Abstract
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 N=50 and N=100 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.
Highlights
Target tracking is widely used in many fields of smart city
The development of target tracking algorithm can be divided into four categories: the first category is mainly based on particle filter correlation algorithm; the second category is based on sparse representation theory; and the third category is based on correlation filter tracking algorithm
The specific combination process of quantum genetic algorithm and particle filter is as follows: each particle of the particle filter is regarded as the chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle; for each particle state with weight, firstly, the particle is binary coded with qubit and quantum superposition state, and it is selected, crossed, and selected by quantum revolving gate After the set number of iterations, the final particle set is more accurate and diverse
Summary
Target tracking is widely used in many fields of smart city. Tasks such as video surveillance, human-computer interaction, automatic vehicle control, and human behavior analysis make great use of target tracking [1]. The experiment shows that the improved method can improve the accuracy of the target tracking model to a certain extent, but the real-time performance of the algorithm is usually affected by too much calculation when solving the problem. The depth learning model usually needs a large number of training samples with supervision information to train the model, so how to design a suitable structure to improve the calculation speed to meet the real-time requirements of the algorithm is an urgent problem to be solved [27,28,29]. The simulation experiments of nonlinear target tracking model and time-constant model show that the algorithm has high precision and good numerical stability and can complete the accurate tracking of smart city moving targets
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