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摘要:
多目标跟踪中的传感器控制本质上是一个最优非线性控制问题, 其在理论分析和计算上极具挑战性. 本文基于区间不确定性推理, 利用箱粒子多伯努利滤波器提出了一种基于信息测度的传感器控制策略. 首先, 本文利用箱粒子实现多伯努利滤波器, 并通过一组带有权值的箱粒子来表征多目标后验概率密度函数. 其次, 利用箱粒子的高斯分布假设, 将多伯努利密度近似为高斯混合. 随后, 选择柯西施瓦兹(Cauchy-Schwarz, CS) 散度作为评价函数, 并详细推导了两个高斯混合之间的CS散度的求解公式, 以此为基础提出相应的传感器控制策略. 此外, 作为一种对比方案, 利用蒙特卡罗方法, 本文还给出了通过对箱粒子进行混合均匀采样, 进而通过点粒子求解CS散度的递推公式, 并提出了相应的控制策略. 最后, 仿真实验验证了所提算法的有效性.
Abstract:In multi-target tracking, sensor control is essentially an optimal nonlinear control problem. And it is also challenging in theoretical analysis and calculation. On the basis of interval uncertainty reasoning, this paper proposes an information measure based sensor control via box-particle multi-Bernoulli fllter. First, the box-particle multi-Bernoulli fllter is given and the posterior multi-Bernoulli density is approximated by a set of box particles with weights. Then, by constructing a box particle as a Gaussian distribution, the multi-Bernoulli density is approximated by mixed Gaussian components. Subsequently, this paper chooses the Cauchy-Schwarz (CS) divergence as the evaluation function, and deduces the CS divergence in detail between two Gaussian mixed multi-Bernoulli densities. The corresponding sensor control strategy is also proposed. Furthermore, as a compared scheme, this paper also gives a recursive formula for solving CS divergence by sampling particles from a box in the mixed uniform way using Monte Carlo method and presents the corresponding control strategy. Finally, simulation results verify the efiectiveness of the proposed algorithm.
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Key words:
- Multi-target tracking /
- box-particle /
- interval analysis /
- Gaussian mixture /
- sensor control
1) 本文责任编委 曹向辉 -
图 7 所提方案中不同过程噪声强度对估计性能的影响
Fig. 7 Tracking performance of different process noise intensities for the proposed strategy
图 8 所提方案中不同量测噪声系数对估计性能的影响
Fig. 8 Tracking performance of different measure noise factors for the proposed strategy
图 9 所提方案中不同K值对估计性能的影响
Fig. 9 Tracking performance of difierent K values for the proposed strategy
图 10 所提方案中不同的传感器速度对估计性能的影响
Fig. 10 Tracking performance of different sensor speeds for the proposed strategy
表 1 多目标参数
Table 1 Parameters of multi-target
新生时刻(s) 消亡时刻(s) 初始位置(m) 速度(m/s) 目标1 1 50 [-800, -600] [8, 7] 目标2 5 40 [-900, 800] [10, -12] 目标3 10 40 [1 000, -400] [-20, -10] 目标4 15 50 [700, -800] [-7, 16] 
下载: 导出CSV
表 2 四种控制方案势估计误差均值的绝对值
Table 2 Absolute value of cardinality error for four control strategies
方案 势误差Ne 方案一(箱粒子高斯分布近似) 0.21338 方案二(箱粒子混合均匀采样) 0.23839 方案三(随机控制) 0.24979 方案四(PENT) 0.19987
下载: 导出CSV
表 3 四种控制方案单步平均运行时间对比
Table 3 The average execution time for four control strategies
方案 单步平均运行时间(s) 方案一(箱粒子高斯分布近似) 2.54639 方案二(箱粒子混合均匀采样) 3.71813 方案三(随机控制) 1.88743 方案四(PENT) 5.55129
下载: 导出CSV
表 4 不同高斯分量个数的性能比较
Table 4 Tracking performance comparison of different Gaussian components
wm 0.3 0.2 0.1 0.01 rm = 0.5 OSPA(m) 18.04 17.62 17.38 16.88 时间(s) 2.49 2.61 2.87 3.59 rm = 0.3 OSPA(m) 17.57 17.19 16.95 16.48 时间(s) 2.62 2.76 3.01 3.83 rm = 0.1 OSPA(m) 17.27 17.01 16.53 15.98 时间(s) 2.89 3.12 3.64 4.37
下载: 导出CSV
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