论文标题
粒子过滤的最佳测量预算分配
Optimal measurement budget allocation for particle filtering
论文作者
论文摘要
粒子过滤是目标跟踪的强大工具。当限制观察预算时，有必要将测量结果降低到精心选择的有限样本。在有限的时间范围内研究了离散的随机非线性动力学系统。选择粒子过滤的最佳测量时间的问题被形式化为组合优化问题。我们提出了一种基于遗传算法，蒙特卡洛算法和颗粒滤波器的嵌套的近似解决方案。首先，一个例子表明，遗传算法的表现优于随机试验优化。然后，说明了不规则测量与定期时间间隔进行的测量的兴趣，并量化了我们提出的解决方案的效率：在87.5％的情况下获得更好的过滤性能，平均而言，相对改善为27.7％。
Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter. Firstly, an example demonstrates that the genetic algorithm outperforms a random trial optimization. Then, the interest of non-regular measurements versus measurements performed at regular time intervals is illustrated and the efficiency of our proposed solution is quantified: better filtering performances are obtained in 87.5% of the cases and on average, the relative improvement is 27.7%.
