-PHD Tracking Algorithm
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摘要: 针对多传感器观测数据质量不同且未知时，多传感器量测迭代更新高斯混合概率假设密度(GM-PHD)滤波器跟踪算法的结果对更新顺序敏感的问题，该文提出一种多传感器自适应量测迭代更新GM-PHD跟踪算法AIU-GM-PHD。首先基于多传感器融合一致性度量，提出一种用于在线评估各传感器跟踪结果质量的方法；然后对多传感器迭代融合顺序进行优化，最后构建相应的多传感器GM-PHD融合跟踪算法。为了解决多传感器自适应顺序迭代融合无法体现传感器质量差距的问题，提出了一种自适应带权伪量测迭代更新GM-PHD跟踪算法PAIU-GM-PHD。仿真结果表明，与常规多传感器迭代更新GM-PHD跟踪算法相比，所提算法能够获得鲁棒性更好、精度更高的跟踪结果。
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关键词:
- 多传感器多目标跟踪 /
- 随机有限集 /
- 自适应融合 /
- 高斯混合概率假设密度滤波器 /
- 量测迭代更新
Abstract: For the problem that the results of multi-sensor measurement iteratively updating Gaussian Mixture Probability Hypothesis Density (GM-PHD) tracking algorithm is sensitive to the updating order if the qualities of multi-sensor observation data are different and unknown, a multi-sensor Adaptive observation Iteratively Updating GM-PHD tracking algorithm (AIU-GM-PHD) is proposed. Firstly, based on the multi-sensor fusion consistency measure, a method is proposed to evaluate the online quality of each sensor's tracking results. Then, the sequence of multi-sensor iterative fusion is optimized. Finally, the corresponding multi-sensor GM-PHD fusion tracking algorithm is constructed. To solve the problem that the multi-sensor adaptive order iterative fusion can not reflect the sensor quality gap, an Adaptive Iteratively Updating GM-PHD tracking algorithm PAIU-GM-PHD with weighted pseudo measurements is proposed. The simulation results show that, compared with the conventional multi-sensor iterative update GM-PHD tracking algorithm, the proposed algorithms can obtain more robust and accurate tracking results. -
表 1 实验1场景设置
检测概率 杂波数量 场景1 $p_d^1 = 0.95,{\text{ } }p_d^2 = 0.85,{\text{ } }p_d^3 = 0.75,{\text{ } }p_d^4 = 0.65$ ${\lambda _1} = 2,0{\text{ } }{\lambda _2} = 20,{\text{ } }{\lambda _3} = 20,{\text{ } }{\lambda _4} = 20$ 场景2 $p_d^1 = 0.90,{\text{ } }p_d^2 = 0.90,{\text{ } }p_d^3 = 0.90,{\text{ } }p_d^4 = 0.90$ ${\lambda _1} = 20,{\text{ } }{\lambda _2} = 30,{\text{ } }{\lambda _3} = 40,{\text{ } }{\lambda _4} = 50$ 
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表 2 实验2场景设置
检测概率 杂波数量 场景1 $p_{{d} }^1 = 0.95,{\text{ } }p_{{d} }^2 = 0.85,{\text{ } }p_{{d} }^3 = 0.75,{\text{ } }p_{{d} }^4 = 0.65$ $ {\lambda _1} = 20,{\text{ }}{\lambda _2} = 20,{\text{ }}{\lambda _3} = 20,{\text{ }}{\lambda _4} = 20 $ 场景2 ${p}_{ {d} }^{1}=0.90，\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{ }{p}_{ {d} }^{2}=0.90,\text{ }{p}_{ {d} }^{3}=0.90,\text{ }{p}_{ {d} }^{4}=0.90\text{}\text{}$ ${\lambda _1} = 20,{\text{ }}{\lambda _2} = 30,{\text{ }}{\lambda _3} = 40,{\text{ }}{\lambda _4} = 50$ 场景3 $p_{{d} }^1 = 0.95,{\text{ } }p_{{d} }^2 = 0.85,{\text{ } }p_{{d} }^3 = 0.75,{\text{ } }p_{{d} }^4 = 0.65$ ${\lambda _1} = 20,{\text{ }}{\lambda _2} = 30,{\text{ }}{\lambda _3} = 40,{\text{ }}{\lambda _4} = 50$
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表 3 场景1仿真结果
算法 OSPA精度 目标数目估计(真实3) 单传感器GM-PHD 22.8412 2.5361 RIU-GM-PHD 15.6506 2.7581 AIU-GM-PHD 10.5369 2.9808 PAIU-GM-PHD 10.3282 3.0557 MD-IC-PHD 12.9869 2.4657
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表 4 场景2仿真结果
算法 OSPA精度 目标数目估计(真实3) 单传感器GM-PHD 22.8412 2.5361 RIU-GM-PHD 20.4843 2.4578 AIU-GM-PHD 13.4627 2.8301 PAIU-GM-PHD 12.2804 2.9288 MD-IC-PHD 8.1814 2.9336
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表 5 场景3仿真数据
算法 OSPA精度 目标数估计(真实3) 单传感器GM-PHD 24.3033 2.4351 RIU-GM-PHD 20.5465 2.4874 AIU-GM-PHD 13.6899 2.8429 PAIU-GM-PHD 12.7136 2.9526 MD-IC-PHD 19.0824 2.4410
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表 6 3 种算法时间复杂度对比
算法 时间复杂度 空间复杂度 单帧时间消耗(s) RIU-GM-PHD $(2L - 1) \cdot O(n)$ $O(d)$ 15.37 AIU-GM-PHD $(2L - 1) \cdot O(n) + L \cdot O(s)$ $O(d) + O(L)$ 19.02 PAIU-GM-PHD $(2L - 1) \cdot O(n) + L \cdot (O(s) + O(j))$ $O(d) + 2 \cdot O(L)$ 20.01
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