In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of and for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

中文翻译：

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基于量子遗传和粒子滤波的智能城市移动目标跟踪算法

在智能城市中的移动目标跟踪中，粒子滤波技术具有处理非线性和非高斯问题的优势，但是当标准粒子滤波采用重采样方法解决退化现象时，简单复制粒子会引起局部优化。困难，导致滤波精度不稳定。针对上述问题，提出了一种结合量子遗传算法（QGA）的粒子滤波算法。针对粒子过滤器中的粒子耗竭问题，该算法采用了进化算法相结合的方法。遗传算法将粒子过滤器中的每个粒子视为一条染色体，每个染色体的适应度对应于粒子的重量。对于每个具有重量的粒子状态，粒子首先用量子位和量子叠加态进行二进制编码，然后使用量子旋转门进行选择，交叉，变异和其他操作，经过一定次数的迭代，最终的粒子集具有更好的准确性和更好的多样性。本文的滤波器状态估计和RMSF 和 针对非线性目标跟踪问题，给出了非线性目标跟踪条件下时间常数模型中真实状态与状​​态估计轨迹的比较。可以看出，在非线性状态下，量子遗传和粒子滤波（QGPF）算法可以实现较高的状态估计精度，并且每次的QGPF算法的滤波误差都比较均匀，说明本文算法具有更好的算法稳定性。在时间常数模型下，该算法拟合真实状态，实现了稳定，准确的跟踪。